update ENAS

configs/nas-benchmark/algos/ENAS.config

@@ -0,0 +1,17 @@
+{
+  "scheduler": ["str",   "cos"],
+  "LR"       : ["float", "0.05"],
+  "eta_min"  : ["float", "0.0005"],
+  "epochs"   : ["int",   "310"],
+  "T_max"    : ["int",   "10"],
+  "warmup"   : ["int",   "0"],
+  "optim"    : ["str",   "SGD"],
+  "decay"    : ["float", "0.00025"],
+  "momentum" : ["float", "0.9"],
+  "nesterov" : ["bool",  "1"],
+  "controller_lr"   : ["float", "0.001"],
+  "controller_betas": ["float", [0, 0.999]],
+  "controller_eps"  : ["float", 0.001],
+  "criterion": ["str",   "Softmax"],
+  "batch_size": ["int",  "128"]
+}

exps/algos/ENAS.py

@@ -0,0 +1,347 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import os, sys, time, glob, random, argparse
+import numpy as np
+from copy import deepcopy
+import torch
+import torch.nn as nn
+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, dict2config, configure2str
+from datasets     import get_datasets, SearchDataset
+from procedures   import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
+from utils        import get_model_infos, obtain_accuracy
+from log_utils    import AverageMeter, time_string, convert_secs2time
+from models       import get_cell_based_tiny_net, get_search_spaces
+
+
+def train_shared_cnn(xloader, shared_cnn, controller, criterion, scheduler, optimizer, epoch_str, print_freq, logger):
+  data_time, batch_time = AverageMeter(), AverageMeter()
+  losses, top1s, top5s, xend = AverageMeter(), AverageMeter(), AverageMeter(), time.time()
+  
+  shared_cnn.train()
+  controller.eval()
+
+  for step, (inputs, targets) in enumerate(xloader):
+    scheduler.update(None, 1.0 * step / len(xloader))
+    targets = targets.cuda(non_blocking=True)
+    # measure data loading time
+    data_time.update(time.time() - xend)
+    
+    with torch.no_grad():
+      _, _, sampled_arch = controller()
+
+    optimizer.zero_grad()
+    shared_cnn.module.update_arch(sampled_arch)
+    _, logits = shared_cnn(inputs)
+    loss      = criterion(logits, targets)
+    loss.backward()
+    torch.nn.utils.clip_grad_norm_(shared_cnn.parameters(), 5)
+    optimizer.step()
+    # record
+    prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
+    losses.update(loss.item(),  inputs.size(0))
+    top1s.update (prec1.item(), inputs.size(0))
+    top5s.update (prec5.item(), inputs.size(0))
+
+    # measure elapsed time
+    batch_time.update(time.time() - xend)
+    xend = time.time()
+
+    if step % print_freq == 0 or step + 1 == len(xloader):
+      Sstr = '*Train-Shared-CNN* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader))
+      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}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=losses, top1=top1s, top5=top5s)
+      logger.log(Sstr + ' ' + Tstr + ' ' + Wstr)
+  return losses.avg, top1s.avg, top5s.avg
+
+
+def train_controller(xloader, shared_cnn, controller, criterion, optimizer, config, epoch_str, print_freq, logger):
+  # 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()
+  
+  shared_cnn.eval()
+  controller.train()
+  controller.zero_grad()
+  #for step, (inputs, targets) in enumerate(xloader):
+  loader_iter = iter(xloader)
+  for step in range(config.ctl_train_steps * config.ctl_num_aggre):
+    try:
+      inputs, targets = next(loader_iter)
+    except:
+      loader_iter = iter(xloader)
+      inputs, targets = next(loader_iter)
+    targets = targets.cuda(non_blocking=True)
+    # measure data loading time
+    data_time.update(time.time() - xend)
+    
+    log_prob, entropy, sampled_arch = controller()
+    with torch.no_grad():
+      shared_cnn.module.update_arch(sampled_arch)
+      _, logits = shared_cnn(inputs)
+      val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
+      val_top1  = val_top1.view(-1) / 100
+    reward = val_top1 + config.ctl_entropy_w * entropy
+    if config.baseline is None:
+      baseline = val_top1
+    else:
+      baseline = config.baseline - (1 - config.ctl_bl_dec) * (config.baseline - reward)
+   
+    loss = -1 * log_prob * (reward - baseline)
+    
+    # account
+    RewardMeter.update(reward.item())
+    BaselineMeter.update(baseline.item())
+    ValAccMeter.update(val_top1.item())
+    LossMeter.update(loss.item())
+  
+    # Average gradient over controller_num_aggregate samples
+    loss = loss / config.ctl_num_aggre
+    loss.backward(retain_graph=True)
+
+    # measure elapsed time
+    batch_time.update(time.time() - xend)
+    xend = time.time()
+    if (step+1) % config.ctl_num_aggre == 0:
+      grad_norm = torch.nn.utils.clip_grad_norm_(controller.parameters(), 5.0)
+      GradnormMeter.update(grad_norm)
+      optimizer.step()
+      controller.zero_grad()
+
+    if step % print_freq == 0:
+      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)
+
+  return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg, baseline.item()
+
+
+def get_best_arch(controller, shared_cnn, xloader, n_samples=10):
+  with torch.no_grad():
+    controller.eval()
+    shared_cnn.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 = controller()
+      arch = shared_cnn.module.update_arch(sampled_arch)
+      _, logits = shared_cnn(inputs)
+      val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
+
+      archs.append( 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.eval()
+  end = time.time()
+  with torch.no_grad():
+    for step, (arch_inputs, arch_targets) in enumerate(xloader):
+      arch_targets = arch_targets.cuda(non_blocking=True)
+      # measure data loading time
+      data_time.update(time.time() - end)
+      # prediction
+      _, logits = network(arch_inputs)
+      arch_loss = criterion(logits, arch_targets)
+      # record
+      arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
+      arch_losses.update(arch_loss.item(),  arch_inputs.size(0))
+      arch_top1.update  (arch_prec1.item(), arch_inputs.size(0))
+      arch_top5.update  (arch_prec5.item(), arch_inputs.size(0))
+      # measure elapsed time
+      batch_time.update(time.time() - end)
+      end = time.time()
+  return arch_losses.avg, arch_top1.avg, arch_top5.avg
+
+
+def main(xargs):
+  assert torch.cuda.is_available(), 'CUDA is not available.'
+  torch.backends.cudnn.enabled   = True
+  torch.backends.cudnn.benchmark = False
+  torch.backends.cudnn.deterministic = True
+  torch.set_num_threads( xargs.workers )
+  prepare_seed(xargs.rand_seed)
+  logger = prepare_logger(args)
+
+  train_data, test_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
+  if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
+    split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
+    cifar_split = load_config(split_Fpath, None, None)
+    train_split, valid_split = cifar_split.train, cifar_split.valid
+    logger.log('Load split file from {:}'.format(split_Fpath))
+  elif xargs.dataset.startswith('ImageNet16'):
+    split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
+    imagenet16_split = load_config(split_Fpath, None, None)
+    train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
+    logger.log('Load split file from {:}'.format(split_Fpath))
+  else:
+    raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
+  logger.log('use config from : {:}'.format(xargs.config_path))
+  config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
+  logger.log('config: {:}'.format(config))
+  # To split data
+  train_data_v2 = deepcopy(train_data)
+  train_data_v2.transform = test_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(train_split), 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)
+  logger.log('||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(train_loader), len(valid_loader), config.batch_size))
+  logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
+
+  search_space = get_search_spaces('cell', xargs.search_space_name)
+  model_config = dict2config({'name': 'ENAS', 'C': xargs.channel, 'N': xargs.num_cells,
+                              'max_nodes': xargs.max_nodes, 'num_classes': class_num,
+                              'space'    : search_space}, None)
+  shared_cnn = get_cell_based_tiny_net(model_config)
+  controller = shared_cnn.create_controller()
+  
+  w_optimizer, w_scheduler, criterion = get_optim_scheduler(shared_cnn.parameters(), config)
+  a_optimizer = torch.optim.Adam(controller.parameters(), lr=config.controller_lr, betas=config.controller_betas, eps=config.controller_eps)
+  logger.log('w-optimizer : {:}'.format(w_optimizer))
+  logger.log('a-optimizer : {:}'.format(a_optimizer))
+  logger.log('w-scheduler : {:}'.format(w_scheduler))
+  logger.log('criterion   : {:}'.format(criterion))
+  #flop, param  = get_model_infos(shared_cnn, xshape)
+  #logger.log('{:}'.format(shared_cnn))
+  #logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
+  shared_cnn, controller, criterion = torch.nn.DataParallel(shared_cnn).cuda(), controller.cuda(), criterion.cuda()
+
+  last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
+
+  if last_info.exists(): # automatically resume from previous checkpoint
+    logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
+    last_info   = torch.load(last_info)
+    start_epoch = last_info['epoch']
+    checkpoint  = torch.load(last_info['last_checkpoint'])
+    genotypes   = checkpoint['genotypes']
+    baseline    = checkpoint['baseline']
+    valid_accuracies = checkpoint['valid_accuracies']
+    shared_cnn.load_state_dict( checkpoint['shared_cnn'] )
+    controller.load_state_dict( checkpoint['controller'] )
+    w_scheduler.load_state_dict ( checkpoint['w_scheduler'] )
+    w_optimizer.load_state_dict ( checkpoint['w_optimizer'] )
+    a_optimizer.load_state_dict ( checkpoint['a_optimizer'] )
+    logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
+  else:
+    logger.log("=> do not find the last-info file : {:}".format(last_info))
+    start_epoch, valid_accuracies, genotypes, baseline = 0, {'best': -1}, {}, None
+
+  # start training
+  start_time, epoch_time, total_epoch = time.time(), AverageMeter(), config.epochs + config.warmup
+  for epoch in range(start_epoch, total_epoch):
+    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())))
+
+    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))
+    ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline \
+                                 = train_controller(valid_loader, shared_cnn, controller, criterion, a_optimizer, \
+                                                        dict2config({'baseline': baseline,
+                                                                     'ctl_train_steps': xargs.controller_train_steps, 'ctl_num_aggre': xargs.controller_num_aggregate,
+                                                                     'ctl_entropy_w': xargs.controller_entropy_weight, 
+                                                                     'ctl_bl_dec'   : xargs.controller_bl_dec}, None), \
+                                                        epoch_str, xargs.print_freq, logger)
+    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)
+
+    genotypes[epoch] = best_arch
+    # check the best accuracy
+    valid_accuracies[epoch] = best_valid_acc
+    if best_valid_acc > valid_accuracies['best']:
+      valid_accuracies['best'] = best_valid_acc
+      genotypes['best']        = best_arch
+      find_best = True
+    else: find_best = False
+
+    logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
+    # save checkpoint
+    save_path = save_checkpoint({'epoch' : epoch + 1,
+                'args'  : deepcopy(xargs),
+                'baseline'    : baseline,
+                'shared_cnn'  : shared_cnn.state_dict(),
+                'controller'  : controller.state_dict(),
+                'w_optimizer' : w_optimizer.state_dict(),
+                'a_optimizer' : a_optimizer.state_dict(),
+                'w_scheduler' : w_scheduler.state_dict(),
+                'genotypes'   : genotypes,
+                'valid_accuracies' : valid_accuracies},
+                model_base_path, logger)
+    last_info = save_checkpoint({
+          'epoch': epoch + 1,
+          'args' : deepcopy(args),
+          'last_checkpoint': save_path,
+          }, logger.path('info'), logger)
+    if find_best:
+      logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, best_valid_acc))
+      copy_checkpoint(model_base_path, model_best_path, logger)
+    # measure elapsed time
+    epoch_time.update(time.time() - start_time)
+    start_time = time.time()
+
+  logger.log('\n' + '-'*100)
+  # 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))
+  #else:
+  #  nas_bench = NASBenchmarkAPI(xargs.arch_nas_dataset)
+  #  geno = genotypes[total_epoch-1]
+  #  logger.log('The last model is {:}'.format(geno))
+  #  info = nas_bench.query_by_arch( geno )
+  #  if info is None: logger.log('Did not find this architecture : {:}.'.format(geno))
+  #  else           : logger.log('{:}'.format(info))
+  #  logger.log('-'*100)
+  #  geno = genotypes['best']
+  #  logger.log('The best model is {:}'.format(geno))
+  #  info = nas_bench.query_by_arch( geno )
+  #  if info is None: logger.log('Did not find this architecture : {:}.'.format(geno))
+  #  else           : logger.log('{:}'.format(info))
+  logger.close()
+  
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser("ENAS")
+  parser.add_argument('--data_path',          type=str,   help='Path to dataset')
+  parser.add_argument('--dataset',            type=str,   choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
+  # channels and number-of-cells
+  parser.add_argument('--search_space_name',  type=str,   help='The search space name.')
+  parser.add_argument('--max_nodes',          type=int,   help='The maximum number of nodes.')
+  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('--config_path',        type=str,   help='The config file to train ENAS.')
+  parser.add_argument('--controller_train_steps',    type=int,     help='.')
+  parser.add_argument('--controller_num_aggregate',  type=int,     help='.')
+  parser.add_argument('--controller_entropy_weight', type=float,   help='The weight for the entropy of the controller.')
+  parser.add_argument('--controller_bl_dec'        , type=float,   help='.')
+  parser.add_argument('--controller_num_samples'   , type=int,     help='.')
+  # log
+  parser.add_argument('--workers',            type=int,   default=2,    help='number of data loading workers (default: 2)')
+  parser.add_argument('--save_dir',           type=str,   help='Folder to save checkpoints and log.')
+  parser.add_argument('--arch_nas_dataset',   type=str,   help='The path to load the architecture dataset (nas-benchmark).')
+  parser.add_argument('--print_freq',         type=int,   help='print frequency (default: 200)')
+  parser.add_argument('--rand_seed',          type=int,   help='manual seed')
+  args = parser.parse_args()
+  if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
+  main(args)

lib/models/__init__.py

@@ -16,18 +16,10 @@ from .cell_searchs import CellStructure, CellArchitectures
 
 # Cell-based NAS Models
 def get_cell_based_tiny_net(config):
-  if config.name == 'DARTS-V1':
-    from .cell_searchs import TinyNetworkDartsV1
-    return TinyNetworkDartsV1(config.C, config.N, config.max_nodes, config.num_classes, config.space)
-  elif config.name == 'DARTS-V2':
-    from .cell_searchs import TinyNetworkDartsV2
-    return TinyNetworkDartsV2(config.C, config.N, config.max_nodes, config.num_classes, config.space)
-  elif config.name == 'GDAS':
-    from .cell_searchs import TinyNetworkGDAS
-    return TinyNetworkGDAS(config.C, config.N, config.max_nodes, config.num_classes, config.space)
-  elif config.name == 'SETN':
-    from .cell_searchs import TinyNetworkSETN
-    return TinyNetworkSETN(config.C, config.N, config.max_nodes, config.num_classes, config.space)
+  group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS']
+  from .cell_searchs import nas_super_nets
+  if config.name in group_names:
+    return nas_super_nets[config.name](config.C, config.N, config.max_nodes, config.num_classes, config.space)
   elif config.name == 'infer.tiny':
     from .cell_infers import TinyNetwork
     return TinyNetwork(config.C, config.N, config.genotype, config.num_classes)

lib/models/cell_searchs/__init__.py

@@ -2,4 +2,11 @@ from .search_model_darts_v1 import TinyNetworkDartsV1
 from .search_model_darts_v2 import TinyNetworkDartsV2
 from .search_model_gdas     import TinyNetworkGDAS
 from .search_model_setn     import TinyNetworkSETN
+from .search_model_enas     import TinyNetworkENAS
 from .genotypes             import Structure as CellStructure, architectures as CellArchitectures
+
+nas_super_nets = {'DARTS-V1': TinyNetworkDartsV1,
+                  'DARTS-V2': TinyNetworkDartsV2,
+                  'GDAS'    : TinyNetworkGDAS,
+                  'SETN'    : TinyNetworkSETN,
+                  'ENAS'    : TinyNetworkENAS}

lib/models/cell_searchs/_test_module.py

@@ -0,0 +1,9 @@
+import torch
+from search_model_enas_utils import Controller
+
+def main():
+  controller = Controller(6, 4)
+  predictions = controller()
+
+if __name__ == '__main__':
+  main()

lib/models/cell_searchs/search_model_enas.py

@@ -0,0 +1,94 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##########################################################################
+# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
+##########################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from ..cell_operations import ResNetBasicblock
+from .search_cells     import SearchCell
+from .genotypes        import Structure
+from .search_model_enas_utils import Controller
+
+
+class TinyNetworkENAS(nn.Module):
+
+  def __init__(self, C, N, max_nodes, num_classes, search_space):
+    super(TinyNetworkENAS, self).__init__()
+    self._C        = C
+    self._layerN   = N
+    self.max_nodes = max_nodes
+    self.stem = nn.Sequential(
+                    nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(C))
+  
+    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * N + [C*4 ] + [C*4  ] * N    
+    layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+    C_prev, num_edge, edge2index = C, None, None
+    self.cells = nn.ModuleList()
+    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
+      if reduction:
+        cell = ResNetBasicblock(C_prev, C_curr, 2)
+      else:
+        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
+        if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
+        else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
+      self.cells.append( cell )
+      C_prev = cell.out_dim
+    self.op_names   = deepcopy( search_space )
+    self._Layer     = len(self.cells)
+    self.edge2index = edge2index
+    self.lastact    = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+    self.global_pooling = nn.AdaptiveAvgPool2d(1)
+    self.classifier = nn.Linear(C_prev, num_classes)
+    # to maintain the sampled architecture
+    self.sampled_arch = None
+
+  def update_arch(self, _arch):
+    if _arch is None:
+      self.sampled_arch = None
+    elif isinstance(_arch, Structure):
+      self.sampled_arch = _arch
+    elif isinstance(_arch, (list, tuple)):
+      genotypes = []
+      for i in range(1, self.max_nodes):
+        xlist = []
+        for j in range(i):
+          node_str = '{:}<-{:}'.format(i, j)
+          op_index = _arch[ self.edge2index[node_str] ]
+          op_name  = self.op_names[ op_index ]
+          xlist.append((op_name, j))
+        genotypes.append( tuple(xlist) )
+      self.sampled_arch = Structure(genotypes)
+    else:
+      raise ValueError('invalid type of input architecture : {:}'.format(_arch))
+    return self.sampled_arch
+    
+  def create_controller(self):
+    return Controller(len(self.edge2index), len(self.op_names))
+
+  def get_message(self):
+    string = self.extra_repr()
+    for i, cell in enumerate(self.cells):
+      string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
+    return string
+
+  def extra_repr(self):
+    return ('{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  def forward(self, inputs):
+
+    feature = self.stem(inputs)
+    for i, cell in enumerate(self.cells):
+      if isinstance(cell, SearchCell):
+        feature = cell.forward_dynamic(feature, self.sampled_arch)
+      else: feature = cell(feature)
+
+    out = self.lastact(feature)
+    out = self.global_pooling( out )
+    out = out.view(out.size(0), -1)
+    logits = self.classifier(out)
+
+    return out, logits

lib/models/cell_searchs/search_model_enas_utils.py

@@ -0,0 +1,55 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##########################################################################
+# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
+##########################################################################
+import torch
+import torch.nn as nn
+from torch.distributions.categorical import Categorical
+
+class Controller(nn.Module):
+  # we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py
+  def __init__(self, num_edge, num_ops, lstm_size=32, lstm_num_layers=2, tanh_constant=2.5, temperature=5.0):
+    super(Controller, self).__init__()
+    # assign the attributes
+    self.num_edge  = num_edge
+    self.num_ops   = num_ops
+    self.lstm_size = lstm_size
+    self.lstm_N    = lstm_num_layers
+    self.tanh_constant = tanh_constant
+    self.temperature   = temperature
+    # create parameters
+    self.register_parameter('input_vars', nn.Parameter(torch.Tensor(1, 1, lstm_size)))
+    self.w_lstm = nn.LSTM(input_size=self.lstm_size, hidden_size=self.lstm_size, num_layers=self.lstm_N)
+    self.w_embd = nn.Embedding(self.num_ops, self.lstm_size)
+    self.w_pred = nn.Linear(self.lstm_size, self.num_ops)
+
+    nn.init.uniform_(self.input_vars         , -0.1, 0.1)
+    nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1)
+    nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1)
+    nn.init.uniform_(self.w_embd.weight      , -0.1, 0.1)
+    nn.init.uniform_(self.w_pred.weight      , -0.1, 0.1)
+
+  def forward(self):
+
+    inputs, h0 = self.input_vars, None
+    log_probs, entropys, sampled_arch = [], [], []
+    for iedge in range(self.num_edge):
+      outputs, h0 = self.w_lstm(inputs, h0)
+      
+      logits = self.w_pred(outputs)
+      logits = logits / self.temperature
+      logits = self.tanh_constant * torch.tanh(logits)
+      # distribution
+      op_distribution = Categorical(logits=logits)
+      op_index    = op_distribution.sample()
+      sampled_arch.append( op_index.item() )
+
+      op_log_prob = op_distribution.log_prob(op_index)
+      log_probs.append( op_log_prob.view(-1) )
+      op_entropy  = op_distribution.entropy()
+      entropys.append( op_entropy.view(-1) )
+      
+      # obtain the input embedding for the next step
+      inputs = self.w_embd(op_index)
+    return torch.sum(torch.cat(log_probs)), torch.sum(torch.cat(entropys)), sampled_arch