update GDAS and SETN

exps/algos/DARTS-V1.py

@@ -0,0 +1,252 @@
+##################################################
+# 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 search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
+  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))
+    base_targets = base_targets.cuda(non_blocking=True)
+    arch_targets = arch_targets.cuda(non_blocking=True)
+    # measure data loading time
+    data_time.update(time.time() - end)
+    
+    # update the weights
+    w_optimizer.zero_grad()
+    _, logits = network(base_inputs)
+    base_loss = criterion(logits, base_targets)
+    base_loss.backward()
+    torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
+    w_optimizer.step()
+    # record
+    base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
+    base_losses.update(base_loss.item(),  base_inputs.size(0))
+    base_top1.update  (base_prec1.item(), base_inputs.size(0))
+    base_top5.update  (base_prec5.item(), base_inputs.size(0))
+
+    # update the architecture-weight
+    a_optimizer.zero_grad()
+    _, logits = network(arch_inputs)
+    arch_loss = criterion(logits, arch_targets)
+    arch_loss.backward()
+    a_optimizer.step()
+    # 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()
+
+    if step % print_freq == 0 or step + 1 == len(xloader):
+      Sstr = '*SEARCH* ' + 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 = '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
+
+
+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, valid_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))
+  config_path = 'configs/nas-benchmark/algos/DARTS.config'
+  config = load_config(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
+  valid_data    = train_data_v2
+  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)
+  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))
+
+  search_space = get_search_spaces('cell', xargs.search_space_name)
+  model_config = dict2config({'name': 'DARTS-V1', 'C': xargs.channel, 'N': xargs.num_cells,
+                              'max_nodes': xargs.max_nodes, 'num_classes': class_num,
+                              'space'    : search_space}, None)
+  search_model = get_cell_based_tiny_net(model_config)
+  
+  w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)
+  a_optimizer = torch.optim.Adam(search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)
+  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(search_model, xshape)
+  #logger.log('{:}'.format(search_model))
+  logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
+
+  last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
+  network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
+
+  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']
+    valid_accuracies = checkpoint['valid_accuracies']
+    search_model.load_state_dict( checkpoint['search_model'] )
+    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 = 0, {'best': -1}, {}
+
+  # 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())))
+
+    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)
+    logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5))
+    valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
+    logger.log('[{:}] 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']:
+      valid_accuracies['best'] = valid_a_top1
+      genotypes['best']        = search_model.genotype()
+      find_best = True
+    else: find_best = False
+
+    genotypes[epoch] = search_model.genotype()
+    logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
+    # save checkpoint
+    save_path = save_checkpoint({'epoch' : epoch + 1,
+                'args'  : deepcopy(xargs),
+                'search_model': search_model.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, valid_a_top1))
+      copy_checkpoint(model_base_path, model_best_path, logger)
+    with torch.no_grad():
+      logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
+    # 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("DARTS first order")
+  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.')
+  # 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')
+  # 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)

exps/algos/DARTS-V2.py

@@ -0,0 +1,319 @@
+##################################################
+# 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 _concat(xs):
+  return torch.cat([x.view(-1) for x in xs])
+
+
+def _hessian_vector_product(vector, network, criterion, base_inputs, base_targets, r=1e-2):
+  R = r / _concat(vector).norm()
+  for p, v in zip(network.module.get_weights(), vector):
+    p.data.add_(R, v)
+  _, logits = network(base_inputs)
+  loss = criterion(logits, base_targets)
+  grads_p = torch.autograd.grad(loss, network.module.get_alphas())
+
+  for p, v in zip(network.module.get_weights(), vector):
+    p.data.sub_(2*R, v)
+  _, logits = network(base_inputs)
+  loss = criterion(logits, base_targets)
+  grads_n = torch.autograd.grad(loss, network.module.get_alphas())
+
+  for p, v in zip(network.module.get_weights(), vector):
+    p.data.add_(R, v)
+  return [(x-y).div_(2*R) for x, y in zip(grads_p, grads_n)]
+
+
+def backward_step_unrolled(network, criterion, base_inputs, base_targets, w_optimizer, arch_inputs, arch_targets):
+  # _compute_unrolled_model
+  _, logits = network(base_inputs)
+  loss = criterion(logits, base_targets)
+  LR, WD, momentum = w_optimizer.param_groups[0]['lr'], w_optimizer.param_groups[0]['weight_decay'], w_optimizer.param_groups[0]['momentum']
+  with torch.no_grad():
+    theta = _concat(network.module.get_weights())
+    try:
+      moment = _concat(w_optimizer.state[v]['momentum_buffer'] for v in network.module.get_weights())
+      moment = moment.mul_(momentum)
+    except:
+      moment = torch.zeros_like(theta)
+    dtheta = _concat(torch.autograd.grad(loss, network.module.get_weights())) + WD*theta
+    params = theta.sub(LR, moment+dtheta)
+  unrolled_model = deepcopy(network)
+  model_dict  = unrolled_model.state_dict()
+  new_params, offset = {}, 0
+  for k, v in network.named_parameters():
+    if 'arch_parameters' in k: continue
+    v_length = np.prod(v.size())
+    new_params[k] = params[offset: offset+v_length].view(v.size())
+    offset += v_length
+  model_dict.update(new_params)
+  unrolled_model.load_state_dict(model_dict)
+
+  unrolled_model.zero_grad()
+  _, unrolled_logits = unrolled_model(arch_inputs)
+  unrolled_loss = criterion(unrolled_logits, arch_targets)
+  unrolled_loss.backward()
+
+  dalpha = unrolled_model.module.arch_parameters.grad
+  vector = [v.grad.data for v in unrolled_model.module.get_weights()]
+  [implicit_grads] = _hessian_vector_product(vector, network, criterion, base_inputs, base_targets)
+  
+  dalpha.data.sub_(LR, implicit_grads.data)
+
+  if network.module.arch_parameters.grad is None:
+    network.module.arch_parameters.grad = deepcopy( dalpha )
+  else:
+    network.module.arch_parameters.grad.data.copy_( dalpha.data )
+  return unrolled_loss.detach(), unrolled_logits.detach()
+  
+
+def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
+  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))
+    base_targets = base_targets.cuda(non_blocking=True)
+    arch_targets = arch_targets.cuda(non_blocking=True)
+    # measure data loading time
+    data_time.update(time.time() - end)
+
+    # update the architecture-weight
+    a_optimizer.zero_grad()
+    arch_loss, arch_logits = backward_step_unrolled(network, criterion, base_inputs, base_targets, w_optimizer, arch_inputs, arch_targets)
+    a_optimizer.step()
+    # record
+    arch_prec1, arch_prec5 = obtain_accuracy(arch_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))
+    
+    # update the weights
+    w_optimizer.zero_grad()
+    _, logits = network(base_inputs)
+    base_loss = criterion(logits, base_targets)
+    base_loss.backward()
+    torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
+    w_optimizer.step()
+    # record
+    base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
+    base_losses.update(base_loss.item(),  base_inputs.size(0))
+    base_top1.update  (base_prec1.item(), base_inputs.size(0))
+    base_top5.update  (base_prec5.item(), base_inputs.size(0))
+
+    # measure elapsed time
+    batch_time.update(time.time() - end)
+    end = time.time()
+
+    if step % print_freq == 0 or step + 1 == len(xloader):
+      Sstr = '*SEARCH* ' + 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 = '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
+
+
+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, valid_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))
+  config_path = 'configs/nas-benchmark/algos/DARTS.config'
+  config = load_config(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
+  valid_data    = train_data_v2
+  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)
+  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))
+
+  search_space = get_search_spaces('cell', xargs.search_space_name)
+  model_config = dict2config({'name': 'DARTS-V2', 'C': xargs.channel, 'N': xargs.num_cells,
+                              'max_nodes': xargs.max_nodes, 'num_classes': class_num,
+                              'space'    : search_space}, None)
+  search_model = get_cell_based_tiny_net(model_config)
+  
+  w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)
+  a_optimizer = torch.optim.Adam(search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)
+  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(search_model, xshape)
+  #logger.log('{:}'.format(search_model))
+  logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
+
+  last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
+  network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
+
+  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']
+    valid_accuracies = checkpoint['valid_accuracies']
+    search_model.load_state_dict( checkpoint['search_model'] )
+    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 = 0, {'best': -1}, {}
+
+  # 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)
+    min_LR    = min(w_scheduler.get_lr())
+    logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min_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)
+    logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5))
+    valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
+    logger.log('[{:}] 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']:
+      valid_accuracies['best'] = valid_a_top1
+      genotypes['best']        = search_model.genotype()
+      find_best = True
+    else: find_best = False
+
+    genotypes[epoch] = search_model.genotype()
+    logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
+    # save checkpoint
+    save_path = save_checkpoint({'epoch' : epoch + 1,
+                'args'  : deepcopy(xargs),
+                'search_model': search_model.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, valid_a_top1))
+      copy_checkpoint(model_base_path, model_best_path, logger)
+    with torch.no_grad():
+      logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
+    # 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("DARTS Second Order")
+  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.')
+  # 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')
+  # 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 (tiny-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)

exps/algos/GDAS.py

@@ -0,0 +1,224 @@
+##################################################
+# 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 search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
+  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))
+    base_targets = base_targets.cuda(non_blocking=True)
+    arch_targets = arch_targets.cuda(non_blocking=True)
+    # measure data loading time
+    data_time.update(time.time() - end)
+    
+    # update the weights
+    w_optimizer.zero_grad()
+    _, logits = network(base_inputs)
+    base_loss = criterion(logits, base_targets)
+    base_loss.backward()
+    torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
+    w_optimizer.step()
+    # record
+    base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
+    base_losses.update(base_loss.item(),  base_inputs.size(0))
+    base_top1.update  (base_prec1.item(), base_inputs.size(0))
+    base_top5.update  (base_prec5.item(), base_inputs.size(0))
+
+    # update the architecture-weight
+    a_optimizer.zero_grad()
+    _, logits = network(arch_inputs)
+    arch_loss = criterion(logits, arch_targets)
+    arch_loss.backward()
+    a_optimizer.step()
+    # 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()
+
+    if step % print_freq == 0 or step + 1 == len(xloader):
+      Sstr = '*SEARCH* ' + 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 = '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, 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, _, 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))
+  config_path = 'configs/nas-benchmark/algos/GDAS.config'
+  config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger)
+  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)
+  logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_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': 'GDAS', 'C': xargs.channel, 'N': xargs.num_cells,
+                              'max_nodes': xargs.max_nodes, 'num_classes': class_num,
+                              'space'    : search_space}, None)
+  search_model = get_cell_based_tiny_net(model_config)
+  
+  w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)
+  a_optimizer = torch.optim.Adam(search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)
+  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(search_model, xshape)
+  #logger.log('{:}'.format(search_model))
+  logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
+  logger.log('search_space : {:}'.format(search_space))
+
+  last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
+  network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
+
+  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']
+    valid_accuracies = checkpoint['valid_accuracies']
+    search_model.load_state_dict( checkpoint['search_model'] )
+    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 = 0, {'best': -1}, {}
+
+  # 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)
+    search_model.set_tau( xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1) )
+    logger.log('\n[Search the {:}-th epoch] {:}, tau={:}, LR={:}'.format(epoch_str, need_time, search_model.get_tau(), min(w_scheduler.get_lr())))
+
+    search_w_loss, search_w_top1, search_w_top5, valid_a_loss , valid_a_top1 , valid_a_top5 \
+              = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
+    logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5))
+    logger.log('[{:}] 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']:
+      valid_accuracies['best'] = valid_a_top1
+      genotypes['best']        = search_model.genotype()
+      find_best = True
+    else: find_best = False
+
+    genotypes[epoch] = search_model.genotype()
+    logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
+    # save checkpoint
+    save_path = save_checkpoint({'epoch' : epoch + 1,
+                'args'  : deepcopy(xargs),
+                'search_model': search_model.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, valid_a_top1))
+      copy_checkpoint(model_base_path, model_best_path, logger)
+    with torch.no_grad():
+      logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
+    # 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)
+  """
+  logger.close()
+  
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser("GDAS")
+  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.')
+  # 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')
+  parser.add_argument('--tau_min',            type=float,               help='The minimum tau for Gumbel')
+  parser.add_argument('--tau_max',            type=float,               help='The maximum tau for Gumbel')
+  # 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 (tiny-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)

exps/algos/SETN.py

@@ -0,0 +1,281 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 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 search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
+  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))
+    base_targets = base_targets.cuda(non_blocking=True)
+    arch_targets = arch_targets.cuda(non_blocking=True)
+    # measure data loading time
+    data_time.update(time.time() - end)
+    
+    # update the weights
+    network.module.set_cal_mode( 'urs' )
+    w_optimizer.zero_grad()
+    _, logits = network(base_inputs)
+    base_loss = criterion(logits, base_targets)
+    base_loss.backward()
+    w_optimizer.step()
+    # record
+    base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
+    base_losses.update(base_loss.item(),  base_inputs.size(0))
+    base_top1.update  (base_prec1.item(), base_inputs.size(0))
+    base_top5.update  (base_prec5.item(), base_inputs.size(0))
+
+    # update the architecture-weight
+    network.module.set_cal_mode( 'joint' )
+    a_optimizer.zero_grad()
+    _, logits = network(arch_inputs)
+    arch_loss = criterion(logits, arch_targets)
+    arch_loss.backward()
+    a_optimizer.step()
+    # 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()
+
+    if step % print_freq == 0 or step + 1 == len(xloader):
+      Sstr = '*SEARCH* ' + 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 = '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
+
+
+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():
+    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, valid_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))
+  config_path = 'configs/nas-benchmark/algos/SETN.config'
+  config = load_config(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
+  valid_data    = train_data_v2
+  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)
+  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))
+
+  search_space = get_search_spaces('cell', xargs.search_space_name)
+  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)
+  search_model = get_cell_based_tiny_net(model_config)
+  
+  w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)
+  a_optimizer = torch.optim.Adam(search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)
+  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(search_model, xshape)
+  #logger.log('{:}'.format(search_model))
+  logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
+
+  last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
+  network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
+
+  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']
+    valid_accuracies = checkpoint['valid_accuracies']
+    search_model.load_state_dict( checkpoint['search_model'] )
+    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 = 0, {'best': -1}, {}
+
+  # 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())))
+
+    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)
+    logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5))
+    search_model.set_cal_mode('urs')
+    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))
+    search_model.set_cal_mode('joint')
+    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))
+    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']:
+      valid_accuracies['best'] = valid_a_top1
+      genotypes['best']        = search_model.genotype()
+      find_best = True
+    else: find_best = False
+
+    genotypes[epoch] = search_model.genotype()
+    logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
+    # save checkpoint
+    save_path = save_checkpoint({'epoch' : epoch + 1,
+                'args'  : deepcopy(xargs),
+                'search_model': search_model.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, valid_a_top1))
+      copy_checkpoint(model_base_path, model_best_path, logger)
+    with torch.no_grad():
+      logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
+    # measure elapsed time
+    epoch_time.update(time.time() - start_time)
+    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()
+  while len(selected_archs) < xargs.select_num:
+    architecture = search_model.dync_genotype()
+    selected_archs.add( architecture )
+  logger.log('select {:} architectures based on the learned arch-parameters'.format( len(selected_archs) ))
+
+  best_arch, best_acc = None, -1
+  state_dict = deepcopy( network.state_dict() )
+  for index, arch in enumerate(selected_archs):
+    with torch.no_grad():
+      search_model.set_cal_mode('dynamic', arch)
+      network.load_state_dict( deepcopy(state_dict) )
+      valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
+    logger.log('{:} [{:03d}/{:03d}] : {:125s}, loss={:.3f}, accuracy={:.3f}%'.format(time_string(), index, len(selected_archs), str(arch), valid_a_loss , valid_a_top1))
+    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
+  """
+  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 = TinyNASBenchmarkAPI(xargs.arch_nas_dataset)
+    geno      = best_arch
+    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)
+  """
+  logger.close()
+  
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser("SETN")
+  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('--select_num',         type=int,   help='The number of selected architectures to evaluate.')
+  # 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')
+  # 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 (tiny-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)