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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
######################################################################################
# python ./exps/algos-v2/search-cell.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo darts-v1 --rand_seed 777
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v1 --drop_path_rate 0.3
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v1
####
# python ./exps/algos-v2/search-cell.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo darts-v2 --rand_seed 777
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v2
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v2
####
# python ./exps/algos-v2/search-cell.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo gdas --rand_seed 777
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo gdas
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo gdas
####
# python ./exps/algos-v2/search-cell.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo setn --rand_seed 777
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo setn
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo setn
####
# python ./exps/algos-v2/search-cell.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo random --rand_seed 777
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo random
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo random
####
# python ./exps/algos-v2/search-cell.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo enas
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo enas
######################################################################################
import os, sys, time, 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, get_nas_search_loaders
from procedures   import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
from utils        import count_parameters_in_MB, obtain_accuracy
from log_utils    import AverageMeter, time_string, convert_secs2time
from models       import get_cell_based_tiny_net, get_search_spaces
from nas_201_api  import NASBench201API as API


# The following three functions are used for DARTS-V2
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.weights, vector):
    p.data.add_(R, v)
  _, logits = network(base_inputs)
  loss = criterion(logits, base_targets)
  grads_p = torch.autograd.grad(loss, network.alphas)

  for p, v in zip(network.weights, vector):
    p.data.sub_(2*R, v)
  _, logits = network(base_inputs)
  loss = criterion(logits, base_targets)
  grads_n = torch.autograd.grad(loss, network.alphas)

  for p, v in zip(network.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.weights)
    try:
      moment = _concat(w_optimizer.state[v]['momentum_buffer'] for v in network.weights)
      moment = moment.mul_(momentum)
    except:
      moment = torch.zeros_like(theta)
    dtheta = _concat(torch.autograd.grad(loss, network.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.arch_parameters.grad
  vector = [v.grad.data for v in unrolled_model.weights]
  [implicit_grads] = _hessian_vector_product(vector, network, criterion, base_inputs, base_targets)
  
  dalpha.data.sub_(LR, implicit_grads.data)

  if network.arch_parameters.grad is None:
    network.arch_parameters.grad = deepcopy( dalpha )
  else:
    network.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, algo, 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()
  end = time.time()
  network.train()
  for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
    scheduler.update(None, 1.0 * step / len(xloader))
    base_inputs = base_inputs.cuda(non_blocking=True)
    arch_inputs = arch_inputs.cuda(non_blocking=True)
    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
    if algo == 'setn':
      sampled_arch = network.dync_genotype(True)
      network.set_cal_mode('dynamic', sampled_arch)
    elif algo == 'gdas':
      network.set_cal_mode('gdas', None)
    elif algo.startswith('darts'):
      network.set_cal_mode('joint', None)
    elif algo == 'random':
      network.set_cal_mode('urs', None)
    elif algo == 'enas':
      with torch.no_grad():
        network.controller.eval()
        _, _, sampled_arch = network.controller()
      network.set_cal_mode('dynamic', sampled_arch)
    else:
      raise ValueError('Invalid algo name : {:}'.format(algo))
      
    network.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
    if algo == 'setn':
      network.set_cal_mode('joint')
    elif algo == 'gdas':
      network.set_cal_mode('gdas', None)
    elif algo.startswith('darts'):
      network.set_cal_mode('joint', None)
    elif algo == 'random':
      network.set_cal_mode('urs', None)
    elif algo != 'enas':
      raise ValueError('Invalid algo name : {:}'.format(algo))
    network.zero_grad()
    if algo == 'darts-v2':
      arch_loss, logits = backward_step_unrolled(network, criterion, base_inputs, base_targets, w_optimizer, arch_inputs, arch_targets)
      a_optimizer.step()
    elif algo == 'random' or algo == 'enas':
      with torch.no_grad():
        _, logits = network(arch_inputs)
        arch_loss = criterion(logits, arch_targets)
    else:
      _, 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 train_controller(xloader, network, criterion, optimizer, prev_baseline, 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, EntropyMeter, BaselineMeter, RewardMeter, xend = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time()
  
  controller_num_aggregate = 20
  controller_train_steps = 50
  controller_bl_dec = 0.99
  controller_entropy_weight = 0.0001

  network.eval()
  network.controller.train()
  network.controller.zero_grad()
  loader_iter = iter(xloader)
  for step in range(controller_train_steps * controller_num_aggregate):
    try:
      inputs, targets = next(loader_iter)
    except:
      loader_iter = iter(xloader)
      inputs, targets = next(loader_iter)
    inputs  = inputs.cuda(non_blocking=True)
    targets = targets.cuda(non_blocking=True)
    # measure data loading time
    data_time.update(time.time() - xend)
    
    log_prob, entropy, sampled_arch = network.controller()
    with torch.no_grad():
      network.set_cal_mode('dynamic', sampled_arch)
      _, logits = network(inputs)
      val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
      val_top1  = val_top1.view(-1) / 100
    reward = val_top1 + controller_entropy_weight * entropy
    if prev_baseline is None:
      baseline = val_top1
    else:
      baseline = prev_baseline - (1 - controller_bl_dec) * (prev_baseline - reward)
   
    loss = -1 * log_prob * (reward - baseline)
    
    # account
    RewardMeter.update(reward.item())
    BaselineMeter.update(baseline.item())
    ValAccMeter.update(val_top1.item()*100)
    LossMeter.update(loss.item())
    EntropyMeter.update(entropy.item())
  
    # Average gradient over controller_num_aggregate samples
    loss = loss / controller_num_aggregate
    loss.backward(retain_graph=True)

    # measure elapsed time
    batch_time.update(time.time() - xend)
    xend = time.time()
    if (step+1) % controller_num_aggregate == 0:
      grad_norm = torch.nn.utils.clip_grad_norm_(network.controller.parameters(), 5.0)
      GradnormMeter.update(grad_norm)
      optimizer.step()
      network.controller.zero_grad()

    if step % print_freq == 0:
      Sstr = '*Train-Controller* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, controller_train_steps * controller_num_aggregate)
      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)
      Estr = 'Entropy={:.4f} ({:.4f})'.format(EntropyMeter.val, EntropyMeter.avg)
      logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Estr)

  return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg


def get_best_arch(xloader, network, n_samples, algo):
  with torch.no_grad():
    network.eval()
    if algo == 'random':
      archs, valid_accs = network.return_topK(n_samples, True), []
    elif algo == 'setn':
      archs, valid_accs = network.return_topK(n_samples, False), []
    elif algo.startswith('darts') or algo == 'gdas':
      arch = network.genotype
      archs, valid_accs = [arch], []
    elif algo == 'enas':
      archs, valid_accs = [], []
      for _ in range(n_samples):
        _, _, sampled_arch = network.controller()
        archs.append(sampled_arch)
    else:
      raise ValueError('Invalid algorithm name : {:}'.format(algo))
    loader_iter = iter(xloader)
    for i, sampled_arch in enumerate(archs):
      network.set_cal_mode('dynamic', sampled_arch)
      try:
        inputs, targets = next(loader_iter)
      except:
        loader_iter = iter(xloader)
        inputs, targets = next(loader_iter)
      _, logits = network(inputs.cuda(non_blocking=True))
      val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
      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, algo, logger):
  data_time, batch_time = AverageMeter(), AverageMeter()
  arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
  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
      data_time.update(time.time() - end)
      # prediction
      _, logits = network(arch_inputs.cuda(non_blocking=True))
      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)
  config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
  search_loader, train_loader, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \
                                        (config.batch_size, config.test_batch_size), xargs.workers)
  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(xargs.search_space, 'nas-bench-301')

  
  model_config = dict2config(
      dict(name='generic', C=xargs.channel, N=xargs.num_cells, max_nodes=xargs.max_nodes, num_classes=class_num,
           space=search_space, affine=bool(xargs.affine), track_running_stats=bool(xargs.track_running_stats)), None)
  logger.log('search space : {:}'.format(search_space))
  logger.log('model config : {:}'.format(model_config))
  search_model = get_cell_based_tiny_net(model_config)
  search_model.set_algo(xargs.algo)
  logger.log('{:}'.format(search_model))

  w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.weights, config)
  a_optimizer = torch.optim.Adam(search_model.alphas, lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay, eps=xargs.arch_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))
  params = count_parameters_in_MB(search_model)
  logger.log('The parameters of the search model = {:.2f} MB'.format(params))
  logger.log('search-space : {:}'.format(search_space))
  try:
    api = API(verbose=False)
  except:
    api = None
  logger.log('{:} create API = {:} done'.format(time_string(), api))

  last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
  network, criterion = search_model.cuda(), criterion.cuda()  # use a single GPU

  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']
    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}, {-1: network.return_topK(1, True)[0]}
    baseline = None

  # start training
  start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), 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())))

    network.set_drop_path(float(epoch+1) / total_epoch, xargs.drop_path_rate)
    if xargs.algo == 'gdas':
      network.set_tau( xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1) )
      logger.log('[RESET tau as : {:} and drop_path as {:}]'.format(network.tau, network.drop_path))
    search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
                = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, xargs.algo, logger)
    search_time.update(time.time() - start_time)
    logger.log('[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum))
    logger.log('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5))
    if xargs.algo == 'enas':
      ctl_loss, ctl_acc, baseline, ctl_reward \
                                 = train_controller(valid_loader, network, criterion, a_optimizer, baseline, epoch_str, xargs.print_freq, logger)
      logger.log('[{:}] controller : loss={:}, acc={:}, baseline={:}, reward={:}'.format(epoch_str, ctl_loss, ctl_acc, baseline, ctl_reward))

    genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.eval_candidate_num, xargs.algo)
    if xargs.algo == 'setn' or xargs.algo == 'enas':
      network.set_cal_mode('dynamic', genotype)
    elif xargs.algo == 'gdas':
      network.set_cal_mode('gdas', None)
    elif xargs.algo.startswith('darts'):
      network.set_cal_mode('joint', None)
    elif xargs.algo == 'random':
      network.set_cal_mode('urs', None)
    else:
      raise ValueError('Invalid algorithm name : {:}'.format(xargs.algo))
    logger.log('[{:}] - [get_best_arch] : {:} -> {:}'.format(epoch_str, genotype, temp_accuracy))
    valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion, xargs.algo, logger)
    logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype))
    valid_accuracies[epoch] = valid_a_top1

    genotypes[epoch] = genotype
    logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
    # save checkpoint
    save_path = save_checkpoint({'epoch' : epoch + 1,
                'args'  : deepcopy(xargs),
                'baseline'    : baseline,
                '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)
    with torch.no_grad():
      logger.log('{:}'.format(search_model.show_alphas()))
    if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[epoch], '200')))
    # measure elapsed time
    epoch_time.update(time.time() - start_time)
    start_time = time.time()

  # the final post procedure : count the time
  start_time = time.time()
  genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.eval_candidate_num, xargs.algo)
  if xargs.algo == 'setn':
    network.set_cal_mode('dynamic', genotype)
  elif xargs.algo == 'gdas':
    network.set_cal_mode('gdas', None)
  elif xargs.algo.startswith('darts'):
    network.set_cal_mode('joint', None)
  elif xargs.algo == 'random':
    network.set_cal_mode('urs', None)
  else:
    raise ValueError('Invalid algorithm name : {:}'.format(xargs.algo))
  search_time.update(time.time() - start_time)

  valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion, xargs.algo, logger)
  logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1))

  logger.log('\n' + '-'*100)
  # check the performance from the architecture dataset
  logger.log('[{:}] run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(xargs.algo, total_epoch, search_time.sum, genotype))
  if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype, '200') ))
  logger.close()
  

if __name__ == '__main__':
  parser = argparse.ArgumentParser("Weight sharing NAS methods to search for cells.")
  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.')
  parser.add_argument('--search_space',       type=str,   default='tss', choices=['tss'], help='The search space name.')
  parser.add_argument('--algo'        ,       type=str,   choices=['darts-v1', 'darts-v2', 'gdas', 'setn', 'random', 'enas'], help='The search space name.')
  # FOR GDAS
  parser.add_argument('--tau_min',            type=float, default=0.1,  help='The minimum tau for Gumbel Softmax.')
  parser.add_argument('--tau_max',            type=float, default=10,   help='The maximum tau for Gumbel Softmax.')
  # channels and number-of-cells
  parser.add_argument('--max_nodes'   ,       type=int,   default=4,  help='The maximum number of nodes.')
  parser.add_argument('--channel'     ,       type=int,   default=16, help='The number of channels.')
  parser.add_argument('--num_cells'   ,       type=int,   default=5,  help='The number of cells in one stage.')
  #
  parser.add_argument('--eval_candidate_num', type=int,   default=100, help='The number of selected architectures to evaluate.')
  #
  parser.add_argument('--track_running_stats',type=int,   default=0, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
  parser.add_argument('--affine'      ,       type=int,   default=0, choices=[0,1],help='Whether use affine=True or False in the BN layer.')
  parser.add_argument('--config_path' ,       type=str,   default='./configs/nas-benchmark/algos/weight-sharing.config', help='The path of configuration.')
  # 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('--arch_eps'          , type=float, default=1e-8, help='weight decay for arch encoding')
  parser.add_argument('--drop_path_rate'  ,  type=float, help='The drop path rate.')
  # log
  parser.add_argument('--workers',            type=int,   default=2,    help='number of data loading workers (default: 2)')
  parser.add_argument('--save_dir',           type=str,   default='./output/search', help='Folder to save checkpoints and log.')
  parser.add_argument('--print_freq',         type=int,   default=200,  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)
  args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space),
                               args.dataset,
                               '{:}-affine{:}_BN{:}-{:}'.format(args.algo, args.affine, args.track_running_stats, args.drop_path_rate))

  main(args)