Prototype generic nas model.

configs/nas-benchmark/algos/weight-sharing.config

@@ -0,0 +1,14 @@
+{
+  "scheduler": ["str",   "cos"],
+  "LR"       : ["float", "0.025"],
+  "eta_min"  : ["float", "0.001"],
+  "epochs"   : ["int",   "250"],
+  "warmup"   : ["int",   "0"],
+  "optim"    : ["str",   "SGD"],
+  "decay"    : ["float", "0.0005"],
+  "momentum" : ["float", "0.9"],
+  "nesterov" : ["bool",  "1"],
+  "criterion": ["str",   "Softmax"],
+  "batch_size": ["int",  "64"],
+  "test_batch_size": ["int",  "512"]
+}

exps/algos-v2/run-all.sh

@@ -14,7 +14,7 @@ do
     python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
     python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
     python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
-    python exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
+    python ./exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
   done
 done
 

exps/algos-v2/search-cell.py

@@ -0,0 +1,290 @@
+##################################################
+# 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 1
+# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v1
+# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v1
+######################################################################################
+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
+
+
+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()
+  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_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
+    sampled_arch = network.module.dync_genotype(True)
+    network.module.set_cal_mode('dynamic', sampled_arch)
+    #network.module.set_cal_mode( 'urs' )
+    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
+    network.module.set_cal_mode( 'joint' )
+    network.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)
+      #print (nn.functional.softmax(network.module.arch_parameters, dim=-1))
+      #print (network.module.arch_parameters)
+  return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
+
+
+def get_best_arch(xloader, network, n_samples):
+  with torch.no_grad():
+    network.eval()
+    archs, valid_accs = network.module.return_topK(n_samples), []
+    #print ('obtain the top-{:} architectures'.format(n_samples))
+    loader_iter = iter(xloader)
+    for i, sampled_arch in enumerate(archs):
+      network.module.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)
+      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):
+  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)
+      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, _, 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)
+
+  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)
+  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))
+  api = API()
+  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 = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
+  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']
+    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, 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())))
+
+    import pdb; pdb.set_trace()
+  
+    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, 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))
+
+    genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
+    network.module.set_cal_mode('dynamic', genotype)
+    valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
+    logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype))
+    #search_model.set_cal_mode('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
+
+    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),
+                '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.select_num)
+  search_time.update(time.time() - start_time)
+  network.module.set_cal_mode('dynamic', genotype)
+  valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
+  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('SETN : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(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.')
+  # 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,   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')
+  # 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,   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, args.algo)
+
+  main(args)

lib/models/__init__.py

@@ -20,7 +20,7 @@ from .cell_searchs import CellStructure, CellArchitectures
 def get_cell_based_tiny_net(config):
   if isinstance(config, dict): config = dict2config(config, None) # to support the argument being a dict
   super_type = getattr(config, 'super_type', 'basic')
-  group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS', 'RANDOM']
+  group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS', 'RANDOM', 'generic']
   if super_type == 'basic' and config.name in group_names:
     from .cell_searchs import nas201_super_nets as nas_super_nets
     try:

lib/models/cell_searchs/__init__.py

@@ -7,6 +7,7 @@ from .search_model_gdas     import TinyNetworkGDAS
 from .search_model_setn     import TinyNetworkSETN
 from .search_model_enas     import TinyNetworkENAS
 from .search_model_random   import TinyNetworkRANDOM
+from .generic_model         import GenericNAS201Model
 from .genotypes             import Structure as CellStructure, architectures as CellArchitectures
 # NASNet-based macro structure
 from .search_model_gdas_nasnet import NASNetworkGDAS
@@ -18,7 +19,8 @@ nas201_super_nets = {'DARTS-V1': TinyNetworkDarts,
                      "GDAS": TinyNetworkGDAS,
                      "SETN": TinyNetworkSETN,
                      "ENAS": TinyNetworkENAS,
-                     "RANDOM": TinyNetworkRANDOM}
+                     "RANDOM": TinyNetworkRANDOM,
+                     "generic": GenericNAS201Model}
 
 nasnet_super_nets = {"GDAS": NASNetworkGDAS,
                      "DARTS": NASNetworkDARTS}

lib/models/cell_searchs/generic_model.py

@@ -0,0 +1,200 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
+#####################################################
+import torch, random
+import torch.nn as nn
+from copy import deepcopy
+from typing import Text
+
+from ..cell_operations import ResNetBasicblock
+from .search_cells     import NAS201SearchCell as SearchCell
+from .genotypes        import Structure
+from .search_model_enas_utils import Controller
+
+
+class GenericNAS201Model(nn.Module):
+
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
+    super(GenericNAS201Model, 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, affine, track_running_stats)
+        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)
+    self._num_edge   = num_edge
+    # algorithm related
+    self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
+    self._mode        = None
+    self.dynamic_cell = None
+    self._tau         = None
+    self._algo        = None
+
+  def set_algo(self, algo: Text):
+    # used for searching
+    assert self._algo is None, 'This functioin can only be called once.'
+    self._algo = algo
+    if algo == 'enas':
+      self.controller = Controller(len(self.edge2index), len(self._op_names))
+    else:
+      self.arch_parameters = nn.Parameter( 1e-3*torch.randn(self._num_edge, len(self._op_names)) )
+      if algo == 'gdas':
+        self._tau         = 10
+    
+  def set_cal_mode(self, mode, dynamic_cell=None):
+    assert mode in ['gdas', 'enas', 'urs', 'joint', 'select', 'dynamic']
+    self.mode = mode
+    if mode == 'dynamic': self.dynamic_cell = deepcopy(dynamic_cell)
+    else                : self.dynamic_cell = None
+
+  @property
+  def mode(self):
+    return self._mode
+
+  @property
+  def weights(self):
+    xlist = list(self._stem.parameters())
+    xlist+= list(self._cells.parameters())
+    xlist+= list(self.lastact.parameters())
+    xlist+= list(self.global_pooling.parameters())
+    xlist+= list(self.classifier.parameters())
+    return xlist
+
+  def set_tau(self, tau):
+    self._tau = tau
+
+  @property
+  def tau(self):
+    return self._tau
+
+  @property
+  def alphas(self):
+    if self._algo == 'enas':
+      return list(self.controller.parameters())
+    else:
+      return [self.arch_parameters]
+
+  @property
+  def 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}, alg={_algo})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  @property
+  def genotype(self):
+    genotypes = []
+    for i in range(1, self._max_nodes):
+      xlist = []
+      for j in range(i):
+        node_str = '{:}<-{:}'.format(i, j)
+        with torch.no_grad():
+          weights = self.arch_parameters[ self.edge2index[node_str] ]
+          op_name = self.op_names[ weights.argmax().item() ]
+        xlist.append((op_name, j))
+      genotypes.append(tuple(xlist))
+    return Structure(genotypes)
+
+  def dync_genotype(self, use_random=False):
+    genotypes = []
+    with torch.no_grad():
+      alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
+    for i in range(1, self._max_nodes):
+      xlist = []
+      for j in range(i):
+        node_str = '{:}<-{:}'.format(i, j)
+        if use_random:
+          op_name  = random.choice(self.op_names)
+        else:
+          weights  = alphas_cpu[ self.edge2index[node_str] ]
+          op_index = torch.multinomial(weights, 1).item()
+          op_name  = self.op_names[ op_index ]
+        xlist.append((op_name, j))
+      genotypes.append(tuple(xlist))
+    return Structure(genotypes)
+
+  def get_log_prob(self, arch):
+    with torch.no_grad():
+      logits = nn.functional.log_softmax(self.arch_parameters, dim=-1)
+    select_logits = []
+    for i, node_info in enumerate(arch.nodes):
+      for op, xin in node_info:
+        node_str = '{:}<-{:}'.format(i+1, xin)
+        op_index = self.op_names.index(op)
+        select_logits.append( logits[self.edge2index[node_str], op_index] )
+    return sum(select_logits).item()
+
+  def return_topK(self, K):
+    archs = Structure.gen_all(self.op_names, self._max_nodes, False)
+    pairs = [(self.get_log_prob(arch), arch) for arch in archs]
+    if K < 0 or K >= len(archs): K = len(archs)
+    sorted_pairs = sorted(pairs, key=lambda x: -x[0])
+    return_pairs = [sorted_pairs[_][1] for _ in range(K)]
+    return return_pairs
+
+  def normalize_archp(self):
+    if self.mode == 'gdas':
+      while True:
+        gumbels = -torch.empty_like(self.arch_parameters).exponential_().log()
+        logits  = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.tau
+        probs   = nn.functional.softmax(logits, dim=1)
+        index   = probs.max(-1, keepdim=True)[1]
+        one_h   = torch.zeros_like(logits).scatter_(-1, index, 1.0)
+        hardwts = one_h - probs.detach() + probs
+        if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()):
+          continue
+        else: break
+      with torch.no_grad():
+        hardwts_cpu = hardwts.detach().cpu()
+      return hardwts, hardwts_cpu, index
+    else:
+      alphas  = nn.functional.softmax(self.arch_parameters, dim=-1)
+      index   = alphas.max(-1, keepdim=True)[1]
+      with torch.no_grad():
+        alphas_cpu = alphas.detach().cpu()
+      return alphas, alphas_cpu, index
+
+  def forward(self, inputs):
+    alphas, alphas_cpu, index = self.normalize_archp()
+    feature = self._stem(inputs)
+    for i, cell in enumerate(self._cells):
+      if isinstance(cell, SearchCell):
+        if self.mode == 'urs':
+          feature = cell.forward_urs(feature)
+        elif self.mode == 'select':
+          feature = cell.forward_select(feature, alphas_cpu)
+        elif self.mode == 'joint':
+          feature = cell.forward_joint(feature, alphas)
+        elif self.mode == 'dynamic':
+          feature = cell.forward_dynamic(feature, self.dynamic_cell)
+        elif self.mode == 'gdas':
+          feature = cell.forward_gdas(feature, alphas, index)
+        else: raise ValueError('invalid mode={:}'.format(self.mode))
+      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/utils/__init__.py

@@ -1,5 +1,5 @@
 from .evaluation_utils import obtain_accuracy
 from .gpu_manager      import GPUManager
-from .flop_benchmark   import get_model_infos
+from .flop_benchmark   import get_model_infos, count_parameters_in_MB
 from .affine_utils     import normalize_points, denormalize_points
 from .affine_utils     import identity2affine, solve2theta, affine2image