simplify DARTS codes and update affine/track

NAS-Bench-102.md

@@ -194,6 +194,7 @@ If you find that NAS-Bench-102 helps your research, please consider citing it:
   title     = {NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search},
   author    = {Dong, Xuanyi and Yang, Yi},
   booktitle = {International Conference on Learning Representations (ICLR)},
+  url       = {https://openreview.net/forum?id=HJxyZkBKDr},
   year      = {2020}
 }
 ```

README.md

@@ -15,7 +15,7 @@ More NAS resources can be found in [Awesome-NAS](https://github.com/D-X-Y/Awesom
 
 Please install `PyTorch>=1.2.0`, `Python>=3.6`, and `opencv`.
 
-The CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
+CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
 Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Driver](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.
 
 ### Usefull tools
@@ -150,6 +150,7 @@ If you find that this project helps your research, please consider citing some o
   title     = {NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search},
   author    = {Dong, Xuanyi and Yang, Yi},
   booktitle = {International Conference on Learning Representations (ICLR)},
+  url       = {https://openreview.net/forum?id=HJxyZkBKDr},
   year      = {2020}
 }
 @inproceedings{dong2019tas,

exps/algos/DARTS-V1.py

@@ -114,7 +114,8 @@ def main(xargs):
   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)
+                              'space'    : search_space,
+                              'affine'   : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
   search_model = get_cell_based_tiny_net(model_config)
   logger.log('search-model :\n{:}'.format(search_model))
   
@@ -217,6 +218,7 @@ if __name__ == '__main__':
   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('--track_running_stats',type=int,   choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
   # 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')

exps/algos/DARTS-V2.py

@@ -177,7 +177,8 @@ def main(xargs):
   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)
+                              'space'    : search_space,
+                              'affine'   : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
   search_model = get_cell_based_tiny_net(model_config)
   logger.log('search-model :\n{:}'.format(search_model))
   
@@ -282,6 +283,7 @@ if __name__ == '__main__':
   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('--track_running_stats',type=int,   choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
   # 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')

exps/algos/ENAS.py

@@ -198,7 +198,8 @@ def main(xargs):
   search_space = get_search_spaces('cell', xargs.search_space_name)
   model_config = dict2config({'name': 'ENAS', 'C': xargs.channel, 'N': xargs.num_cells,
                               'max_nodes': xargs.max_nodes, 'num_classes': class_num,
-                              'space'    : search_space}, None)
+                              'space'    : search_space,
+                              'affine'   : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
   shared_cnn = get_cell_based_tiny_net(model_config)
   controller = shared_cnn.create_controller()
   
@@ -319,6 +320,7 @@ if __name__ == '__main__':
   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('--track_running_stats',type=int,   choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
   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.')

exps/algos/RANDOM-NAS.py

@@ -126,7 +126,8 @@ def main(xargs):
   search_space = get_search_spaces('cell', xargs.search_space_name)
   model_config = dict2config({'name': 'RANDOM', 'C': xargs.channel, 'N': xargs.num_cells,
                               'max_nodes': xargs.max_nodes, 'num_classes': class_num,
-                              'space'    : search_space}, None)
+                              'space'    : search_space,
+                              'affine'   : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
   search_model = get_cell_based_tiny_net(model_config)
   
   w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.parameters(), config)
@@ -222,6 +223,7 @@ if __name__ == '__main__':
   parser.add_argument('--channel',            type=int,   help='The number of channels.')
   parser.add_argument('--num_cells',          type=int,   help='The number of cells in one stage.')
   parser.add_argument('--select_num',         type=int,   help='The number of selected architectures to evaluate.')
+  parser.add_argument('--track_running_stats',type=int,   choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
   # 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.')

lib/models/cell_searchs/__init__.py

@@ -1,16 +1,15 @@
 ##################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
 ##################################################
-from .search_model_darts_v1 import TinyNetworkDartsV1
+from .search_model_darts    import TinyNetworkDarts
-from .search_model_darts_v2 import TinyNetworkDartsV2
 from .search_model_gdas     import TinyNetworkGDAS
 from .search_model_setn     import TinyNetworkSETN
 from .search_model_enas     import TinyNetworkENAS
 from .search_model_random   import TinyNetworkRANDOM
 from .genotypes             import Structure as CellStructure, architectures as CellArchitectures
 
-nas_super_nets = {'DARTS-V1': TinyNetworkDartsV1,
+nas_super_nets = {'DARTS-V1': TinyNetworkDarts,
-                  'DARTS-V2': TinyNetworkDartsV2,
+                  'DARTS-V2': TinyNetworkDarts,
                   'GDAS'    : TinyNetworkGDAS,
                   'SETN'    : TinyNetworkSETN,
                   'ENAS'    : TinyNetworkENAS,

lib/models/cell_searchs/search_model_darts.py

@@ -11,10 +11,10 @@ from .search_cells     import SearchCell
 from .genotypes        import Structure
 
 
-class TinyNetworkDartsV1(nn.Module):
+class TinyNetworkDarts(nn.Module):
 
-  def __init__(self, C, N, max_nodes, num_classes, search_space):
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
-    super(TinyNetworkDartsV1, self).__init__()
+    super(TinyNetworkDarts, self).__init__()
     self._C        = C
     self._layerN   = N
     self.max_nodes = max_nodes
@@ -31,7 +31,7 @@ class TinyNetworkDartsV1(nn.Module):
       if reduction:
         cell = ResNetBasicblock(C_prev, C_curr, 2)
       else:
-        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
+        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 )

lib/models/cell_searchs/search_model_darts_v2.py

@@ -1,93 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-########################################################
-# DARTS: Differentiable Architecture Search, ICLR 2019 #
-########################################################
-import torch
-import torch.nn as nn
-from copy import deepcopy
-from ..cell_operations import ResNetBasicblock
-from .search_cells     import SearchCell
-from .genotypes        import Structure
-
-
-class TinyNetworkDartsV2(nn.Module):
-
-  def __init__(self, C, N, max_nodes, num_classes, search_space):
-    super(TinyNetworkDartsV2, self).__init__()
-    self._C        = C
-    self._layerN   = N
-    self.max_nodes = max_nodes
-    self.stem = nn.Sequential(
-                    nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
-                    nn.BatchNorm2d(C))
-  
-    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * N + [C*4 ] + [C*4  ] * N    
-    layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
-
-    C_prev, num_edge, edge2index = C, None, None
-    self.cells = nn.ModuleList()
-    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
-      if reduction:
-        cell = ResNetBasicblock(C_prev, C_curr, 2)
-      else:
-        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
-        if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
-        else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
-      self.cells.append( cell )
-      C_prev = cell.out_dim
-    self.op_names   = deepcopy( search_space )
-    self._Layer     = len(self.cells)
-    self.edge2index = edge2index
-    self.lastact    = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
-    self.global_pooling = nn.AdaptiveAvgPool2d(1)
-    self.classifier = nn.Linear(C_prev, num_classes)
-    self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
-
-  def get_weights(self):
-    xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
-    xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() )
-    xlist+= list( self.classifier.parameters() )
-    return xlist
-
-  def get_alphas(self):
-    return [self.arch_parameters]
-
-  def get_message(self):
-    string = self.extra_repr()
-    for i, cell in enumerate(self.cells):
-      string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
-    return string
-
-  def extra_repr(self):
-    return ('{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
-
-  def 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 forward(self, inputs):
-    alphas  = nn.functional.softmax(self.arch_parameters, dim=-1)
-
-    feature = self.stem(inputs)
-    for i, cell in enumerate(self.cells):
-      if isinstance(cell, SearchCell):
-        feature = cell(feature, alphas)
-      else:
-        feature = cell(feature)
-
-    out = self.lastact(feature)
-    out = self.global_pooling( out )
-    out = out.view(out.size(0), -1)
-    logits = self.classifier(out)
-
-    return out, logits

lib/models/cell_searchs/search_model_enas.py

@@ -14,7 +14,7 @@ from .search_model_enas_utils import Controller
 
 class TinyNetworkENAS(nn.Module):
 
-  def __init__(self, C, N, max_nodes, num_classes, search_space):
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
     super(TinyNetworkENAS, self).__init__()
     self._C        = C
     self._layerN   = N
@@ -32,7 +32,7 @@ class TinyNetworkENAS(nn.Module):
       if reduction:
         cell = ResNetBasicblock(C_prev, C_curr, 2)
       else:
-        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
+        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 )

lib/models/cell_searchs/search_model_random.py

@@ -13,7 +13,7 @@ from .genotypes        import Structure
 
 class TinyNetworkRANDOM(nn.Module):
 
-  def __init__(self, C, N, max_nodes, num_classes, search_space):
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
     super(TinyNetworkRANDOM, self).__init__()
     self._C        = C
     self._layerN   = N
@@ -31,7 +31,7 @@ class TinyNetworkRANDOM(nn.Module):
       if reduction:
         cell = ResNetBasicblock(C_prev, C_curr, 2)
       else:
-        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
+        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 )

scripts-search/algos/DARTS-V1.sh

@@ -35,5 +35,6 @@ OMP_NUM_THREADS=4 python ./exps/algos/DARTS-V1.py \
 	--search_space_name ${space} \
 	--config_path configs/nas-benchmark/algos/DARTS.config \
 	--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
+	--track_running_stats 1 \
 	--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
 	--workers 4 --print_freq 200 --rand_seed ${seed}

scripts-search/algos/DARTS-V2.sh

@@ -35,5 +35,6 @@ OMP_NUM_THREADS=4 python ./exps/algos/DARTS-V2.py \
 	--search_space_name ${space} \
 	--config_path configs/nas-benchmark/algos/DARTS.config \
 	--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
+	--track_running_stats 1 \
 	--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
 	--workers 4 --print_freq 200 --rand_seed ${seed}

scripts-search/algos/ENAS.sh

@@ -35,6 +35,7 @@ OMP_NUM_THREADS=4 python ./exps/algos/ENAS.py \
 	--dataset ${dataset} --data_path ${data_path} \
 	--search_space_name ${space} \
 	--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
+	--track_running_stats 1 \
 	--config_path ./configs/nas-benchmark/algos/ENAS.config \
 	--controller_entropy_weight 0.0001 \
 	--controller_bl_dec 0.99 \

scripts-search/algos/RANDOM-NAS.sh

@@ -34,6 +34,7 @@ OMP_NUM_THREADS=4 python ./exps/algos/RANDOM-NAS.py \
 	--save_dir ${save_dir} --max_nodes ${max_nodes} --channel ${channel} --num_cells ${num_cells} \
 	--dataset ${dataset} --data_path ${data_path} \
 	--search_space_name ${space} \
+	--track_running_stats 1 \
 	--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
 	--config_path ./configs/nas-benchmark/algos/RANDOM.config \
 	--select_num 100 \