support first-order DARTS on the NASNet search space

configs/search-archs/DARTS-NASNet-CIFAR.config

@@ -0,0 +1,9 @@
+{
+  "super_type"      : ["str",  "nasnet-super"],
+  "name"            : ["str",  "GDAS"],
+  "C"               : ["int",  "16"  ],
+  "N"               : ["int",  "2"  ],
+  "steps"           : ["int",  "4"  ],
+  "multiplier"      : ["int",  "4"  ],
+  "stem_multiplier" : ["int",  "3"  ]
+}

configs/search-opts/DARTS-NASNet-CIFAR.config

@@ -0,0 +1,13 @@
+{
+  "scheduler": ["str",   "cos"],
+  "LR"       : ["float", "0.025"],
+  "eta_min"  : ["float", "0.001"],
+  "epochs"   : ["int",   "50"],
+  "warmup"   : ["int",   "0"],
+  "optim"    : ["str",   "SGD"],
+  "decay"    : ["float", "0.0005"],
+  "momentum" : ["float", "0.9"],
+  "nesterov" : ["bool",  "1"],
+  "criterion": ["str",   "Softmax"],
+  "batch_size": ["int",  "256"]
+}

docs/CVPR-2019-GDAS.md

@@ -46,13 +46,13 @@ If you want to train the searched architecture found by the above scripts, you n
 ### Searching on a small search space (NAS-Bench-201)
 The GDAS searching codes on a small search space:
 ```
-CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 -1
+CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 1 -1
 ```
 
 The baseline searching codes are DARTS:
 ```
-CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V1.sh cifar10 -1
-CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 -1
+CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V1.sh cifar10 1 -1
+CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 1 -1
 ```
 
 **After searching**, if you want to train the searched architecture found by the above scripts, please use the following codes:

docs/ICCV-2019-SETN.md

@@ -32,7 +32,7 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN
 
 The searching codes of SETN on a small search space (NAS-Bench-201).
 ```
-CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh cifar10 -1
+CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh cifar10 1 -1
 ```
 
 

docs/ICLR-2019-DARTS.md

@@ -10,6 +10,11 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 1 -1
 CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh     cifar10 1 -1
 ```
 
+**Run the first-order DARTS on the NASNet search space**:
+```
+CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/DARTS1V-search-NASNet-space.sh cifar10 -1
+```
+
 # Citation
 
 ```

exps/algos/DARTS-V1.py

@@ -112,10 +112,14 @@ def main(xargs):
   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,
-                              'affine'   : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
+  if xargs.model_config is None:
+    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,
+                                'affine'   : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
+  else:
+    model_config = load_config(xargs.model_config, {'num_classes': class_num, '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))
 
@@ -213,12 +217,13 @@ def main(xargs):
   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('--config_path',        type=str,   help='The config path.')
   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('--track_running_stats',type=int,   choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
+  parser.add_argument('--config_path',        type=str,   help='The config path.')
+  parser.add_argument('--model_config',       type=str,   help='The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.')
   # 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')

lib/models/cell_searchs/__init__.py

@@ -10,6 +10,7 @@
 from .genotypes             import Structure as CellStructure, architectures as CellArchitectures
 # NASNet-based macro structure
 from .search_model_gdas_nasnet import NASNetworkGDAS
+from .search_model_darts_nasnet import NASNetworkDARTS
 
 
 nas201_super_nets = {'DARTS-V1': TinyNetworkDarts,
@@ -19,4 +20,5 @@
                   'ENAS'    : TinyNetworkENAS,
                   'RANDOM'  : TinyNetworkRANDOM}
 
-nasnet_super_nets = {'GDAS' : NASNetworkGDAS}
+nasnet_super_nets = {'GDAS' : NASNetworkGDAS,
+                     'DARTS': NASNetworkDARTS}

lib/models/cell_searchs/search_cells.py

@@ -131,10 +131,12 @@ def __init__(self, space, C, stride, affine, track_running_stats):
       op = OPS[primitive](C, C, stride, affine, track_running_stats)
       self._ops.append(op)
 
-  def forward(self, x, weights, index):
-    #return sum(w * op(x) for w, op in zip(weights, self._ops))
+  def forward_gdas(self, x, weights, index):
     return self._ops[index](x) * weights[index]
 
+  def forward_darts(self, x, weights):
+    return sum(w * op(x) for w, op in zip(weights, self._ops))
+
 
 # Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018
 class NASNetSearchCell(nn.Module):
@@ -173,7 +175,23 @@ def forward_gdas(self, s0, s1, weightss, indexs):
         op = self.edges[ node_str ]
         weights = weightss[ self.edge2index[node_str] ]
         index   = indexs[ self.edge2index[node_str] ].item()
-        clist.append( op(h, weights, index) )
+        clist.append( op.forward_gdas(h, weights, index) )
+      states.append( sum(clist) )
+
+    return torch.cat(states[-self._multiplier:], dim=1)
+
+  def forward_darts(self, s0, s1, weightss):
+    s0 = self.preprocess0(s0)
+    s1 = self.preprocess1(s1)
+
+    states = [s0, s1]
+    for i in range(self._steps):
+      clist = []
+      for j, h in enumerate(states):
+        node_str = '{:}<-{:}'.format(i, j)
+        op = self.edges[ node_str ]
+        weights = weightss[ self.edge2index[node_str] ]
+        clist.append( op.forward_darts(h, weights) )
       states.append( sum(clist) )
 
     return torch.cat(states[-self._multiplier:], dim=1)

lib/models/cell_searchs/search_model_darts_nasnet.py

@@ -0,0 +1,107 @@
+####################
+# DARTS, ICLR 2019 # 
+####################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from .search_cells     import NASNetSearchCell as SearchCell
+from .genotypes        import Structure
+
+
+# The macro structure is based on NASNet
+class NASNetworkDARTS(nn.Module):
+
+  def __init__(self, C, N, steps, multiplier, stem_multiplier, num_classes, search_space, affine, track_running_stats):
+    super(NASNetworkDARTS, self).__init__()
+    self._C        = C
+    self._layerN   = N
+    self._steps    = steps
+    self._multiplier = multiplier
+    self.stem = nn.Sequential(
+                    nn.Conv2d(3, C*stem_multiplier, kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(C*stem_multiplier))
+
+    # config for each layer
+    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * (N-1) + [C*4 ] + [C*4  ] * (N-1)
+    layer_reductions = [False] * N + [True] + [False] * (N-1) + [True] + [False] * (N-1)
+
+    num_edge, edge2index = None, None
+    C_prev_prev, C_prev, C_curr, reduction_prev = C*stem_multiplier, C*stem_multiplier, C, False
+
+    self.cells = nn.ModuleList()
+    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
+      cell = SearchCell(search_space, steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, 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_prev, C_prev, reduction_prev = C_prev, multiplier*C_curr, reduction
+    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_normal_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
+    self.arch_reduce_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_normal_parameters, self.arch_reduce_parameters]
+
+  def show_alphas(self):
+    with torch.no_grad():
+      A = 'arch-normal-parameters :\n{:}'.format( nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu() )
+      B = 'arch-reduce-parameters :\n{:}'.format( nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu() )
+    return '{:}\n{:}'.format(A, B)
+
+  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}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  def genotype(self):
+    def _parse(weights):
+      gene = []
+      for i in range(self._steps):
+        edges = []
+        for j in range(2+i):
+          node_str = '{:}<-{:}'.format(i, j)
+          ws = weights[ self.edge2index[node_str] ]
+          for k, op_name in enumerate(self.op_names):
+            if op_name == 'none': continue
+            edges.append( (op_name, j, ws[k]) )
+        edges = sorted(edges, key=lambda x: -x[-1])
+        selected_edges = edges[:2]
+        gene.append( tuple(selected_edges) )
+      return gene
+    with torch.no_grad():
+      gene_normal = _parse(torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy())
+      gene_reduce = _parse(torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy())
+    return {'normal': gene_normal, 'normal_concat': list(range(2+self._steps-self._multiplier, self._steps+2)),
+            'reduce': gene_reduce, 'reduce_concat': list(range(2+self._steps-self._multiplier, self._steps+2))}
+
+  def forward(self, inputs):
+
+    normal_w = nn.functional.softmax(self.arch_normal_parameters, dim=1)
+    reduce_w = nn.functional.softmax(self.arch_reduce_parameters, dim=1)
+
+    s0 = s1 = self.stem(inputs)
+    for i, cell in enumerate(self.cells):
+      if cell.reduction: ww = reduce_w
+      else             : ww = normal_w
+      s0, s1 = s1, cell.forward_darts(s0, s1, ww)
+    out = self.lastact(s1)
+    out = self.global_pooling( out )
+    out = out.view(out.size(0), -1)
+    logits = self.classifier(out)
+
+    return out, logits

scripts-search/DARTS1V-search-NASNet-space.sh

@@ -0,0 +1,41 @@
+#!/bin/bash
+# bash ./scripts-search/DARTS1V-search-NASNet-space.sh cifar10 -1
+echo script name: $0
+echo $# arguments
+if [ "$#" -ne 2 ] ;then
+  echo "Input illegal number of parameters " $#
+  echo "Need 2 parameters for dataset, and seed"
+  exit 1
+fi
+if [ "$TORCH_HOME" = "" ]; then
+  echo "Must set TORCH_HOME envoriment variable for data dir saving"
+  exit 1
+else
+  echo "TORCH_HOME : $TORCH_HOME"
+fi
+
+dataset=$1
+BN=1
+seed=$2
+channel=16
+num_cells=5
+max_nodes=4
+space=darts
+
+if [ "$dataset" == "cifar10" ] || [ "$dataset" == "cifar100" ]; then
+  data_path="$TORCH_HOME/cifar.python"
+else
+  data_path="$TORCH_HOME/cifar.python/ImageNet16"
+fi
+
+save_dir=./output/search-cell-${space}/DARTS-V1-${dataset}-BN${BN}
+
+OMP_NUM_THREADS=4 python ./exps/algos/DARTS-V1.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} \
+	--config_path configs/search-opts/DARTS-NASNet-CIFAR.config \
+	--model_config configs/search-archs/GDAS-NASNet-CIFAR.config \
+	--track_running_stats ${BN} \
+	--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
+	--workers 4 --print_freq 200 --rand_seed ${seed}