update CVPR-2019-GDAS re-train NASNet-search-space searched models

lib/nas_infer_model/DXYs/genotypes.py

@@ -168,5 +168,15 @@ Networks = {'DARTS_V1': DARTS_V1,
             'SETN'    : SETN,
            }
 
+# This function will return a Genotype from a dict.
 def build_genotype_from_dict(xdict):
-  import pdb; pdb.set_trace()
+  def remove_value(nodes):
+    return [tuple([(x[0], x[1]) for x in node]) for node in nodes]
+  genotype = Genotype(
+      normal=remove_value(xdict['normal']),
+      normal_concat=xdict['normal_concat'],
+      reduce=remove_value(xdict['reduce']),
+      reduce_concat=xdict['reduce_concat'],
+      connectN=None, connects=None
+      )
+  return genotype

lib/nas_infer_model/__init__.py

@@ -6,12 +6,22 @@
 # Currently, this package is used to reproduce the results in GDAS (Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019).
 ##################################################
 
-import torch
+import os, torch
 
-def obtain_nas_infer_model(config):
+def obtain_nas_infer_model(config, extra_model_path=None):
+  
   if config.arch == 'dxys':
     from .DXYs import CifarNet, ImageNet, Networks
-    genotype = Networks[config.genotype]
+    from .DXYs import build_genotype_from_dict
+    if config.genotype is None:
+      if extra_model_path is not None and not os.path.isfile(extra_model_path):
+        raise ValueError('When genotype in confiig is None, extra_model_path must be set as a path instead of {:}'.format(extra_model_path))
+      xdata = torch.load(extra_model_path)
+      current_epoch = xdata['epoch']
+      genotype_dict = xdata['genotypes'][current_epoch-1]
+      genotype = build_genotype_from_dict(genotype_dict)
+    else:
+      genotype = Networks[config.genotype]
     if config.dataset == 'cifar':
       return CifarNet(config.ichannel, config.layers, config.stem_multi, config.auxiliary, genotype, config.class_num)
     elif config.dataset == 'imagenet':