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

lib/models/__init__.py

@@ -3,6 +3,7 @@
 ##################################################
 from os import path as osp
 from typing import List, Text
+import torch
 
 __all__ = ['change_key', 'get_cell_based_tiny_net', 'get_search_spaces', 'get_cifar_models', 'get_imagenet_models', \
            'obtain_model', 'obtain_search_model', 'load_net_from_checkpoint', \
@@ -38,6 +39,9 @@ def get_cell_based_tiny_net(config):
       genotype = CellStructure.str2structure(config.arch_str)
     else: raise ValueError('Can not find genotype from this config : {:}'.format(config))
     return TinyNetwork(config.C, config.N, genotype, config.num_classes)
+  elif config.name == 'infer.nasnet-cifar':
+    from .cell_infers import NASNetonCIFAR
+    raise NotImplementedError
   else:
     raise ValueError('invalid network name : {:}'.format(config.name))
 
@@ -52,13 +56,12 @@ def get_search_spaces(xtype, name) -> List[Text]:
     raise ValueError('invalid search-space type is {:}'.format(xtype))
 
 
-def get_cifar_models(config):
-  from .CifarResNet      import CifarResNet
-  from .CifarDenseNet    import DenseNet
-  from .CifarWideResNet  import CifarWideResNet
-  
+def get_cifar_models(config, extra_path=None):
   super_type = getattr(config, 'super_type', 'basic')
   if super_type == 'basic':
+    from .CifarResNet      import CifarResNet
+    from .CifarDenseNet    import DenseNet
+    from .CifarWideResNet  import CifarWideResNet
     if config.arch == 'resnet':
       return CifarResNet(config.module, config.depth, config.class_num, config.zero_init_residual)
     elif config.arch == 'densenet':
@@ -71,6 +74,7 @@ def get_cifar_models(config):
     from .shape_infers import InferWidthCifarResNet
     from .shape_infers import InferDepthCifarResNet
     from .shape_infers import InferCifarResNet
+    from .cell_infers import NASNetonCIFAR
     assert len(super_type.split('-')) == 2, 'invalid super_type : {:}'.format(super_type)
     infer_mode = super_type.split('-')[1]
     if infer_mode == 'width':
@@ -79,6 +83,16 @@ def get_cifar_models(config):
       return InferDepthCifarResNet(config.module, config.depth, config.xblocks, config.class_num, config.zero_init_residual)
     elif infer_mode == 'shape':
       return InferCifarResNet(config.module, config.depth, config.xblocks, config.xchannels, config.class_num, config.zero_init_residual)
+    elif infer_mode == 'nasnet.cifar':
+      genotype = config.genotype
+      if extra_path is not None:  # reload genotype by extra_path
+        if not osp.isfile(extra_path): raise ValueError('invalid extra_path : {:}'.format(extra_path))
+        xdata = torch.load(extra_path)
+        current_epoch = xdata['epoch']
+        genotype = xdata['genotypes'][current_epoch-1]
+      C = config.C if hasattr(config, 'C') else config.ichannel
+      N = config.N if hasattr(config, 'N') else config.layers
+      return NASNetonCIFAR(C, N, config.stem_multi, config.class_num, genotype, config.auxiliary)
     else:
       raise ValueError('invalid infer-mode : {:}'.format(infer_mode))
   else:
@@ -111,9 +125,10 @@ def get_imagenet_models(config):
     raise ValueError('invalid super-type : {:}'.format(super_type))
 
 
-def obtain_model(config):
+# Try to obtain the network by config.
+def obtain_model(config, extra_path=None):
   if config.dataset == 'cifar':
-    return get_cifar_models(config)
+    return get_cifar_models(config, extra_path)
   elif config.dataset == 'imagenet':
     return get_imagenet_models(config)
   else:
@@ -152,7 +167,6 @@ def obtain_search_model(config):
 
 
 def load_net_from_checkpoint(checkpoint):
-  import torch
   assert osp.isfile(checkpoint), 'checkpoint {:} does not exist'.format(checkpoint)
   checkpoint   = torch.load(checkpoint)
   model_config = dict2config(checkpoint['model-config'], None)