fix train-final naming issue

nas-search/plot-progress/parse_search_output.py

@@ -33,6 +33,7 @@ def encode_single_path_nas_arch(inds, hard=False):
   for layer_cnt in range(20):
 
     inds_row = inds[layer_cnt]
+    print(inds_row)
     if inds_row == [0.0, 0.0, 0.0]:
       idx = 4 # skip 
     elif inds_row == [0.0, 0.0, 1.0]:

train-final/models.py

@@ -107,7 +107,7 @@ def encode(self, blocks_args):
 
 
 import parse_netarch
-def parse_netarch(parse_lambda_dir, depth_multiplier=None):
+def parse_netarch_model(parse_lambda_dir, depth_multiplier=None):
   """Creates the RNAS found model. No need to hard-code
   model, it parses the output of previous search
 
@@ -128,7 +128,7 @@ def parse_netarch(parse_lambda_dir, depth_multiplier=None):
   indicator_values = parse_netarch.parse_indicators_single_path_nas(parse_lambda_dir, tf_size_guidance)
   network = parse_netarch.encode_single_path_nas_arch(indicator_values)
   parse_netarch.print_net(network)
-  blocks_args = parse_netarch.mnasnet_encoder(network)
+  blocks_args = parse_netarch.convnet_encoder(network)
   parse_netarch.print_encoded_net(blocks_args)
 
   decoder = MnasNetDecoder()
@@ -146,7 +146,7 @@ def parse_netarch(parse_lambda_dir, depth_multiplier=None):
 
 
 def build_model(images, model_name, training, override_params=None,
-        parse_output_dir=None):
+        parse_search_dir=None):
   """A helper functiion to creates a ConvNet model and returns predicted logits.
 
   Args:
@@ -165,8 +165,8 @@ def build_model(images, model_name, training, override_params=None,
   """
   assert isinstance(images, tf.Tensor)
   if model_name == 'single-path':
-    assert parse_output_dir is not None
-    blocks_args, global_params = parse_netarch(parse_output_dir)
+    assert parse_search_dir is not None
+    blocks_args, global_params = parse_netarch_model(parse_search_dir)
   else:
     raise NotImplementedError('model name is not pre-defined: %s' % model_name)