update NAS-Bench

exps/algos/GDAS.py

@@ -3,11 +3,9 @@
 ###########################################################################
 # Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
 ###########################################################################
-import os, sys, time, random, argparse
-import numpy as np
+import sys, time, random, argparse
 from copy import deepcopy
 import torch
-import torch.nn as nn
 from pathlib import Path
 lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
 if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
@@ -107,7 +105,6 @@ def main(xargs):
   logger.log('w-scheduler : {:}'.format(w_scheduler))
   logger.log('criterion   : {:}'.format(criterion))
   flop, param  = get_model_infos(search_model, xshape)
-  #logger.log('{:}'.format(search_model))
   logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
   logger.log('search-space [{:} ops] : {:}'.format(len(search_space), search_space))
   if xargs.arch_nas_dataset is None:

exps/algos/SETN.py

@@ -3,7 +3,7 @@
 ######################################################################################
 # One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
 ######################################################################################
-import os, sys, time, glob, random, argparse
+import sys, time, random, argparse
 import numpy as np
 from copy import deepcopy
 import torch
@@ -93,8 +93,7 @@ def get_best_arch(xloader, network, n_samples):
       _, logits = network(inputs)
       val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
 
-      valid_accs.append( val_top1.item() )
-      #print ('--- {:}/{:} : {:} : {:}'.format(i, len(archs), sampled_arch, val_top1))
+      valid_accs.append(val_top1.item())
 
     best_idx = np.argmax(valid_accs)
     best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
@@ -142,10 +141,13 @@ 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': 'SETN', '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(
+      dict(name='SETN', 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, dict(num_classes=class_num, space=search_space, affine=False,
+                                                        track_running_stats=bool(xargs.track_running_stats)), None)
   logger.log('search space : {:}'.format(search_space))
   search_model = get_cell_based_tiny_net(model_config)
   
@@ -156,7 +158,6 @@ def main(xargs):
   logger.log('w-scheduler : {:}'.format(w_scheduler))
   logger.log('criterion   : {:}'.format(criterion))
   flop, param  = get_model_infos(search_model, xshape)
-  #logger.log('{:}'.format(search_model))
   logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
   logger.log('search-space : {:}'.format(search_space))
   if xargs.arch_nas_dataset is None:
@@ -233,7 +234,7 @@ def main(xargs):
           'last_checkpoint': save_path,
           }, logger.path('info'), logger)
     with torch.no_grad():
-      logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
+      logger.log('{:}'.format(search_model.show_alphas()))
     if api is not None: logger.log('{:}'.format(api.query_by_arch( genotypes[epoch] )))
     # measure elapsed time
     epoch_time.update(time.time() - start_time)