Update Weight Watcher in utils

exps/algos/BOHB.py

@@ -5,18 +5,15 @@
 # required to install hpbandster ##################################
 # bash ./scripts-search/algos/BOHB.sh -1         ##################
 ###################################################################
-import os, sys, time, glob, random, argparse
-import numpy as np, collections
+import os, sys, time, random, argparse
 from copy import deepcopy
 from pathlib import Path
 import torch
-import torch.nn as nn
 lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
 if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
-from config_utils import load_config, dict2config, configure2str
+from config_utils import load_config
 from datasets     import get_datasets, SearchDataset
-from procedures   import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
-from utils        import get_model_infos, obtain_accuracy
+from procedures   import prepare_seed, prepare_logger
 from log_utils    import AverageMeter, time_string, convert_secs2time
 from nas_201_api  import NASBench201API as API
 from models       import CellStructure, get_search_spaces

exps/algos/DARTS-V1.py

@@ -3,11 +3,9 @@
 ########################################################
 # DARTS: Differentiable Architecture Search, ICLR 2019 #
 ########################################################
-import os, sys, time, glob, 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))

exps/experimental/test-ww.py

@@ -0,0 +1,21 @@
+import sys, time, random, argparse
+from copy import deepcopy
+import torchvision.models as models
+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))
+
+from utils import weight_watcher
+
+
+def main():
+  model = models.vgg19_bn(pretrained=True)
+  _, summary = weight_watcher.analyze(model, alphas=False)
+  # print(summary)
+  for key, value in summary.items():
+    print('{:10s} : {:}'.format(key, value))
+  # import pdb; pdb.set_trace()
+
+
+if __name__ == '__main__':
+  main()