resnet manual training, add .contiguous() for PyTorch 1.7

archai/common/common.py

@@ -172,7 +172,7 @@ def common_init(config_filepath: Optional[str]=None,
 
     _create_sysinfo(conf)
 
-    # create a[ex to know distributed processing paramters
+    # create apex to know distributed processing paramters
     conf_apex = get_conf_common(conf)['apex']
     apex = ApexUtils(conf_apex, logger=logger)
 

archai/common/ml_utils.py

@@ -207,7 +207,7 @@ def accuracy(output, target, topk=(1,)):
 
     res = []
     for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
+        correct_k = correct[:k].contiguous().view(-1).float().sum(0)
         res.append(correct_k.mul_(1.0 / batch_size))
 
     return res

archai/common/trainer.py

@@ -41,6 +41,8 @@ class Trainer(EnforceOverrides):
         self._validation_freq = 0 if conf_validation is None else conf_validation['freq']
         # endregion
 
+        logger.pushd(self._title + '__init__')
+
         self._apex = ApexUtils(conf_apex, logger)
 
         self._checkpoint = checkpoint
@@ -59,6 +61,8 @@ class Trainer(EnforceOverrides):
 
         self._start_epoch = -1 # nothing is started yet
 
+        logger.popd()
+
     def fit(self, train_dl:DataLoader, val_dl:Optional[DataLoader])->Metrics:
         logger.pushd(self._title)
 

confs/algos/resnet.yaml

@@ -0,0 +1,18 @@
+__include__: 'darts.yaml' # just use darts defaults
+
+common:
+  experiment_name: 'MyResnet'
+
+nas:
+  eval:
+    loader:
+      train_batch: 128
+      test_batch: 4096
+      cutout: 0
+      drop_path_prob: 0.0
+      grad_clip: 0.0
+      aux_weight: 0.0
+    trainer:
+      epochs: 10
+      logger_freq: 1
+

scripts/plain_models/train_resnet.py

@@ -12,19 +12,8 @@ from archai.common.common import logger, common_init
 from archai.datasets import data
 
 def train_test(conf_eval:Config):
-    # region conf vars
     conf_loader       = conf_eval['loader']
     conf_trainer = conf_eval['trainer']
-    # endregion
-
-    conf_trainer['validation']['freq']=1
-    conf_trainer['epochs'] = 10
-    conf_loader['train_batch'] = 128
-    conf_loader['test_batch'] = 4096
-    conf_loader['cutout'] = 0
-    conf_trainer['drop_path_prob'] = 0.0
-    conf_trainer['grad_clip'] = 0.0
-    conf_trainer['aux_weight'] = 0.0
 
     Net = cifar10_models.resnet34
     model = Net().to(torch.device('cuda', 0))
@@ -38,8 +27,7 @@ def train_test(conf_eval:Config):
 
 
 if __name__ == '__main__':
-    conf = common_init(config_filepath='confs/algos/darts.yaml',
-                       param_args=['--common.experiment_name', 'restnet_test'])
+    conf = common_init(config_filepath='confs/algos/resnet.yaml')
 
     conf_eval = conf['nas']['eval']