fixed lightning import

pytorch_lightning/core/__init__.py

@@ -152,7 +152,6 @@ Test
 # 
 # """
 
-from .test_b import TestB
 from .lightning import LightningModule
 
-__all__ = ['TestB']
+__all__ = ['LightningModule']

pytorch_lightning/core/lightning.py

@@ -4,6 +4,7 @@ import os
 import warnings
 import collections
 import logging
+import pandas as pd
 from abc import ABC, abstractmethod
 from argparse import Namespace
 
@@ -15,7 +16,6 @@ from pytorch_lightning.core.grads import GradInformation
 from pytorch_lightning.core.hooks import ModelHooks
 from pytorch_lightning.core.saving import ModelIO
 from pytorch_lightning.core.memory import ModelSummary
-from pytorch_lightning.trainer.training_io import load_hparams_from_tags_csv
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
 
 
@@ -1095,3 +1095,32 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
         """
         pass
+
+
+def load_hparams_from_tags_csv(tags_csv):
+    if not os.path.isfile(tags_csv):
+        logging.warning(f'Missing Tags: {tags_csv}.')
+        return Namespace()
+
+    tags_df = pd.read_csv(tags_csv)
+    dic = tags_df.to_dict(orient='records')
+    ns_dict = {row['key']: convert(row['value']) for row in dic}
+    ns = Namespace(**ns_dict)
+    return ns
+
+
+def convert(val):
+    constructors = [int, float, str]
+
+    if type(val) is str:
+        if val.lower() == 'true':
+            return True
+        if val.lower() == 'false':
+            return False
+
+    for c in constructors:
+        try:
+            return c(val)
+        except ValueError:
+            pass
+    return val

pytorch_lightning/trainer/training_io.py

@@ -96,9 +96,7 @@ import warnings
 from subprocess import call
 import logging
 from abc import ABC
-from argparse import Namespace
 
-import pandas as pd
 import torch
 import torch.distributed as dist
 
@@ -459,32 +457,3 @@ class TrainerIOMixin(ABC):
             ckpt_vs.append(int(name))
 
         return max(ckpt_vs)
-
-
-def load_hparams_from_tags_csv(tags_csv):
-    if not os.path.isfile(tags_csv):
-        logging.warning(f'Missing Tags: {tags_csv}.')
-        return Namespace()
-
-    tags_df = pd.read_csv(tags_csv)
-    dic = tags_df.to_dict(orient='records')
-    ns_dict = {row['key']: convert(row['value']) for row in dic}
-    ns = Namespace(**ns_dict)
-    return ns
-
-
-def convert(val):
-    constructors = [int, float, str]
-
-    if type(val) is str:
-        if val.lower() == 'true':
-            return True
-        if val.lower() == 'false':
-            return False
-
-    for c in constructors:
-        try:
-            return c(val)
-        except ValueError:
-            pass
-    return val

tests/test_trainer.py

@@ -15,7 +15,7 @@ from pytorch_lightning.testing import (
     LightningValidationMultipleDataloadersMixin,
     LightningTestMultipleDataloadersMixin,
 )
-from pytorch_lightning.trainer import training_io
+from pytorch_lightning.core.lightning import load_hparams_from_tags_csv 
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 
 
@@ -186,7 +186,7 @@ def test_loading_meta_tags(tmpdir):
     # load tags
     path_expt_dir = tutils.get_data_path(logger, path_dir=tmpdir)
     tags_path = os.path.join(path_expt_dir, 'meta_tags.csv')
-    tags = training_io.load_hparams_from_tags_csv(tags_path)
+    tags = load_hparams_from_tags_csv(tags_path)
 
     assert tags.batch_size == 32 and tags.hidden_dim == 1000