test: save hparams to yaml (#2198)

* save hparams to yaml

* import

* resolves

* req

* Update requirements/base.txt

Co-authored-by: Omry Yadan <omry@fb.com>

Co-authored-by: Omry Yadan <omry@fb.com>

pytorch_lightning/core/saving.py

@@ -10,14 +10,14 @@ from typing import Union, Dict, Any, Optional, Callable
 
 from pytorch_lightning import _logger as log
 from pytorch_lightning.utilities import rank_zero_warn, AttributeDict
-from pytorch_lightning.utilities.io import load as pl_load
+from pytorch_lightning.utilities.cloud_io import load as pl_load
 
 PRIMITIVE_TYPES = (bool, int, float, str)
 ALLOWED_CONFIG_TYPES = (AttributeDict, dict, Namespace)
 try:
     from omegaconf import Container
 except ImportError:
-    pass
+    Container = None
 else:
     ALLOWED_CONFIG_TYPES = ALLOWED_CONFIG_TYPES + (Container, )
 
@@ -332,11 +332,25 @@ def load_hparams_from_yaml(config_yaml: str) -> Dict[str, Any]:
 
 
 def save_hparams_to_yaml(config_yaml, hparams: Union[dict, Namespace]) -> None:
+    """
+    Args:
+        config_yaml: path to new YAML file
+        hparams: parameters to be saved
+    """
     if not os.path.isdir(os.path.dirname(config_yaml)):
         raise RuntimeError(f'Missing folder: {os.path.dirname(config_yaml)}.')
 
+    if Container is not None and isinstance(hparams, Container):
+        from omegaconf import OmegaConf
+        OmegaConf.save(hparams, config_yaml, resolve=True)
+        return
+
+    # saving the standard way
     if isinstance(hparams, Namespace):
         hparams = vars(hparams)
+    elif isinstance(hparams, AttributeDict):
+        hparams = dict(hparams)
+    assert isinstance(hparams, dict)
 
     with open(config_yaml, 'w', newline='') as fp:
         yaml.dump(hparams, fp)

pytorch_lightning/loggers/tensorboard.py

@@ -17,11 +17,6 @@ from pytorch_lightning.core.saving import save_hparams_to_yaml
 from pytorch_lightning.loggers.base import LightningLoggerBase
 from pytorch_lightning.utilities import rank_zero_only
 
-try:
-    from omegaconf import Container
-except ImportError:
-    Container = None
-
 
 class TensorBoardLogger(LightningLoggerBase):
     r"""
@@ -156,14 +151,7 @@ class TensorBoardLogger(LightningLoggerBase):
         hparams_file = os.path.join(dir_path, self.NAME_HPARAMS_FILE)
 
         # save the metatags file
-        if Container is not None:
-            if isinstance(self.hparams, Container):
-                from omegaconf import OmegaConf
-                OmegaConf.save(self.hparams, hparams_file, resolve=True)
-            else:
-                save_hparams_to_yaml(hparams_file, self.hparams)
-        else:
-            save_hparams_to_yaml(hparams_file, self.hparams)
+        save_hparams_to_yaml(hparams_file, self.hparams)
 
     @rank_zero_only
     def finalize(self, status: str) -> None:

pytorch_lightning/trainer/training_io.py

@@ -101,7 +101,7 @@ from pytorch_lightning.overrides.data_parallel import (
     LightningDataParallel,
 )
 from pytorch_lightning.utilities import rank_zero_warn
-from pytorch_lightning.utilities.io import load as pl_load
+from pytorch_lightning.utilities.cloud_io import load as pl_load
 
 try:
     import torch_xla

requirements/base.txt

@@ -5,4 +5,5 @@ tqdm>=4.41.0
 torch>=1.3
 tensorboard>=1.14
 future>=0.17.1  # required for builtins in setup.py
-pyyaml>=3.13
+# pyyaml>=3.13
+PyYAML>=5.1  # OmegaConf requirement

tests/models/test_hparams.py

@@ -8,6 +8,7 @@ import torch
 from omegaconf import OmegaConf, Container
 
 from pytorch_lightning import Trainer, LightningModule
+from pytorch_lightning.core.saving import save_hparams_to_yaml, load_hparams_from_yaml
 from pytorch_lightning.utilities import AttributeDict
 from tests.base import EvalModelTemplate
 
@@ -407,3 +408,21 @@ def test_hparams_pickle(tmpdir):
     assert ad == pickle.loads(pkl)
     pkl = cloudpickle.dumps(ad)
     assert ad == pickle.loads(pkl)
+
+
+def test_hparams_save_yaml(tmpdir):
+    hparams = dict(batch_size=32, learning_rate=0.001, data_root='./any/path/here',
+                   nasted=dict(any_num=123, anystr='abcd'))
+    path_yaml = os.path.join(tmpdir, 'testing-hparams.yaml')
+
+    save_hparams_to_yaml(path_yaml, hparams)
+    assert load_hparams_from_yaml(path_yaml) == hparams
+
+    save_hparams_to_yaml(path_yaml, Namespace(**hparams))
+    assert load_hparams_from_yaml(path_yaml) == hparams
+
+    save_hparams_to_yaml(path_yaml, AttributeDict(hparams))
+    assert load_hparams_from_yaml(path_yaml) == hparams
+
+    save_hparams_to_yaml(path_yaml, OmegaConf.create(hparams))
+    assert load_hparams_from_yaml(path_yaml) == hparams

tests/trainer/test_trainer.py

@@ -19,7 +19,7 @@ from pytorch_lightning.core.saving import (
 from pytorch_lightning.loggers import TensorBoardLogger
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.io import load as pl_load
+from pytorch_lightning.utilities.cloud_io import load as pl_load
 from tests.base import EvalModelTemplate