hparams as dict [blocked by 1041] (Lightning-AI#1029)

* hparams as dict

* hparams as dict

* fixing

* fixing

* fixing

* fixing

* typing

* typing

* chnagelog

* update set hparams

* use setter

* simplify

* chnagelog

* imports

* pylint

* typing

* Update training_io.py

* Update training_io.py

* Update lightning.py

* Update test_trainer.py

* Update __init__.py

* Update base.py

* Update utils.py

* Update test_trainer.py

* Update training_io.py

* Update test_trainer.py

* Update test_trainer.py

* Update test_trainer.py

* Update test_trainer.py

* Update callback_config.py

* Update callback_config.py

* Update test_trainer.py

Co-authored-by: William Falcon <waf2107@columbia.edu>

Author: Borda

CHANGELOG.md

@@ -24,6 +24,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Split callbacks in multiple files ([#849](https://github.com/PyTorchLightning/pytorch-lightning/pull/849))
 - Support for user defined callbacks ([#889](https://github.com/PyTorchLightning/pytorch-lightning/pull/889) and [#950](https://github.com/PyTorchLightning/pytorch-lightning/pull/950))
 - Added support for multiple loggers to be passed to `Trainer` as an iterable (e.g. list, tuple, etc.) ([#903](https://github.com/PyTorchLightning/pytorch-lightning/pull/903))
+- Added support for logging hparams as dict ([#1029](https://github.com/PyTorchLightning/pytorch-lightning/pull/1029))
 
 ### Changed
 
@@ -32,6 +33,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed `pytorch_lightning.logging` to `pytorch_lightning.loggers` ([#767](https://github.com/PyTorchLightning/pytorch-lightning/pull/767))
 - Moved the default `tqdm_dict` definition from Trainer to `LightningModule`, so it can be overridden by the user ([#749](https://github.com/PyTorchLightning/pytorch-lightning/pull/749))
 - Moved functionality of `LightningModule.load_from_metrics` into `LightningModule.load_from_checkpoint` ([#995](https://github.com/PyTorchLightning/pytorch-lightning/pull/995))
+- Changed Checkpoint path parameter from `filepath` to `dirpath` ([#1016](https://github.com/PyTorchLightning/pytorch-lightning/pull/1016))
+- Freezed models `hparams` as `Namespace` property ([#1029](https://github.com/PyTorchLightning/pytorch-lightning/pull/1029))
 
 ### Deprecated
 

docs/source/weights_loading.rst

@@ -62,7 +62,7 @@ The Lightning checkpoint also saves the hparams (hyperparams) passed into the Li
    from argparse import Namespace
 
    # usually these come from command line args
-   args = Namespace(**{'learning_rate':0.001})
+   args = Namespace(learning_rate=0.001)
 
    # define you module to have hparams as the first arg
    # this means your checkpoint will have everything that went into making

pytorch_lightning/callbacks/model_checkpoint.py

@@ -27,9 +27,11 @@ class ModelCheckpoint(Callback):
 
                 # save epoch and val_loss in name
                 ModelCheckpoint(filepath='{epoch:02d}-{val_loss:.2f}.hdf5')
+
                 # saves file like: /my/path/here/sample-mnist_epoch=02_val_loss=0.32.ckpt
                 # if model already exits, the file will be: /my/path/here/sample-mnist-v0_epoch=02_val_loss=0.32.ckpt
 
+
         monitor: quantity to monitor.
         verbose: verbosity mode, False or True.
         save_top_k: if `save_top_k == k`,
@@ -135,7 +137,7 @@ def _save_model(self, filepath: str) -> None:
         if self.save_function is not None:
             self.save_function(filepath)
         else:
-            raise ValueError(".save_function() not set")
+            raise ValueError("Method `.save_function()` not set")
 
     def check_monitor_top_k(self, current: float) -> bool:
         less_than_k_models = len(self.best_k_models) < self.save_top_k

pytorch_lightning/core/lightning.py

@@ -5,7 +5,7 @@
 import warnings
 from abc import ABC, abstractmethod
 from argparse import Namespace
-from typing import Optional, Union, Dict, Callable
+from typing import Any, Callable, Dict, Optional, Union
 
 import torch
 import torch.distributed as dist
@@ -68,6 +68,20 @@ def __init__(self, *args, **kwargs):
         #: True if using amp
         self.use_amp = False
 
+    @property
+    def hparams(self) -> Namespace:
+        if not hasattr(self, '_hparams'):
+            return Namespace()
+        assert isinstance(self._hparams, dict)
+        return Namespace(**self._hparams)
+
+    @hparams.setter
+    def hparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
+        """Set the model hyper-parameters."""
+        if isinstance(params, Namespace):
+            params = vars(params)
+        self._hparams = params
+
     def print(self, *args, **kwargs):
         r"""
         Prints only from process 0. Use this in any distributed mode to log only once
@@ -1201,7 +1215,8 @@ def _load_model_state(cls, checkpoint):
 
         if cls_takes_hparams:
             if ckpt_hparams is not None:
-                hparams = Namespace(**ckpt_hparams)
+                is_namespace = checkpoint.get('hparams_type') == 'namespace'
+                hparams = Namespace(**ckpt_hparams) if is_namespace else ckpt_hparams
             else:
                 warnings.warn(
                     f"Checkpoint does not contain hyperparameters but {cls.__name__}'s __init__ contains"

pytorch_lightning/core/saving.py

@@ -36,16 +36,14 @@ def on_hpc_load(self, checkpoint):
         """
 
 
-def load_hparams_from_tags_csv(tags_csv):
+def load_hparams_from_tags_csv(tags_csv) -> Namespace:
     if not os.path.isfile(tags_csv):
         log.warning(f'Missing Tags: {tags_csv}.')
         return Namespace()
 
-    tags = {}
     with open(tags_csv) as f:
         csv_reader = csv.reader(f, delimiter=',')
-        for row in list(csv_reader)[1:]:
-            tags[row[0]] = convert(row[1])
+        tags = {row[0]: convert(row[1]) for row in list(csv_reader)[1:]}
     ns = Namespace(**tags)
     return ns
 

pytorch_lightning/loggers/base.py

@@ -1,5 +1,6 @@
 import argparse
 from abc import ABC, abstractmethod
+from argparse import Namespace
 from functools import wraps
 from typing import Union, Optional, Dict, Iterable, Any, Callable, List
 
@@ -41,6 +42,12 @@ def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
         """
         pass
 
+    def _convert_params(self, params: Union[Dict[str, Any], Namespace]) -> Dict[str, Any]:
+        # in case converting from namespace
+        if isinstance(params, Namespace):
+            params = vars(params)
+        return params
+
     @abstractmethod
     def log_hyperparams(self, params: argparse.Namespace):
         """Record hyperparameters.
@@ -50,19 +57,19 @@ def log_hyperparams(self, params: argparse.Namespace):
         """
         pass
 
-    def save(self):
+    def save(self) -> None:
         """Save log data."""
         pass
 
-    def finalize(self, status: str):
+    def finalize(self, status: str) -> None:
         """Do any processing that is necessary to finalize an experiment.
 
         Args:
             status: Status that the experiment finished with (e.g. success, failed, aborted)
         """
         pass
 
-    def close(self):
+    def close(self) -> None:
         """Do any cleanup that is necessary to close an experiment."""
         pass
 
@@ -72,7 +79,7 @@ def rank(self) -> int:
         return self._rank
 
     @rank.setter
-    def rank(self, value: int):
+    def rank(self, value: int) -> None:
         """Set the process rank."""
         self._rank = value
 
@@ -107,23 +114,23 @@ def __getitem__(self, index: int) -> LightningLoggerBase:
     def experiment(self) -> List[Any]:
         return [logger.experiment for logger in self._logger_iterable]
 
-    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
+    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
         [logger.log_metrics(metrics, step) for logger in self._logger_iterable]
 
-    def log_hyperparams(self, params: argparse.Namespace):
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
         [logger.log_hyperparams(params) for logger in self._logger_iterable]
 
-    def save(self):
+    def save(self) -> None:
         [logger.save() for logger in self._logger_iterable]
 
-    def finalize(self, status: str):
+    def finalize(self, status: str) -> None:
         [logger.finalize(status) for logger in self._logger_iterable]
 
-    def close(self):
+    def close(self) -> None:
         [logger.close() for logger in self._logger_iterable]
 
     @LightningLoggerBase.rank.setter
-    def rank(self, value: int):
+    def rank(self, value: int) -> None:
         self._rank = value
         for logger in self._logger_iterable:
             logger.rank = value

pytorch_lightning/loggers/comet.py

@@ -5,9 +5,9 @@
 CometLogger
 -------------
 """
-import argparse
+from argparse import Namespace
 from logging import getLogger
-from typing import Optional, Dict, Union
+from typing import Optional, Dict, Union, Any
 
 try:
     from comet_ml import Experiment as CometExperiment
@@ -162,15 +162,16 @@ def experiment(self) -> CometBaseExperiment:
         return self._experiment
 
     @rank_zero_only
-    def log_hyperparams(self, params: argparse.Namespace):
-        self.experiment.log_parameters(vars(params))
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
+        params = self._convert_params(params)
+        self.experiment.log_parameters(params)
 
     @rank_zero_only
     def log_metrics(
             self,
             metrics: Dict[str, Union[torch.Tensor, float]],
             step: Optional[int] = None
-    ):
+    ) -> None:
         # Comet.ml expects metrics to be a dictionary of detached tensors on CPU
         for key, val in metrics.items():
             if is_tensor(val):
@@ -182,7 +183,7 @@ def reset_experiment(self):
         self._experiment = None
 
     @rank_zero_only
-    def finalize(self, status: str):
+    def finalize(self, status: str) -> None:
         r"""
         When calling self.experiment.end(), that experiment won't log any more data to Comet. That's why, if you need
         to log any more data you need to create an ExistingCometExperiment. For example, to log data when testing your
@@ -199,7 +200,7 @@ def name(self) -> str:
         return self.experiment.project_name
 
     @name.setter
-    def name(self, value: str):
+    def name(self, value: str) -> None:
         self.experiment.set_name(value)
 
     @property

pytorch_lightning/loggers/mlflow.py

@@ -23,10 +23,10 @@ def any_lightning_module_function_or_hook(...):
         self.logger.experiment.whatever_ml_flow_supports(...)
 
 """
-import argparse
+from argparse import Namespace
 from logging import getLogger
 from time import time
-from typing import Optional, Dict, Any
+from typing import Optional, Dict, Any, Union
 
 try:
     import mlflow
@@ -88,12 +88,13 @@ def run_id(self):
         return self._run_id
 
     @rank_zero_only
-    def log_hyperparams(self, params: argparse.Namespace):
-        for k, v in vars(params).items():
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
+        params = self._convert_params(params)
+        for k, v in params.items():
             self.experiment.log_param(self.run_id, k, v)
 
     @rank_zero_only
-    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
+    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
         timestamp_ms = int(time() * 1000)
         for k, v in metrics.items():
             if isinstance(v, str):
@@ -105,7 +106,7 @@ def save(self):
         pass
 
     @rank_zero_only
-    def finalize(self, status: str = 'FINISHED'):
+    def finalize(self, status: str = 'FINISHED') -> None:
         if status == 'success':
             status = 'FINISHED'
         self.experiment.set_terminated(self.run_id, status)

pytorch_lightning/loggers/neptune.py

@@ -6,7 +6,7 @@
 NeptuneLogger
 --------------
 """
-import argparse
+from argparse import Namespace
 from logging import getLogger
 from typing import Optional, List, Dict, Any, Union, Iterable
 
@@ -164,16 +164,17 @@ def experiment(self) -> Experiment:
         return self._experiment
 
     @rank_zero_only
-    def log_hyperparams(self, params: argparse.Namespace):
-        for key, val in vars(params).items():
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
+        params = self._convert_params(params)
+        for key, val in params.items():
             self.experiment.set_property(f'param__{key}', val)
 
     @rank_zero_only
     def log_metrics(
             self,
             metrics: Dict[str, Union[torch.Tensor, float]],
             step: Optional[int] = None
-    ):
+    ) -> None:
         """Log metrics (numeric values) in Neptune experiments
 
         Args:
@@ -184,7 +185,7 @@ def log_metrics(
             self.log_metric(key, val, step=step)
 
     @rank_zero_only
-    def finalize(self, status: str):
+    def finalize(self, status: str) -> None:
         self.experiment.stop()
 
     @property
@@ -207,7 +208,7 @@ def log_metric(
             metric_name: str,
             metric_value: Union[torch.Tensor, float, str],
             step: Optional[int] = None
-    ):
+    ) -> None:
         """Log metrics (numeric values) in Neptune experiments
 
         Args:
@@ -224,7 +225,7 @@ def log_metric(
             self.experiment.log_metric(metric_name, x=step, y=metric_value)
 
     @rank_zero_only
-    def log_text(self, log_name: str, text: str, step: Optional[int] = None):
+    def log_text(self, log_name: str, text: str, step: Optional[int] = None) -> None:
         """Log text data in Neptune experiment
 
         Args:
@@ -235,7 +236,7 @@ def log_text(self, log_name: str, text: str, step: Optional[int] = None):
         self.log_metric(log_name, text, step=step)
 
     @rank_zero_only
-    def log_image(self, log_name: str, image: Union[str, Any], step: Optional[int] = None):
+    def log_image(self, log_name: str, image: Union[str, Any], step: Optional[int] = None) -> None:
         """Log image data in Neptune experiment
 
         Args:
@@ -250,7 +251,7 @@ def log_image(self, log_name: str, image: Union[str, Any], step: Optional[int] =
             self.experiment.log_image(log_name, x=step, y=image)
 
     @rank_zero_only
-    def log_artifact(self, artifact: str, destination: Optional[str] = None):
+    def log_artifact(self, artifact: str, destination: Optional[str] = None) -> None:
         """Save an artifact (file) in Neptune experiment storage.
 
         Args:
@@ -261,7 +262,7 @@ def log_artifact(self, artifact: str, destination: Optional[str] = None):
         self.experiment.log_artifact(artifact, destination)
 
     @rank_zero_only
-    def set_property(self, key: str, value: Any):
+    def set_property(self, key: str, value: Any) -> None:
         """Set key-value pair as Neptune experiment property.
 
         Args:
@@ -271,7 +272,7 @@ def set_property(self, key: str, value: Any):
         self.experiment.set_property(key, value)
 
     @rank_zero_only
-    def append_tags(self, tags: Union[str, Iterable[str]]):
+    def append_tags(self, tags: Union[str, Iterable[str]]) -> None:
         """appends tags to neptune experiment
 
         Args: