hparams as dict [blocked by 1041] (#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>

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 @@ class ModelCheckpoint(Callback):
         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 os
 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 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
         #: 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 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
         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 @@ class ModelIO(object):
         """
 
 
-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]) for row in list(csv_reader)[1:]}
-            tags[row[0]] = convert(row[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 @@ class LightningLoggerBase(ABC):
         """
         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.
@@ -50,11 +57,11 @@ class LightningLoggerBase(ABC):
         """
         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:
@@ -62,7 +69,7 @@ class LightningLoggerBase(ABC):
         """
         pass
 
-    def close(self):
+    def close(self) -> None:
         """Do any cleanup that is necessary to close an experiment."""
         pass
 
@@ -72,7 +79,7 @@ class LightningLoggerBase(ABC):
         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 @@ class LoggerCollection(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 @@ r"""
 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 @@ class CometLogger(LightningLoggerBase):
         return self._experiment
 
     @rank_zero_only
-    def log_hyperparams(self, params: argparse.Namespace):
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
-        self.experiment.log_parameters(vars(params))
+        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 @@ class CometLogger(LightningLoggerBase):
         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 @@ class CometLogger(LightningLoggerBase):
         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 @@ Use the logger anywhere in you LightningModule as follows:
         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 @@ class MLFlowLogger(LightningLoggerBase):
         return self._run_id
 
     @rank_zero_only
-    def log_hyperparams(self, params: argparse.Namespace):
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
-        for k, v in vars(params).items():
+        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 @@ class MLFlowLogger(LightningLoggerBase):
         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 @@ Log using `neptune-logger <https://www.neptune.ml>`_
 NeptuneLogger
 --------------
 """
-import argparse
+from argparse import Namespace
 from logging import getLogger
 from typing import Optional, List, Dict, Any, Union, Iterable
 
@@ -164,8 +164,9 @@ class NeptuneLogger(LightningLoggerBase):
         return self._experiment
 
     @rank_zero_only
-    def log_hyperparams(self, params: argparse.Namespace):
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
-        for key, val in vars(params).items():
+        params = self._convert_params(params)
+        for key, val in params.items():
             self.experiment.set_property(f'param__{key}', val)
 
     @rank_zero_only
@@ -173,7 +174,7 @@ class NeptuneLogger(LightningLoggerBase):
             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 @@ class NeptuneLogger(LightningLoggerBase):
             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 @@ class NeptuneLogger(LightningLoggerBase):
             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 @@ class NeptuneLogger(LightningLoggerBase):
             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 @@ class NeptuneLogger(LightningLoggerBase):
         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 @@ class NeptuneLogger(LightningLoggerBase):
             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 @@ class NeptuneLogger(LightningLoggerBase):
         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 @@ class NeptuneLogger(LightningLoggerBase):
         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:

pytorch_lightning/loggers/tensorboard.py

@@ -1,8 +1,7 @@
-import argparse
 import csv
 import os
 from argparse import Namespace
-from typing import Optional, Dict, Union
+from typing import Optional, Dict, Union, Any
 from warnings import warn
 
 import torch
@@ -100,14 +99,8 @@ class TensorBoardLogger(LightningLoggerBase):
         return self._experiment
 
     @rank_zero_only
-    def log_hyperparams(self, params: argparse.Namespace):
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
-        if params is None:
+        params = self._convert_params(params)
-            return
-
-        # in case converting from namespace
-        if isinstance(params, Namespace):
-            params = vars(params)
-        params = dict(params)
 
         if parse_version(torch.__version__) < parse_version("1.3.0"):
             warn(
@@ -126,14 +119,14 @@ class TensorBoardLogger(LightningLoggerBase):
         self.tags.update(params)
 
     @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:
         for k, v in metrics.items():
             if isinstance(v, torch.Tensor):
                 v = v.item()
             self.experiment.add_scalar(k, v, step)
 
     @rank_zero_only
-    def save(self):
+    def save(self) -> None:
         try:
             self.experiment.flush()
         except AttributeError:
@@ -156,7 +149,7 @@ class TensorBoardLogger(LightningLoggerBase):
                 writer.writerow({'key': k, 'value': v})
 
     @rank_zero_only
-    def finalize(self, status: str):
+    def finalize(self, status: str) -> None:
         self.save()
 
     @property

pytorch_lightning/loggers/test_tube.py

@@ -1,5 +1,5 @@
-import argparse
+from argparse import Namespace
-from typing import Optional, Dict, Any
+from typing import Optional, Dict, Any, Union
 
 try:
     from test_tube import Experiment
@@ -92,32 +92,33 @@ class TestTubeLogger(LightningLoggerBase):
         return self._experiment
 
     @rank_zero_only
-    def log_hyperparams(self, params: argparse.Namespace):
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
         # TODO: HACK figure out where this is being set to true
         self.experiment.debug = self.debug
-        self.experiment.argparse(params)
+        params = self._convert_params(params)
+        self.experiment.argparse(Namespace(**params))
 
     @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:
         # TODO: HACK figure out where this is being set to true
         self.experiment.debug = self.debug
         self.experiment.log(metrics, global_step=step)
 
     @rank_zero_only
-    def save(self):
+    def save(self) -> None:
         # TODO: HACK figure out where this is being set to true
         self.experiment.debug = self.debug
         self.experiment.save()
 
     @rank_zero_only
-    def finalize(self, status: str):
+    def finalize(self, status: str) -> None:
         # TODO: HACK figure out where this is being set to true
         self.experiment.debug = self.debug
         self.save()
         self.close()
 
     @rank_zero_only
-    def close(self):
+    def close(self) -> None:
         # TODO: HACK figure out where this is being set to true
         self.experiment.debug = self.debug
         if not self.debug:
@@ -129,7 +130,7 @@ class TestTubeLogger(LightningLoggerBase):
         return self._rank
 
     @rank.setter
-    def rank(self, value: int):
+    def rank(self, value: int) -> None:
         self._rank = value
         if self._experiment is not None:
             self.experiment.rank = value

pytorch_lightning/loggers/wandb.py

@@ -5,9 +5,9 @@ r"""
 WandbLogger
 -------------
 """
-import argparse
 import os
-from typing import Optional, List, Dict
+from argparse import Namespace
+from typing import Optional, List, Dict, Union, Any
 
 import torch.nn as nn
 
@@ -91,17 +91,18 @@ class WandbLogger(LightningLoggerBase):
         wandb.watch(model, log=log, log_freq=log_freq)
 
     @rank_zero_only
-    def log_hyperparams(self, params: argparse.Namespace):
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
+        params = self._convert_params(params)
         self.experiment.config.update(params)
 
     @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:
         if step is not None:
             metrics['global_step'] = step
         self.experiment.log(metrics)
 
     @rank_zero_only
-    def finalize(self, status: str = 'success'):
+    def finalize(self, status: str = 'success') -> None:
         try:
             exit_code = 0 if status == 'success' else 1
             wandb.join(exit_code)

pytorch_lightning/trainer/training_io.py

@@ -4,9 +4,9 @@ import re
 import signal
 import warnings
 from abc import ABC
+from argparse import Namespace
 from subprocess import call
 from typing import Union
-from copy import deepcopy
 
 import torch
 import torch.distributed as dist
@@ -238,7 +238,9 @@ class TrainerIOMixin(ABC):
         checkpoint['state_dict'] = model.state_dict()
 
         if hasattr(model, "hparams"):
-            checkpoint['hparams'] = vars(model.hparams)
+            is_namespace = isinstance(model.hparams, Namespace)
+            checkpoint['hparams'] = vars(model.hparams) if is_namespace else model.hparams
+            checkpoint['hparams_type'] = 'namespace' if is_namespace else 'dict'
         else:
             warnings.warn(
                 "Did not find hyperparameters at model.hparams. Saving checkpoint without"
@@ -322,7 +324,7 @@ class TrainerIOMixin(ABC):
     # ----------------------------------
     # PRIVATE OPS
     # ----------------------------------
-    def hpc_save(self, folderpath, logger):
+    def hpc_save(self, folderpath: str, logger):
         # make sure the checkpoint folder exists
         os.makedirs(folderpath, exist_ok=True)
 
@@ -333,7 +335,7 @@ class TrainerIOMixin(ABC):
 
         if not os.path.exists(folderpath):
             os.makedirs(folderpath, exist_ok=True)
-        filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, ckpt_number)
+        filepath = os.path.join(folderpath, f'hpc_ckpt_{ckpt_number}.ckpt')
 
         # give model a chance to do something on hpc_save
         model = self.get_model()

tests/loggers/test_wandb.py

@@ -24,8 +24,8 @@ def test_wandb_logger(wandb):
     logger.log_metrics({'acc': 1.0}, step=3)
     wandb.init().log.assert_called_once_with({'global_step': 3, 'acc': 1.0})
 
-    logger.log_hyperparams('test')
+    logger.log_hyperparams({'test': None})
-    wandb.init().config.update.assert_called_once_with('test')
+    wandb.init().config.update.assert_called_once_with({'test': None})
 
     logger.watch('model', 'log', 10)
     wandb.watch.assert_called_once_with('model', log='log', log_freq=10)

tests/models/__init__.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from .base import TestModelBase
+from .base import TestModelBase, DictHparamsModel
 from .mixins import (
     LightEmptyTestStep,
     LightValidationStepMixin,

tests/models/base.py

@@ -6,9 +6,9 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch import optim
 from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
 from torchvision import transforms
 from torchvision.datasets import MNIST
+from typing import Dict
 
 try:
     from test_tube import HyperOptArgumentParser
@@ -36,6 +36,28 @@ class TestingMNIST(MNIST):
         self.targets = self.targets[:num_samples]
 
 
+class DictHparamsModel(LightningModule):
+
+    def __init__(self, hparams: Dict):
+        super(DictHparamsModel, self).__init__()
+        self.l1 = torch.nn.Linear(hparams.get('in_features'), hparams['out_features'])
+
+    def forward(self, x):
+        return torch.relu(self.l1(x.view(x.size(0), -1)))
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        y_hat = self.forward(x)
+        return {'loss': F.cross_entropy(y_hat, y)}
+
+    def configure_optimizers(self):
+        return torch.optim.Adam(self.parameters(), lr=0.02)
+
+    def train_dataloader(self):
+        return DataLoader(MNIST(os.getcwd(), train=True, download=True,
+                                transform=transforms.ToTensor()), batch_size=32)
+
+
 class TestModelBase(LightningModule):
     """
     Base LightningModule for testing. Implements only the required

tests/models/utils.py

@@ -168,6 +168,20 @@ def load_model(exp, root_weights_dir, module_class=LightningTemplateModel, path_
     return trained_model
 
 
+def load_model_from_checkpoint(root_weights_dir, module_class=LightningTemplateModel):
+    # load trained model
+    checkpoints = [x for x in os.listdir(root_weights_dir) if '.ckpt' in x]
+    weights_dir = os.path.join(root_weights_dir, checkpoints[0])
+
+    trained_model = module_class.load_from_checkpoint(
+        checkpoint_path=weights_dir,
+    )
+
+    assert trained_model is not None, 'loading model failed'
+
+    return trained_model
+
+
 def run_prediction(dataloader, trained_model, dp=False, min_acc=0.45):
     # run prediction on 1 batch
     for batch in dataloader:

tests/trainer/test_trainer.py

@@ -3,30 +3,28 @@ import math
 import os
 import pytest
 import torch
-import argparse
+from argparse import ArgumentParser, Namespace
 
 import tests.models.utils as tutils
 from unittest import mock
-from pytorch_lightning import Trainer
+from pytorch_lightning import Trainer, LightningModule
 from pytorch_lightning.callbacks import (
     EarlyStopping,
     ModelCheckpoint,
 )
 from tests.models import (
     TestModelBase,
+    DictHparamsModel,
     LightningTestModel,
     LightEmptyTestStep,
     LightValidationStepMixin,
     LightValidationMultipleDataloadersMixin,
     LightTrainDataloader,
     LightTestDataloader,
-    LightValidationMixin,
-    LightTestMixin
 )
 from pytorch_lightning.core.lightning import load_hparams_from_tags_csv
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 from pytorch_lightning.utilities.debugging import MisconfigurationException
-from pytorch_lightning import Callback
 
 
 def test_no_val_module(tmpdir):
@@ -128,7 +126,7 @@ def test_gradient_accumulation_scheduling(tmpdir):
         assert Trainer(accumulate_grad_batches={1: 2.5, 3: 5})
 
     # test optimizer call freq matches scheduler
-    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
+    def _optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
         # only test the first 12 batches in epoch
         if batch_idx < 12:
             if epoch == 0:
@@ -179,7 +177,7 @@ def test_gradient_accumulation_scheduling(tmpdir):
                       default_save_path=tmpdir)
 
     # for the test
-    trainer.optimizer_step = optimizer_step
+    trainer.optimizer_step = _optimizer_step
     model.prev_called_batch_idx = 0
 
     trainer.fit(model)
@@ -188,7 +186,6 @@ def test_gradient_accumulation_scheduling(tmpdir):
 def test_loading_meta_tags(tmpdir):
     tutils.reset_seed()
 
-    from argparse import Namespace
     hparams = tutils.get_hparams()
 
     # save tags
@@ -604,8 +601,9 @@ def test_testpass_overrides(tmpdir):
     model = LightningTestModel(hparams)
     Trainer().test(model)
 
+
 @mock.patch('argparse.ArgumentParser.parse_args',
-            return_value=argparse.Namespace(**Trainer.default_attributes()))
+            return_value=Namespace(**Trainer.default_attributes()))
 def test_default_args(tmpdir):
     """Tests default argument parser for Trainer"""
     tutils.reset_seed()
@@ -613,7 +611,7 @@ def test_default_args(tmpdir):
     # logger file to get meta
     logger = tutils.get_test_tube_logger(tmpdir, False)
 
-    parser = argparse.ArgumentParser(add_help=False)
+    parser = ArgumentParser(add_help=False)
     args = parser.parse_args()
     args.logger = logger
 
@@ -622,3 +620,25 @@ def test_default_args(tmpdir):
 
     assert isinstance(trainer, Trainer)
     assert trainer.max_epochs == 5
+
+
+def test_hparams_save_load(tmpdir):
+    model = DictHparamsModel({'in_features': 28 * 28, 'out_features': 10})
+
+    # logger file to get meta
+    trainer_options = dict(
+        default_save_path=tmpdir,
+        max_epochs=2,
+    )
+
+    # fit model
+    trainer = Trainer(**trainer_options)
+    result = trainer.fit(model)
+
+    assert result == 1
+
+    # try to load the model now
+    pretrained_model = tutils.load_model_from_checkpoint(
+        trainer.checkpoint_callback.dirpath,
+        module_class=DictHparamsModel
+    )