Prune deprecated hparams setter (#6207)

Author: carmocca

CHANGELOG.md

@@ -43,6 +43,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed `mode='auto'` from `EarlyStopping` ([#6167](https://github.com/PyTorchLightning/pytorch-lightning/pull/6167))
 
 
+- Removed deprecated `LightningModule` `hparams` setter ([#6207](https://github.com/PyTorchLightning/pytorch-lightning/pull/6207))
+
+
 ### Fixed
 
 - Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](https://github.com/PyTorchLightning/pytorch-lightning/pull/6011))

pytorch_lightning/core/lightning.py

@@ -17,7 +17,6 @@
 import copy
 import inspect
 import os
-import re
 import tempfile
 import uuid
 from abc import ABC
@@ -1806,39 +1805,6 @@ def hparams_initial(self) -> AttributeDict:
         # prevent any change
         return copy.deepcopy(self._hparams_initial)
 
-    @hparams.setter
-    def hparams(self, hp: Union[dict, Namespace, Any]):
-        # TODO: remove this method in v1.3.0.
-        rank_zero_warn(
-            "The setter for self.hparams in LightningModule is deprecated since v1.1.0 and will be"
-            " removed in v1.3.0. Replace the assignment `self.hparams = hparams` with "
-            " `self.save_hyperparameters()`.", DeprecationWarning
-        )
-        hparams_assignment_name = self.__get_hparams_assignment_variable()
-        self._hparams_name = hparams_assignment_name
-        self._set_hparams(hp)
-        # this resolves case when user does not uses `save_hyperparameters` and do hard assignement in init
-        if not hasattr(self, "_hparams_initial"):
-            self._hparams_initial = copy.deepcopy(self._hparams)
-
-    def __get_hparams_assignment_variable(self):
-        """
-        looks at the code of the class to figure out what the user named self.hparams
-        this only happens when the user explicitly sets self.hparams
-        """
-        try:
-            class_code = inspect.getsource(self.__class__)
-            lines = class_code.split("\n")
-            for line in lines:
-                line = re.sub(r"\s+", "", line, flags=re.UNICODE)
-                if ".hparams=" in line:
-                    return line.split("=")[1]
-        # todo: specify the possible exception
-        except Exception:
-            return "hparams"
-
-        return None
-
     @property
     def model_size(self) -> float:
         # todo: think about better way without need to dump model to drive

tests/deprecated_api/test_remove_1-3.py

@@ -41,14 +41,6 @@ def __init__(self, hparams):
         self.save_hyperparameters(hparams)
 
 
-class AssignHparamsModel(BoringModel):
-    """ Tests that a model can take an object with explicit setter """
-
-    def __init__(self, hparams):
-        super().__init__()
-        self.hparams = hparams
-
-
 def decorate(func):
 
     @functools.wraps(func)
@@ -68,16 +60,6 @@ def __init__(self, hparams, *my_args, **my_kwargs):
         self.save_hyperparameters(hparams)
 
 
-class AssignHparamsDecoratedModel(BoringModel):
-    """ Tests that a model can take an object with explicit setter"""
-
-    @decorate
-    @decorate
-    def __init__(self, hparams, *my_args, **my_kwargs):
-        super().__init__()
-        self.hparams = hparams
-
-
 # -------------------------
 # STANDARD TESTS
 # -------------------------
@@ -114,7 +96,7 @@ def _run_standard_hparams_test(tmpdir, model, cls, try_overwrite=False):
 
 
 @pytest.mark.parametrize(
-    "cls", [SaveHparamsModel, AssignHparamsModel, SaveHparamsDecoratedModel, AssignHparamsDecoratedModel]
+    "cls", [SaveHparamsModel, SaveHparamsDecoratedModel]
 )
 def test_namespace_hparams(tmpdir, cls):
     # init model
@@ -125,7 +107,7 @@ def test_namespace_hparams(tmpdir, cls):
 
 
 @pytest.mark.parametrize(
-    "cls", [SaveHparamsModel, AssignHparamsModel, SaveHparamsDecoratedModel, AssignHparamsDecoratedModel]
+    "cls", [SaveHparamsModel, SaveHparamsDecoratedModel]
 )
 def test_dict_hparams(tmpdir, cls):
     # init model
@@ -136,7 +118,7 @@ def test_dict_hparams(tmpdir, cls):
 
 
 @pytest.mark.parametrize(
-    "cls", [SaveHparamsModel, AssignHparamsModel, SaveHparamsDecoratedModel, AssignHparamsDecoratedModel]
+    "cls", [SaveHparamsModel, SaveHparamsDecoratedModel]
 )
 def test_omega_conf_hparams(tmpdir, cls):
     # init model
@@ -580,8 +562,7 @@ class SuperClassPositionalArgs(BoringModel):
 
     def __init__(self, hparams):
         super().__init__()
-        self._hparams = None  # pretend BoringModel did not call self.save_hyperparameters()
-        self.hparams = hparams
+        self._hparams = hparams  # pretend BoringModel did not call self.save_hyperparameters()
 
 
 class SubClassVarArgs(SuperClassPositionalArgs):
@@ -617,8 +598,6 @@ def test_init_arg_with_runtime_change(tmpdir, cls):
     assert model.hparams.running_arg == 123
     model.hparams.running_arg = -1
     assert model.hparams.running_arg == -1
-    model.hparams = Namespace(abc=42)
-    assert model.hparams.abc == 42
 
     trainer = Trainer(
         default_root_dir=tmpdir,
@@ -664,18 +643,11 @@ class TestHydraModel(BoringModel):
 
         def __init__(self, args_0, args_1, args_2, kwarg_1=None):
             self.save_hyperparameters()
-            self.test_hparams()
-            config_file = f"{tmpdir}/hparams.yaml"
-            save_hparams_to_yaml(config_file, self.hparams)
-            self.hparams = load_hparams_from_yaml(config_file)
-            self.test_hparams()
-            super().__init__()
-
-        def test_hparams(self):
             assert self.hparams.args_0.log == "Something"
             assert self.hparams.args_1['cfg'].log == "Something"
             assert self.hparams.args_2[0].log == "Something"
             assert self.hparams.kwarg_1['cfg'][0].log == "Something"
+            super().__init__()
 
     with initialize(config_path="conf"):
         args_0 = compose(config_name="config")

tests/models/test_hparams.py

@@ -23,6 +23,7 @@
 from pytorch_lightning.utilities import _NATIVE_AMP_AVAILABLE, AMPType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
+from tests.helpers import BoringModel
 from tests.helpers.datamodules import MNISTDataModule
 
 
@@ -282,10 +283,14 @@ def dataloader(self, *args, **kwargs):
 
 def test_auto_scale_batch_size_duplicate_attribute_warning(tmpdir):
     """ Test for a warning when model.batch_size and model.hparams.batch_size both present. """
-    hparams = EvalModelTemplate.get_default_hparams()
-    model = EvalModelTemplate(**hparams)
-    model.hparams = hparams
-    # now we have model.batch_size and model.hparams.batch_size
+    class TestModel(BoringModel):
+        def __init__(self, batch_size=1):
+            super().__init__()
+            # now we have model.batch_size and model.hparams.batch_size
+            self.batch_size = 1
+            self.save_hyperparameters()
+
+    model = TestModel()
     trainer = Trainer(default_root_dir=tmpdir, max_steps=1, max_epochs=1000, auto_scale_batch_size=True)
     expected_message = "Field `model.batch_size` and `model.hparams.batch_size` are mutually exclusive!"
     with pytest.warns(UserWarning, match=expected_message):