Prune deprecated hparams setter (#6207)
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Carlos Mocholí
GitHub
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@ -43,6 +43,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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- Removed `mode='auto'` from `EarlyStopping` ([#6167](https://github.com/PyTorchLightning/pytorch-lightning/pull/6167))
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- Removed deprecated `LightningModule` `hparams` setter ([#6207](https://github.com/PyTorchLightning/pytorch-lightning/pull/6207))
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### Fixed
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- 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))
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@ -17,7 +17,6 @@ import collections
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import copy
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import inspect
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import os
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import re
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import tempfile
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import uuid
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from abc import ABC
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@ -1806,39 +1805,6 @@ class LightningModule(
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# prevent any change
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return copy.deepcopy(self._hparams_initial)
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@hparams.setter
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def hparams(self, hp: Union[dict, Namespace, Any]):
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# TODO: remove this method in v1.3.0.
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rank_zero_warn(
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"The setter for self.hparams in LightningModule is deprecated since v1.1.0 and will be"
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" removed in v1.3.0. Replace the assignment `self.hparams = hparams` with "
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" `self.save_hyperparameters()`.", DeprecationWarning
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)
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hparams_assignment_name = self.__get_hparams_assignment_variable()
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self._hparams_name = hparams_assignment_name
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self._set_hparams(hp)
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# this resolves case when user does not uses `save_hyperparameters` and do hard assignement in init
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if not hasattr(self, "_hparams_initial"):
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self._hparams_initial = copy.deepcopy(self._hparams)
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def __get_hparams_assignment_variable(self):
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"""
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looks at the code of the class to figure out what the user named self.hparams
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this only happens when the user explicitly sets self.hparams
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"""
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try:
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class_code = inspect.getsource(self.__class__)
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lines = class_code.split("\n")
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for line in lines:
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line = re.sub(r"\s+", "", line, flags=re.UNICODE)
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if ".hparams=" in line:
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return line.split("=")[1]
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# todo: specify the possible exception
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except Exception:
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return "hparams"
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return None
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@property
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def model_size(self) -> float:
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# todo: think about better way without need to dump model to drive
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@ -1,30 +0,0 @@
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# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Test deprecated functionality which will be removed in vX.Y.Z"""
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import pytest
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from pytorch_lightning import LightningModule
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def test_v1_3_0_deprecated_arguments(tmpdir):
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with pytest.deprecated_call(match="The setter for self.hparams in LightningModule is deprecated"):
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class DeprecatedHparamsModel(LightningModule):
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def __init__(self, hparams):
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super().__init__()
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self.hparams = hparams
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DeprecatedHparamsModel({})
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@ -41,14 +41,6 @@ class SaveHparamsModel(BoringModel):
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self.save_hyperparameters(hparams)
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class AssignHparamsModel(BoringModel):
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""" Tests that a model can take an object with explicit setter """
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def __init__(self, hparams):
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super().__init__()
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self.hparams = hparams
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def decorate(func):
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@functools.wraps(func)
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@ -68,16 +60,6 @@ class SaveHparamsDecoratedModel(BoringModel):
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self.save_hyperparameters(hparams)
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class AssignHparamsDecoratedModel(BoringModel):
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""" Tests that a model can take an object with explicit setter"""
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@decorate
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@decorate
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def __init__(self, hparams, *my_args, **my_kwargs):
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super().__init__()
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self.hparams = hparams
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# -------------------------
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# STANDARD TESTS
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# -------------------------
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@ -114,7 +96,7 @@ def _run_standard_hparams_test(tmpdir, model, cls, try_overwrite=False):
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@pytest.mark.parametrize(
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"cls", [SaveHparamsModel, AssignHparamsModel, SaveHparamsDecoratedModel, AssignHparamsDecoratedModel]
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"cls", [SaveHparamsModel, SaveHparamsDecoratedModel]
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)
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def test_namespace_hparams(tmpdir, cls):
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# init model
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@ -125,7 +107,7 @@ def test_namespace_hparams(tmpdir, cls):
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@pytest.mark.parametrize(
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"cls", [SaveHparamsModel, AssignHparamsModel, SaveHparamsDecoratedModel, AssignHparamsDecoratedModel]
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"cls", [SaveHparamsModel, SaveHparamsDecoratedModel]
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)
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def test_dict_hparams(tmpdir, cls):
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# init model
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@ -136,7 +118,7 @@ def test_dict_hparams(tmpdir, cls):
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@pytest.mark.parametrize(
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"cls", [SaveHparamsModel, AssignHparamsModel, SaveHparamsDecoratedModel, AssignHparamsDecoratedModel]
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"cls", [SaveHparamsModel, SaveHparamsDecoratedModel]
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)
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def test_omega_conf_hparams(tmpdir, cls):
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# init model
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@ -580,8 +562,7 @@ class SuperClassPositionalArgs(BoringModel):
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def __init__(self, hparams):
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super().__init__()
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self._hparams = None # pretend BoringModel did not call self.save_hyperparameters()
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self.hparams = hparams
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self._hparams = hparams # pretend BoringModel did not call self.save_hyperparameters()
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class SubClassVarArgs(SuperClassPositionalArgs):
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@ -617,8 +598,6 @@ def test_init_arg_with_runtime_change(tmpdir, cls):
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assert model.hparams.running_arg == 123
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model.hparams.running_arg = -1
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assert model.hparams.running_arg == -1
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model.hparams = Namespace(abc=42)
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assert model.hparams.abc == 42
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trainer = Trainer(
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default_root_dir=tmpdir,
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@ -664,18 +643,11 @@ def test_model_save_hyper_parameters_interpolation_with_hydra(tmpdir):
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def __init__(self, args_0, args_1, args_2, kwarg_1=None):
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self.save_hyperparameters()
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self.test_hparams()
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config_file = f"{tmpdir}/hparams.yaml"
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save_hparams_to_yaml(config_file, self.hparams)
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self.hparams = load_hparams_from_yaml(config_file)
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self.test_hparams()
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super().__init__()
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def test_hparams(self):
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assert self.hparams.args_0.log == "Something"
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assert self.hparams.args_1['cfg'].log == "Something"
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assert self.hparams.args_2[0].log == "Something"
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assert self.hparams.kwarg_1['cfg'][0].log == "Something"
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super().__init__()
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with initialize(config_path="conf"):
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args_0 = compose(config_name="config")
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@ -23,6 +23,7 @@ from pytorch_lightning import Trainer
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from pytorch_lightning.utilities import _NATIVE_AMP_AVAILABLE, AMPType
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.base import EvalModelTemplate
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from tests.helpers import BoringModel
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from tests.helpers.datamodules import MNISTDataModule
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@ -282,10 +283,14 @@ def test_auto_scale_batch_size_set_model_attribute(tmpdir, use_hparams):
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def test_auto_scale_batch_size_duplicate_attribute_warning(tmpdir):
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""" Test for a warning when model.batch_size and model.hparams.batch_size both present. """
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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model.hparams = hparams
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# now we have model.batch_size and model.hparams.batch_size
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class TestModel(BoringModel):
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def __init__(self, batch_size=1):
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super().__init__()
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# now we have model.batch_size and model.hparams.batch_size
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self.batch_size = 1
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self.save_hyperparameters()
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, max_steps=1, max_epochs=1000, auto_scale_batch_size=True)
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expected_message = "Field `model.batch_size` and `model.hparams.batch_size` are mutually exclusive!"
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with pytest.warns(UserWarning, match=expected_message):
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