265 lines
8.1 KiB
Python
Executable File
265 lines
8.1 KiB
Python
Executable File
# 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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import os
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from copy import deepcopy
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import pytest
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import torch
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from pytorch_lightning import Trainer
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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.base.datamodules import TrialMNISTDataModule
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def test_error_on_more_than_1_optimizer(tmpdir):
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""" Check that error is thrown when more than 1 optimizer is passed """
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model = EvalModelTemplate()
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model.configure_optimizers = model.configure_optimizers__multiple_schedulers
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# logger file to get meta
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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)
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with pytest.raises(MisconfigurationException):
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trainer.tuner.lr_find(model)
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def test_model_reset_correctly(tmpdir):
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""" Check that model weights are correctly reset after lr_find() """
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model = EvalModelTemplate()
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# logger file to get meta
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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)
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before_state_dict = deepcopy(model.state_dict())
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_ = trainer.tuner.lr_find(model, num_training=5)
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after_state_dict = model.state_dict()
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for key in before_state_dict.keys():
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assert torch.all(torch.eq(before_state_dict[key], after_state_dict[key])), \
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'Model was not reset correctly after learning rate finder'
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assert not os.path.exists(tmpdir / 'lr_find_temp_model.ckpt')
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def test_trainer_reset_correctly(tmpdir):
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""" Check that all trainer parameters are reset correctly after lr_find() """
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model = EvalModelTemplate()
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# logger file to get meta
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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)
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changed_attributes = ['callbacks', 'logger', 'max_steps', 'auto_lr_find',
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'accumulate_grad_batches', 'checkpoint_callback']
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attributes_before = {}
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for ca in changed_attributes:
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attributes_before[ca] = getattr(trainer, ca)
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_ = trainer.tuner.lr_find(model, num_training=5)
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attributes_after = {}
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for ca in changed_attributes:
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attributes_after[ca] = getattr(trainer, ca)
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for key in changed_attributes:
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assert attributes_before[key] == attributes_after[key], \
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f'Attribute {key} was not reset correctly after learning rate finder'
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@pytest.mark.parametrize('use_hparams', [False, True])
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def test_trainer_arg_bool(tmpdir, use_hparams):
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""" Test that setting trainer arg to bool works """
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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before_lr = hparams.get('learning_rate')
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if use_hparams:
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del model.learning_rate
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model.configure_optimizers = model.configure_optimizers__lr_from_hparams
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# logger file to get meta
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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auto_lr_find=True,
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)
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trainer.tune(model)
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if use_hparams:
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after_lr = model.hparams.learning_rate
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else:
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after_lr = model.learning_rate
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assert before_lr != after_lr, \
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'Learning rate was not altered after running learning rate finder'
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@pytest.mark.parametrize('use_hparams', [False, True])
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def test_trainer_arg_str(tmpdir, use_hparams):
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""" Test that setting trainer arg to string works """
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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model.my_fancy_lr = 1.0 # update with non-standard field
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model.hparams['my_fancy_lr'] = 1.0
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before_lr = model.my_fancy_lr
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if use_hparams:
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del model.my_fancy_lr
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model.configure_optimizers = model.configure_optimizers__lr_from_hparams
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# logger file to get meta
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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auto_lr_find='my_fancy_lr',
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)
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trainer.tune(model)
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if use_hparams:
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after_lr = model.hparams.my_fancy_lr
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else:
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after_lr = model.my_fancy_lr
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assert before_lr != after_lr, \
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'Learning rate was not altered after running learning rate finder'
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@pytest.mark.parametrize('optimizer', ['Adam', 'Adagrad'])
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def test_call_to_trainer_method(tmpdir, optimizer):
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""" Test that directly calling the trainer method works """
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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if optimizer == 'adagrad':
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model.configure_optimizers = model.configure_optimizers__adagrad
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before_lr = hparams.get('learning_rate')
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# logger file to get meta
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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)
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lrfinder = trainer.tuner.lr_find(model, mode='linear')
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after_lr = lrfinder.suggestion()
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model.learning_rate = after_lr
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trainer.tune(model)
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assert before_lr != after_lr, \
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'Learning rate was not altered after running learning rate finder'
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def test_datamodule_parameter(tmpdir):
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""" Test that the datamodule parameter works """
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# trial datamodule
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dm = TrialMNISTDataModule(tmpdir)
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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before_lr = hparams.get('learning_rate')
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# logger file to get meta
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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)
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lrfinder = trainer.tuner.lr_find(model, datamodule=dm)
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after_lr = lrfinder.suggestion()
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model.learning_rate = after_lr
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assert before_lr != after_lr, \
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'Learning rate was not altered after running learning rate finder'
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def test_accumulation_and_early_stopping(tmpdir):
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""" Test that early stopping of learning rate finder works, and that
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accumulation also works for this feature """
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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before_lr = hparams.get('learning_rate')
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# logger file to get meta
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trainer = Trainer(
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default_root_dir=tmpdir,
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accumulate_grad_batches=2,
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)
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lrfinder = trainer.tuner.lr_find(model, early_stop_threshold=None)
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after_lr = lrfinder.suggestion()
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expected_num_lrs = 100
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expected_batch_idx = 200 - 1
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assert before_lr != after_lr, \
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'Learning rate was not altered after running learning rate finder'
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assert len(lrfinder.results['lr']) == expected_num_lrs, \
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'Early stopping for learning rate finder did not work'
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assert lrfinder._total_batch_idx == expected_batch_idx, \
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'Accumulation parameter did not work'
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def test_suggestion_parameters_work(tmpdir):
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""" Test that default skipping does not alter results in basic case """
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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# logger file to get meta
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=3,
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)
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lrfinder = trainer.tuner.lr_find(model)
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lr1 = lrfinder.suggestion(skip_begin=10) # default
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lr2 = lrfinder.suggestion(skip_begin=80) # way too high, should have an impact
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assert lr1 != lr2, \
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'Skipping parameter did not influence learning rate'
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def test_suggestion_with_non_finite_values(tmpdir):
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""" Test that non-finite values does not alter results """
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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# logger file to get meta
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=3,
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)
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lrfinder = trainer.tuner.lr_find(model)
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before_lr = lrfinder.suggestion()
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lrfinder.results['loss'][-1] = float('nan')
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after_lr = lrfinder.suggestion()
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assert before_lr == after_lr, \
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'Learning rate was altered because of non-finite loss values'
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