129 lines
4.2 KiB
Python
129 lines
4.2 KiB
Python
# 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 pytorch_lightning import Trainer, seed_everything, callbacks
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from tests.base import EvalModelTemplate, BoringModel
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from unittest import mock
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import pytest
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import torch
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def test_mc_called_on_fastdevrun(tmpdir):
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seed_everything(1234)
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os.environ['PL_DEV_DEBUG'] = '1'
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train_val_step_model = EvalModelTemplate()
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# fast dev run = called once
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# train loop only, dict, eval result
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trainer = Trainer(fast_dev_run=True)
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trainer.fit(train_val_step_model)
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# checkpoint should have been called once with fast dev run
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assert len(trainer.dev_debugger.checkpoint_callback_history) == 1
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# -----------------------
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# also called once with no val step
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# -----------------------
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train_step_only_model = EvalModelTemplate()
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train_step_only_model.validation_step = None
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# fast dev run = called once
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# train loop only, dict, eval result
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trainer = Trainer(fast_dev_run=True)
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trainer.fit(train_step_only_model)
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# make sure only training step was called
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assert train_step_only_model.training_step_called
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assert not train_step_only_model.validation_step_called
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assert not train_step_only_model.test_step_called
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# checkpoint should have been called once with fast dev run
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assert len(trainer.dev_debugger.checkpoint_callback_history) == 1
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def test_mc_called(tmpdir):
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seed_everything(1234)
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os.environ['PL_DEV_DEBUG'] = '1'
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# -----------------
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# TRAIN LOOP ONLY
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# -----------------
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train_step_only_model = EvalModelTemplate()
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train_step_only_model.validation_step = None
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# no callback
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trainer = Trainer(max_epochs=3, checkpoint_callback=False)
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trainer.fit(train_step_only_model)
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assert len(trainer.dev_debugger.checkpoint_callback_history) == 0
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# -----------------
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# TRAIN + VAL LOOP ONLY
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# -----------------
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val_train_model = EvalModelTemplate()
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# no callback
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trainer = Trainer(max_epochs=3, checkpoint_callback=False)
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trainer.fit(val_train_model)
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assert len(trainer.dev_debugger.checkpoint_callback_history) == 0
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@mock.patch('torch.save')
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@pytest.mark.parametrize(['epochs', 'val_check_interval', 'expected'],
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[(1, 1.0, 1), (2, 1.0, 2), (1, 0.25, 4), (2, 0.3, 7)])
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def test_default_checkpoint_freq(save_mock, tmpdir, epochs, val_check_interval, expected):
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=epochs,
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weights_summary=None,
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val_check_interval=val_check_interval
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)
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trainer.fit(model)
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# make sure types are correct
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assert save_mock.call_count == expected
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@mock.patch('torch.save')
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@pytest.mark.parametrize(['k', 'epochs', 'val_check_interval', 'expected'],
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[(1, 1, 1.0, 1), (2, 2, 1.0, 2), (2, 1, 0.25, 4), (2, 2, 0.3, 7)])
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def test_top_k(save_mock, tmpdir, k, epochs, val_check_interval, expected):
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.last_coeff = 10.0
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def training_step(self, batch, batch_idx):
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loss = self.step(torch.ones(32))
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loss = loss / (loss + 0.0000001)
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loss += self.last_coeff
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self.log('my_loss', loss)
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self.last_coeff *= 0.999
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return loss
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model = TestModel()
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trainer = Trainer(
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checkpoint_callback=callbacks.ModelCheckpoint(dirpath=tmpdir, monitor='my_loss', save_top_k=k),
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default_root_dir=tmpdir,
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max_epochs=epochs,
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weights_summary=None,
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val_check_interval=val_check_interval
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)
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trainer.fit(model)
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# make sure types are correct
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assert save_mock.call_count == expected
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