lightning/tests/checkpointing/test_checkpoint_callback_fr...

97 lines
3.1 KiB
Python

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from unittest import mock
import pytest
import torch
from pytorch_lightning import callbacks, seed_everything, Trainer
from tests.base import BoringModel
@mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
def test_mc_called(tmpdir):
seed_everything(1234)
# -----------------
# TRAIN LOOP ONLY
# -----------------
train_step_only_model = BoringModel()
train_step_only_model.validation_step = None
# no callback
trainer = Trainer(max_epochs=3, checkpoint_callback=False)
trainer.fit(train_step_only_model)
assert len(trainer.dev_debugger.checkpoint_callback_history) == 0
# -----------------
# TRAIN + VAL LOOP ONLY
# -----------------
val_train_model = BoringModel()
# no callback
trainer = Trainer(max_epochs=3, checkpoint_callback=False)
trainer.fit(val_train_model)
assert len(trainer.dev_debugger.checkpoint_callback_history) == 0
@mock.patch('torch.save')
@pytest.mark.parametrize(['epochs', 'val_check_interval', 'expected'],
[(1, 1.0, 1), (2, 1.0, 2), (1, 0.25, 4), (2, 0.3, 7)])
def test_default_checkpoint_freq(save_mock, tmpdir, epochs, val_check_interval, expected):
model = BoringModel()
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=epochs,
weights_summary=None,
val_check_interval=val_check_interval
)
trainer.fit(model)
# make sure types are correct
assert save_mock.call_count == expected
@mock.patch('torch.save')
@pytest.mark.parametrize(['k', 'epochs', 'val_check_interval', 'expected'],
[(1, 1, 1.0, 1), (2, 2, 1.0, 2), (2, 1, 0.25, 4), (2, 2, 0.3, 7)])
def test_top_k(save_mock, tmpdir, k, epochs, val_check_interval, expected):
class TestModel(BoringModel):
def __init__(self):
super().__init__()
self.last_coeff = 10.0
def training_step(self, batch, batch_idx):
loss = self.step(torch.ones(32))
loss = loss / (loss + 0.0000001)
loss += self.last_coeff
self.log('my_loss', loss)
self.last_coeff *= 0.999
return loss
model = TestModel()
trainer = Trainer(
callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor='my_loss', save_top_k=k)],
default_root_dir=tmpdir,
max_epochs=epochs,
weights_summary=None,
val_check_interval=val_check_interval
)
trainer.fit(model)
# make sure types are correct
assert save_mock.call_count == expected