142 lines
4.6 KiB
Python
142 lines
4.6 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 Trainer, callbacks, seed_everything
|
|
from tests.base import BoringModel
|
|
|
|
|
|
@mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
|
|
def test_mc_called_on_fastdevrun(tmpdir):
|
|
seed_everything(1234)
|
|
|
|
train_val_step_model = BoringModel()
|
|
|
|
# fast dev run = called once
|
|
# train loop only, dict, eval result
|
|
trainer = Trainer(fast_dev_run=True)
|
|
trainer.fit(train_val_step_model)
|
|
|
|
# checkpoint should have been called once with fast dev run
|
|
assert len(trainer.dev_debugger.checkpoint_callback_history) == 1
|
|
|
|
# -----------------------
|
|
# also called once with no val step
|
|
# -----------------------
|
|
class TrainingStepCalled(BoringModel):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.training_step_called = False
|
|
self.validation_step_called = False
|
|
self.test_step_called = False
|
|
|
|
def training_step(self, batch, batch_idx):
|
|
self.training_step_called = True
|
|
return super().training_step(batch, batch_idx)
|
|
|
|
train_step_only_model = TrainingStepCalled()
|
|
train_step_only_model.validation_step = None
|
|
|
|
# fast dev run = called once
|
|
# train loop only, dict, eval result
|
|
trainer = Trainer(fast_dev_run=True)
|
|
trainer.fit(train_step_only_model)
|
|
|
|
# make sure only training step was called
|
|
assert train_step_only_model.training_step_called
|
|
assert not train_step_only_model.validation_step_called
|
|
assert not train_step_only_model.test_step_called
|
|
|
|
# checkpoint should have been called once with fast dev run
|
|
assert len(trainer.dev_debugger.checkpoint_callback_history) == 1
|
|
|
|
|
|
@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
|