93 lines
3.6 KiB
Python
93 lines
3.6 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 glob
|
|
import os
|
|
import sys
|
|
from unittest.mock import patch
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
import pytorch_lightning as pl
|
|
from pytorch_lightning import Callback, Trainer
|
|
from pytorch_lightning.callbacks import EarlyStopping
|
|
from tests import _PATH_LEGACY, _PROJECT_ROOT
|
|
|
|
LEGACY_CHECKPOINTS_PATH = os.path.join(_PATH_LEGACY, "checkpoints")
|
|
CHECKPOINT_EXTENSION = ".ckpt"
|
|
# load list of all back compatible versions
|
|
with open(os.path.join(_PROJECT_ROOT, "legacy", "back-compatible-versions.txt")) as fp:
|
|
LEGACY_BACK_COMPATIBLE_PL_VERSIONS = [ln.strip() for ln in fp.readlines()]
|
|
|
|
|
|
@pytest.mark.parametrize("pl_version", LEGACY_BACK_COMPATIBLE_PL_VERSIONS)
|
|
def test_load_legacy_checkpoints(tmpdir, pl_version: str):
|
|
PATH_LEGACY = os.path.join(LEGACY_CHECKPOINTS_PATH, pl_version)
|
|
with patch("sys.path", [PATH_LEGACY] + sys.path):
|
|
from simple_classif_training import ClassifDataModule, ClassificationModel
|
|
|
|
path_ckpts = sorted(glob.glob(os.path.join(PATH_LEGACY, f"*{CHECKPOINT_EXTENSION}")))
|
|
assert path_ckpts, f'No checkpoints found in folder "{PATH_LEGACY}"'
|
|
path_ckpt = path_ckpts[-1]
|
|
|
|
model = ClassificationModel.load_from_checkpoint(path_ckpt)
|
|
trainer = Trainer(default_root_dir=str(tmpdir))
|
|
dm = ClassifDataModule()
|
|
res = trainer.test(model, datamodule=dm)
|
|
assert res[0]["test_loss"] <= 0.7
|
|
assert res[0]["test_acc"] >= 0.85
|
|
print(res)
|
|
|
|
|
|
class LimitNbEpochs(Callback):
|
|
def __init__(self, nb: int):
|
|
self.limit = nb
|
|
self._count = 0
|
|
|
|
def on_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
|
|
self._count += 1
|
|
if self._count >= self.limit:
|
|
trainer.should_stop = True
|
|
|
|
|
|
@pytest.mark.parametrize("pl_version", LEGACY_BACK_COMPATIBLE_PL_VERSIONS)
|
|
def test_resume_legacy_checkpoints(tmpdir, pl_version: str):
|
|
PATH_LEGACY = os.path.join(LEGACY_CHECKPOINTS_PATH, pl_version)
|
|
with patch("sys.path", [PATH_LEGACY] + sys.path):
|
|
from simple_classif_training import ClassifDataModule, ClassificationModel
|
|
|
|
path_ckpts = sorted(glob.glob(os.path.join(PATH_LEGACY, f"*{CHECKPOINT_EXTENSION}")))
|
|
assert path_ckpts, f'No checkpoints found in folder "{PATH_LEGACY}"'
|
|
path_ckpt = path_ckpts[-1]
|
|
|
|
dm = ClassifDataModule()
|
|
model = ClassificationModel()
|
|
es = EarlyStopping(monitor="val_acc", mode="max", min_delta=0.005)
|
|
stop = LimitNbEpochs(1)
|
|
trainer = Trainer(
|
|
default_root_dir=str(tmpdir),
|
|
gpus=int(torch.cuda.is_available()),
|
|
precision=(16 if torch.cuda.is_available() else 32),
|
|
checkpoint_callback=True,
|
|
callbacks=[es, stop],
|
|
max_epochs=21,
|
|
accumulate_grad_batches=2,
|
|
deterministic=True,
|
|
resume_from_checkpoint=path_ckpt,
|
|
)
|
|
trainer.fit(model, datamodule=dm)
|
|
res = trainer.test(model, datamodule=dm)
|
|
assert res[0]["test_loss"] <= 0.7
|
|
assert res[0]["test_acc"] >= 0.85
|