129 lines
3.9 KiB
Python
129 lines
3.9 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 functools
|
|
import os
|
|
import traceback
|
|
from contextlib import contextmanager
|
|
from typing import Optional
|
|
|
|
import pytest
|
|
|
|
from pytorch_lightning import seed_everything
|
|
from pytorch_lightning.callbacks import ModelCheckpoint
|
|
from pytorch_lightning.loggers import TensorBoardLogger
|
|
from tests import _TEMP_PATH, RANDOM_PORTS
|
|
from tests.helpers.boring_model import BoringModel
|
|
|
|
|
|
def get_default_logger(save_dir, version=None):
|
|
# set up logger object without actually saving logs
|
|
logger = TensorBoardLogger(save_dir, name="lightning_logs", version=version)
|
|
return logger
|
|
|
|
|
|
def get_data_path(expt_logger, path_dir=None):
|
|
# some calls contain only experiment not complete logger
|
|
|
|
# each logger has to have these attributes
|
|
name, version = expt_logger.name, expt_logger.version
|
|
|
|
# the other experiments...
|
|
if not path_dir:
|
|
if hasattr(expt_logger, "save_dir") and expt_logger.save_dir:
|
|
path_dir = expt_logger.save_dir
|
|
else:
|
|
path_dir = _TEMP_PATH
|
|
path_expt = os.path.join(path_dir, name, "version_%s" % version)
|
|
|
|
# try if the new sub-folder exists, typical case for test-tube
|
|
if not os.path.isdir(path_expt):
|
|
path_expt = path_dir
|
|
return path_expt
|
|
|
|
|
|
def load_model_from_checkpoint(logger, root_weights_dir, module_class=BoringModel):
|
|
trained_model = module_class.load_from_checkpoint(root_weights_dir)
|
|
assert trained_model is not None, "loading model failed"
|
|
return trained_model
|
|
|
|
|
|
def assert_ok_model_acc(trainer, key="test_acc", thr=0.5):
|
|
# this model should get 0.80+ acc
|
|
acc = trainer.callback_metrics[key]
|
|
assert acc > thr, f"Model failed to get expected {thr} accuracy. {key} = {acc}"
|
|
|
|
|
|
def reset_seed(seed=0):
|
|
seed_everything(seed)
|
|
|
|
|
|
def set_random_main_port():
|
|
reset_seed()
|
|
port = RANDOM_PORTS.pop()
|
|
os.environ["MASTER_PORT"] = str(port)
|
|
|
|
|
|
def init_checkpoint_callback(logger):
|
|
checkpoint = ModelCheckpoint(dirpath=logger.save_dir)
|
|
return checkpoint
|
|
|
|
|
|
def pl_multi_process_test(func):
|
|
"""Wrapper for running multi-processing tests."""
|
|
|
|
@functools.wraps(func)
|
|
def wrapper(*args, **kwargs):
|
|
|
|
from multiprocessing import Process, Queue
|
|
|
|
queue = Queue()
|
|
|
|
def inner_f(queue, **kwargs):
|
|
try:
|
|
func(**kwargs)
|
|
queue.put(1)
|
|
except Exception:
|
|
_trace = traceback.format_exc()
|
|
print(_trace)
|
|
# code 17 means RuntimeError: tensorflow/compiler/xla/xla_client/mesh_service.cc:364 :
|
|
# Failed to meet rendezvous 'torch_xla.core.xla_model.save': Socket closed (14)
|
|
if "terminated with exit code 17" in _trace:
|
|
queue.put(1)
|
|
else:
|
|
queue.put(-1)
|
|
|
|
proc = Process(target=inner_f, args=(queue,), kwargs=kwargs)
|
|
proc.start()
|
|
proc.join()
|
|
|
|
result = queue.get()
|
|
assert result == 1, "expected 1, but returned %s" % result
|
|
|
|
return wrapper
|
|
|
|
|
|
@contextmanager
|
|
def no_warning_call(warning_type, match: Optional[str] = None):
|
|
with pytest.warns(None) as record:
|
|
yield
|
|
|
|
try:
|
|
w = record.pop(warning_type)
|
|
if not (match and match in str(w.message)):
|
|
return
|
|
except AssertionError:
|
|
# no warning raised
|
|
return
|
|
raise AssertionError(f"`{warning_type}` was raised: {w}")
|