# Copyright The Lightning AI 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 from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.demos.boring_classes import BoringModel from lightning.pytorch.loggers import TensorBoardLogger from tests_pytorch import _TEMP_PATH def get_default_logger(save_dir, version=None): # set up logger object without actually saving logs return TensorBoardLogger(save_dir, name="lightning_logs", version=version) 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: path_dir = expt_logger.save_dir if hasattr(expt_logger, "save_dir") and expt_logger.save_dir else _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(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 init_checkpoint_callback(logger): return ModelCheckpoint(dirpath=logger.save_dir) def getattr_recursive(obj, attr): return functools.reduce(getattr, [obj] + attr.split("."))