95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import functools
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import os
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import re
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from contextlib import contextmanager
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from typing import Optional, Type
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import pytest
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from pytorch_lightning.callbacks import ModelCheckpoint
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from pytorch_lightning.demos.boring_classes import BoringModel
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from pytorch_lightning.loggers import TensorBoardLogger
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from tests_pytorch import _TEMP_PATH
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def get_default_logger(save_dir, version=None):
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# set up logger object without actually saving logs
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logger = TensorBoardLogger(save_dir, name="lightning_logs", version=version)
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return logger
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def get_data_path(expt_logger, path_dir=None):
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# some calls contain only experiment not complete logger
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# each logger has to have these attributes
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name, version = expt_logger.name, expt_logger.version
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# the other experiments...
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if not path_dir:
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if hasattr(expt_logger, "save_dir") and expt_logger.save_dir:
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path_dir = expt_logger.save_dir
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else:
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path_dir = _TEMP_PATH
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path_expt = os.path.join(path_dir, name, "version_%s" % version)
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# try if the new sub-folder exists, typical case for test-tube
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if not os.path.isdir(path_expt):
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path_expt = path_dir
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return path_expt
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def load_model_from_checkpoint(root_weights_dir, module_class=BoringModel):
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trained_model = module_class.load_from_checkpoint(root_weights_dir)
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assert trained_model is not None, "loading model failed"
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return trained_model
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def assert_ok_model_acc(trainer, key="test_acc", thr=0.5):
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# this model should get 0.80+ acc
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acc = trainer.callback_metrics[key]
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assert acc > thr, f"Model failed to get expected {thr} accuracy. {key} = {acc}"
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def init_checkpoint_callback(logger):
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checkpoint = ModelCheckpoint(dirpath=logger.save_dir)
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return checkpoint
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def getattr_recursive(obj, attr):
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return functools.reduce(getattr, [obj] + attr.split("."))
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@contextmanager
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def no_warning_call(expected_warning: Type[Warning] = UserWarning, match: Optional[str] = None):
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with pytest.warns(None) as record:
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yield
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if match is None:
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try:
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w = record.pop(expected_warning)
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except AssertionError:
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# no warning raised
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return
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else:
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for w in record.list:
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if w.category is expected_warning and re.compile(match).search(w.message.args[0]):
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break
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else:
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return
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msg = "A warning" if expected_warning is None else f"`{expected_warning.__name__}`"
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raise AssertionError(f"{msg} was raised: {w}")
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