lightning/tests/test_deprecated.py

"""Test deprecated functionality which will be removed in vX.Y.Z"""
import random
import sys

import pytest
import torch

from pytorch_lightning import Trainer
from tests.base import EvalModelTemplate


def _soft_unimport_module(str_module):
    # once the module is imported  e.g with parsing with pytest it lives in memory
    if str_module in sys.modules:
        del sys.modules[str_module]


def test_tbd_remove_in_v0_10_0_trainer():
    rnd_val = random.random()
    with pytest.deprecated_call(match='will be removed in v0.10.0'):
        trainer = Trainer(overfit_pct=rnd_val)
    assert trainer.overfit_batches == rnd_val
    with pytest.deprecated_call(match='will be removed in v0.10.0'):
        assert trainer.overfit_pct == rnd_val

    rnd_val = random.random()
    with pytest.deprecated_call(match='will be removed in v0.10.0'):
        trainer = Trainer(train_percent_check=rnd_val)
    assert trainer.limit_train_batches == rnd_val
    with pytest.deprecated_call(match='v0.10.0'):
        assert trainer.train_percent_check == rnd_val

    rnd_val = random.random()
    with pytest.deprecated_call(match='will be removed in v0.10.0'):
        trainer = Trainer(val_percent_check=rnd_val)
    assert trainer.limit_val_batches == rnd_val
    with pytest.deprecated_call(match='will be removed in v0.10.0'):
        assert trainer.val_percent_check == rnd_val

    rnd_val = random.random()
    with pytest.deprecated_call(match='will be removed in v0.10.0'):
        trainer = Trainer(test_percent_check=rnd_val)
    assert trainer.limit_test_batches == rnd_val
    with pytest.deprecated_call(match='will be removed in v0.10.0'):
        assert trainer.test_percent_check == rnd_val

    trainer = Trainer()
    with pytest.deprecated_call(match='will be removed in v0.10.0'):
        trainer.proc_rank = 0
    with pytest.deprecated_call(match='will be removed in v0.10.0'):
        assert trainer.proc_rank == trainer.global_rank

    with pytest.deprecated_call(match='will be removed in v0.10.0'):
        trainer.ckpt_path = 'foo'
        assert trainer.ckpt_path == trainer.weights_save_path == 'foo'


class ModelVer0_6(EvalModelTemplate):

    # todo: this shall not be needed while evaluate asks for dataloader explicitly
    def val_dataloader(self):
        return self.dataloader(train=False)

    def validation_step(self, batch, batch_idx, *args, **kwargs):
        return {'val_loss': torch.tensor(0.6)}

    def validation_end(self, outputs):
        return {'val_loss': torch.tensor(0.6)}

    def test_dataloader(self):
        return self.dataloader(train=False)

    def test_end(self, outputs):
        return {'test_loss': torch.tensor(0.6)}


class ModelVer0_7(EvalModelTemplate):

    # todo: this shall not be needed while evaluate asks for dataloader explicitly
    def val_dataloader(self):
        return self.dataloader(train=False)

    def validation_step(self, batch, batch_idx, *args, **kwargs):
        return {'val_loss': torch.tensor(0.7)}

    def validation_end(self, outputs):
        return {'val_loss': torch.tensor(0.7)}

    def test_dataloader(self):
        return self.dataloader(train=False)

    def test_end(self, outputs):
        return {'test_loss': torch.tensor(0.7)}


# def test_tbd_remove_in_v1_0_0_model_hooks():
#
#     model = ModelVer0_6()
#
#     with pytest.deprecated_call(match='will be removed in v1.0. Use `test_epoch_end` instead'):
#         trainer = Trainer(logger=False)
#         trainer.test(model)
#     assert trainer.callback_metrics == {'test_loss': torch.tensor(0.6)}
#
#     with pytest.deprecated_call(match='will be removed in v1.0. Use `validation_epoch_end` instead'):
#         trainer = Trainer(logger=False)
#         # TODO: why `dataloder` is required if it is not used
#         result = trainer._evaluate(model, dataloaders=[[None]], max_batches=1)
#     assert result[0] == {'val_loss': torch.tensor(0.6)}
#
#     model = ModelVer0_7()
#
#     with pytest.deprecated_call(match='will be removed in v1.0. Use `test_epoch_end` instead'):
#         trainer = Trainer(logger=False)
#         trainer.test(model)
#     assert trainer.callback_metrics == {'test_loss': torch.tensor(0.7)}
#
#     with pytest.deprecated_call(match='will be removed in v1.0. Use `validation_epoch_end` instead'):
#         trainer = Trainer(logger=False)
#         # TODO: why `dataloder` is required if it is not used
#         result = trainer._evaluate(model, dataloaders=[[None]], max_batches=1)
#     assert result[0] == {'val_loss': torch.tensor(0.7)}