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How to use save_handler method in avocado

Best Python code snippet using avocado_python

test_checkpoint.py

Source:test_checkpoint.py

1import os2import warnings3from unittest.mock import MagicMock4import pytest5import torch6import torch.nn as nn7from ignite.engine import Engine, Events, State8from ignite.handlers import Checkpoint, DiskSaver, ModelCheckpoint9_PREFIX = "PREFIX"10class DummyModel(nn.Module):11    def __init__(self):12        super(DummyModel, self).__init__()13        self.net = nn.Linear(1, 1)14    def forward(self, x):15        return self.net(x)16def test_checkpoint_wrong_input():17    with pytest.raises(TypeError, match=r"Argument `to_save` should be a dictionary"):18        Checkpoint(19            12,20            lambda x: x,21            "prefix",22        )23    with pytest.raises(TypeError, match=r"Argument `to_save` should be a dictionary"):24        Checkpoint(25            [26                12,27            ],28            lambda x: x,29            "prefix",30        )31    with pytest.raises(ValueError, match=r"No objects to checkpoint."):32        Checkpoint({}, lambda x: x, "prefix")33    model = DummyModel()34    to_save = {"model": model}35    with pytest.raises(TypeError, match=r"Argument `save_handler` should be callable"):36        Checkpoint(to_save, 12, "prefix")37    with pytest.raises(38        ValueError,39        match=r"If `score_name` is provided, then `score_function` should be also provided.",40    ):41        Checkpoint(to_save, lambda x: x, score_name="acc")42    with pytest.raises(TypeError, match=r"global_step_transform should be a function."):43        Checkpoint(44            to_save,45            lambda x: x,46            score_function=lambda e: 123,47            score_name="acc",48            global_step_transform=123,49        )50def test_checkpoint_default():51    def _test(to_save, obj, name):52        save_handler = MagicMock()53        save_handler.remove = MagicMock()54        checkpointer = Checkpoint(to_save, save_handler=save_handler)55        assert checkpointer.last_checkpoint is None56        trainer = Engine(lambda e, b: None)57        trainer.state = State(epoch=0, iteration=0)58        checkpointer(trainer)59        assert save_handler.call_count == 160        save_handler.assert_called_with(obj, "{}_0.pth".format(name))61        trainer.state.epoch = 1262        trainer.state.iteration = 123463        checkpointer(trainer)64        assert save_handler.call_count == 265        save_handler.assert_called_with(obj, "{}_1234.pth".format(name))66        assert save_handler.remove.call_count == 167        save_handler.remove.assert_called_with("{}_0.pth".format(name))68        assert checkpointer.last_checkpoint == "{}_1234.pth".format(name)69    model = DummyModel()70    to_save = {"model": model}71    _test(to_save, model.state_dict(), "model")72    model = DummyModel()73    optimizer = torch.optim.SGD(model.parameters(), lr=0.1)74    to_save = {"model": model, "optimizer": optimizer}75    _test(76        to_save,77        {"model": model.state_dict(), "optimizer": optimizer.state_dict()},78        "checkpoint",79    )80def test_checkpoint_with_global_step_transform():81    def _test(filename_prefix, to_save, obj, name):82        save_handler = MagicMock()83        save_handler.remove = MagicMock()84        checkpointer = Checkpoint(85            to_save,86            save_handler=save_handler,87            filename_prefix=filename_prefix,88            global_step_transform=lambda e, _: e.state.epoch,89        )90        trainer = Engine(lambda e, b: None)91        trainer.state = State(epoch=1, iteration=1)92        checkpointer(trainer)93        assert save_handler.call_count == 194        if len(filename_prefix) > 0:95            filename_prefix += "_"96        save_handler.assert_called_with(obj, "{}{}_1.pth".format(filename_prefix, name))97        trainer.state.epoch = 1298        trainer.state.iteration = 123499        checkpointer(trainer)100        assert save_handler.call_count == 2101        save_handler.assert_called_with(102            obj, "{}{}_12.pth".format(filename_prefix, name)103        )104        assert save_handler.remove.call_count == 1105        save_handler.remove.assert_called_with(106            "{}{}_1.pth".format(filename_prefix, name)107        )108        assert checkpointer.last_checkpoint == "{}{}_12.pth".format(109            filename_prefix, name110        )111    for prefix in ["", "dummytask"]:112        model = DummyModel()113        to_save = {"model": model}114        _test(prefix, to_save, model.state_dict(), "model")115        model = DummyModel()116        optimizer = torch.optim.SGD(model.parameters(), lr=0.1)117        to_save = {"model": model, "optimizer": optimizer}118        _test(119            prefix,120            to_save,121            {"model": model.state_dict(), "optimizer": optimizer.state_dict()},122            "checkpoint",123        )124def test_checkpoint_with_score_function():125    def _test(to_save, obj, name):126        save_handler = MagicMock()127        save_handler.remove = MagicMock()128        checkpointer = Checkpoint(129            to_save, save_handler=save_handler, score_function=lambda e: e.state.score130        )131        trainer = Engine(lambda e, b: None)132        trainer.state = State(epoch=1, iteration=1, score=0.77)133        checkpointer(trainer)134        assert save_handler.call_count == 1135        save_handler.assert_called_with(obj, "{}_0.77.pth".format(name))136        trainer.state.epoch = 12137        trainer.state.iteration = 1234138        trainer.state.score = 0.78139        checkpointer(trainer)140        assert save_handler.call_count == 2141        save_handler.assert_called_with(obj, "{}_0.78.pth".format(name))142        assert save_handler.remove.call_count == 1143        save_handler.remove.assert_called_with("{}_0.77.pth".format(name))144        assert checkpointer.last_checkpoint == "{}_0.78.pth".format(name)145    model = DummyModel()146    to_save = {"model": model}147    _test(to_save, model.state_dict(), "model")148    model = DummyModel()149    optimizer = torch.optim.SGD(model.parameters(), lr=0.1)150    to_save = {"model": model, "optimizer": optimizer}151    _test(152        to_save,153        {"model": model.state_dict(), "optimizer": optimizer.state_dict()},154        "checkpoint",155    )156def test_checkpoint_with_score_name_and_function():157    def _test(to_save, obj, name):158        save_handler = MagicMock()159        save_handler.remove = MagicMock()160        checkpointer = Checkpoint(161            to_save,162            save_handler=save_handler,163            score_name="loss",164            score_function=lambda e: e.state.score,165        )166        trainer = Engine(lambda e, b: None)167        trainer.state = State(epoch=1, iteration=1, score=-0.77)168        checkpointer(trainer)169        assert save_handler.call_count == 1170        save_handler.assert_called_with(obj, "{}_loss=-0.77.pth".format(name))171        trainer.state.epoch = 12172        trainer.state.iteration = 1234173        trainer.state.score = -0.76174        checkpointer(trainer)175        assert save_handler.call_count == 2176        save_handler.assert_called_with(obj, "{}_loss=-0.76.pth".format(name))177        assert save_handler.remove.call_count == 1178        save_handler.remove.assert_called_with("{}_loss=-0.77.pth".format(name))179        assert checkpointer.last_checkpoint == "{}_loss=-0.76.pth".format(name)180    model = DummyModel()181    to_save = {"model": model}182    _test(to_save, model.state_dict(), "model")183    model = DummyModel()184    optimizer = torch.optim.SGD(model.parameters(), lr=0.1)185    to_save = {"model": model, "optimizer": optimizer}186    _test(187        to_save,188        {"model": model.state_dict(), "optimizer": optimizer.state_dict()},189        "checkpoint",190    )191def test_checkpoint_with_score_function_and_trainer_epoch():192    def _test(to_save, obj, name):193        save_handler = MagicMock()194        save_handler.remove = MagicMock()195        trainer = Engine(lambda e, b: None)196        evaluator = Engine(lambda e, b: None)197        trainer.state = State(epoch=11, iteration=1)198        checkpointer = Checkpoint(199            to_save,200            save_handler=save_handler,201            global_step_transform=lambda _1, _2: trainer.state.epoch,202            score_function=lambda e: e.state.metrics["val_acc"],203        )204        evaluator.state = State(epoch=1, iteration=1000, metrics={"val_acc": 0.77})205        checkpointer(evaluator)206        assert save_handler.call_count == 1207        save_handler.assert_called_with(obj, "{}_11_0.77.pth".format(name))208        trainer.state.epoch = 12209        evaluator.state.metrics["val_acc"] = 0.78210        checkpointer(evaluator)211        assert save_handler.call_count == 2212        save_handler.assert_called_with(obj, "{}_12_0.78.pth".format(name))213        assert save_handler.remove.call_count == 1214        save_handler.remove.assert_called_with("{}_11_0.77.pth".format(name))215        assert checkpointer.last_checkpoint == "{}_12_0.78.pth".format(name)216    model = DummyModel()217    to_save = {"model": model}218    _test(to_save, model.state_dict(), "model")219def test_checkpoint_with_score_name_and_function_and_trainer_epoch():220    def _test(to_save, obj, name):221        save_handler = MagicMock()222        save_handler.remove = MagicMock()223        trainer = Engine(lambda e, b: None)224        evaluator = Engine(lambda e, b: None)225        trainer.state = State(epoch=11, iteration=1)226        checkpointer = Checkpoint(227            to_save,228            save_handler=save_handler,229            global_step_transform=lambda _1, _2: trainer.state.epoch,230            score_name="val_acc",231            score_function=lambda e: e.state.metrics["val_acc"],232        )233        evaluator.state = State(epoch=1, iteration=1000, metrics={"val_acc": 0.77})234        checkpointer(evaluator)235        assert save_handler.call_count == 1236        save_handler.assert_called_with(obj, "{}_11_val_acc=0.77.pth".format(name))237        trainer.state.epoch = 12238        evaluator.state.metrics["val_acc"] = 0.78239        checkpointer(evaluator)240        assert save_handler.call_count == 2241        save_handler.assert_called_with(obj, "{}_12_val_acc=0.78.pth".format(name))242        assert save_handler.remove.call_count == 1243        save_handler.remove.assert_called_with("{}_11_val_acc=0.77.pth".format(name))244        assert checkpointer.last_checkpoint == "{}_12_val_acc=0.78.pth".format(name)245    model = DummyModel()246    to_save = {"model": model}247    _test(to_save, model.state_dict(), "model")248def test_model_checkpoint_args_validation(dirname):249    existing = os.path.join(dirname, "existing_dir")250    nonempty = os.path.join(dirname, "nonempty")251    os.makedirs(existing)252    os.makedirs(nonempty)253    with open(os.path.join(nonempty, "{}_name_0.pth".format(_PREFIX)), "w"):254        pass255    with pytest.raises(256        ValueError, match=r"with extension '.pth' or '.pth.tar' are already present "257    ):258        ModelCheckpoint(nonempty, _PREFIX)259    with pytest.raises(260        ValueError, match=r"Argument save_interval is deprecated and should be None"261    ):262        ModelCheckpoint(existing, _PREFIX, save_interval=42)263    with pytest.raises(ValueError, match=r"Directory path '\S+' is not found"):264        ModelCheckpoint(265            os.path.join(dirname, "non_existing_dir"), _PREFIX, create_dir=False266        )267    with pytest.raises(268        ValueError,269        match=r"Argument save_as_state_dict is deprecated and should be True",270    ):271        ModelCheckpoint(existing, _PREFIX, create_dir=False, save_as_state_dict=False)272    with pytest.raises(273        ValueError, match=r"If `score_name` is provided, then `score_function` "274    ):275        ModelCheckpoint(existing, _PREFIX, create_dir=False, score_name="test")276    with pytest.raises(TypeError, match=r"global_step_transform should be a function"):277        ModelCheckpoint(existing, _PREFIX, create_dir=False, global_step_transform=1234)278    h = ModelCheckpoint(dirname, _PREFIX, create_dir=False)279    assert h.last_checkpoint is None280    with pytest.raises(RuntimeError, match=r"No objects to checkpoint found."):281        h(None, [])282def test_model_checkpoint_simple_recovery(dirname):283    h = ModelCheckpoint(dirname, _PREFIX, create_dir=False)284    engine = Engine(lambda e, b: None)285    engine.state = State(epoch=0, iteration=1)286    model = DummyModel()287    to_save = {"model": model}288    h(engine, to_save)289    fname = h.last_checkpoint290    assert isinstance(fname, str)291    assert os.path.join(dirname, _PREFIX) in fname292    assert os.path.exists(fname)293    loaded_objects = torch.load(fname)294    assert loaded_objects == model.state_dict()295def test_model_checkpoint_simple_recovery_from_existing_non_empty(dirname):296    def _test(ext, require_empty, archived):297        previous_fname = os.path.join(298            dirname, "{}_{}_{}{}".format(_PREFIX, "obj", 1, ext)299        )300        with open(previous_fname, "w") as f:301            f.write("test")302        h = ModelCheckpoint(303            dirname,304            _PREFIX,305            create_dir=True,306            require_empty=require_empty,307            archived=archived,308        )309        engine = Engine(lambda e, b: None)310        engine.state = State(epoch=0, iteration=1)311        model = DummyModel()312        to_save = {"model": model}313        h(engine, to_save)314        fname = h.last_checkpoint315        ext = ".pth.tar" if archived else ".pth"316        assert isinstance(fname, str)317        assert (318            os.path.join(dirname, "{}_{}_{}{}".format(_PREFIX, "model", 1, ext))319            == fname320        )321        assert os.path.exists(fname)322        assert os.path.exists(previous_fname)323        loaded_objects = torch.load(fname)324        assert loaded_objects == model.state_dict()325        os.remove(fname)326    _test(".txt", require_empty=True, archived=False)327    _test(".txt", require_empty=True, archived=True)328    _test(".pth", require_empty=False, archived=False)329def test_disk_saver_atomic(dirname):330    model = DummyModel()331    to_save_serializable = {"model": model}332    to_save_non_serializable = {"model": lambda x: x}333    def _test_existance(atomic, _to_save, expected):334        saver = DiskSaver(dirname, atomic=atomic, create_dir=False, require_empty=False)335        fname = "test.pth"336        try:337            with warnings.catch_warnings():338                # Ignore torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type339                # DummyModel. It won't be checked for correctness upon loading.340                warnings.simplefilter("ignore", category=UserWarning)341                saver(_to_save, fname)342        except Exception:343            pass344        fp = os.path.join(saver.dirname, fname)345        assert os.path.exists(fp) == expected346        if expected:347            saver.remove(fname)348    _test_existance(atomic=False, _to_save=to_save_serializable, expected=True)349    _test_existance(atomic=False, _to_save=to_save_non_serializable, expected=True)350    _test_existance(atomic=True, _to_save=to_save_serializable, expected=True)351    _test_existance(atomic=True, _to_save=to_save_non_serializable, expected=False)352def test_last_k(dirname):353    h = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=2)354    engine = Engine(lambda e, b: None)355    engine.state = State(epoch=0, iteration=0)356    model = DummyModel()357    to_save = {"model": model}358    h(engine, to_save)359    for i in range(1, 9):360        engine.state.iteration = i361        h(engine, to_save)362    expected = ["{}_{}_{}.pth".format(_PREFIX, "model", i) for i in [7, 8]]363    assert sorted(os.listdir(dirname)) == expected, "{} vs {}".format(364        sorted(os.listdir(dirname)), expected365    )366def test_disabled_n_saved(dirname):367    h = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=None)368    engine = Engine(lambda e, b: None)369    engine.state = State(epoch=0, iteration=0)370    model = DummyModel()371    to_save = {"model": model}372    num_iters = 100373    for i in range(num_iters):374        engine.state.iteration = i375        h(engine, to_save)376    saved_files = sorted(os.listdir(dirname))377    assert len(saved_files) == num_iters, "{}".format(saved_files)378    expected = sorted(379        ["{}_{}_{}.pth".format(_PREFIX, "model", i) for i in range(num_iters)]380    )381    assert saved_files == expected, "{} vs {}".format(saved_files, expected)382def test_best_k(dirname):383    scores = iter([1.2, -2.0, 3.1, -4.0])384    def score_function(_):385        return next(scores)386    h = ModelCheckpoint(387        dirname, _PREFIX, create_dir=False, n_saved=2, score_function=score_function388    )389    engine = Engine(lambda e, b: None)390    engine.state = State(epoch=0, iteration=0)391    model = DummyModel()392    to_save = {"model": model}393    for _ in range(4):394        h(engine, to_save)395    expected = ["{}_{}_{}.pth".format(_PREFIX, "model", i) for i in [1.2, 3.1]]396    assert sorted(os.listdir(dirname)) == expected397def test_best_k_with_suffix(dirname):398    scores = [0.3456789, 0.1234, 0.4567, 0.134567]399    scores_iter = iter(scores)400    def score_function(engine):401        return next(scores_iter)402    h = ModelCheckpoint(403        dirname,404        _PREFIX,405        create_dir=False,406        n_saved=2,407        score_function=score_function,408        score_name="val_loss",409    )410    engine = Engine(lambda e, b: None)411    engine.state = State(epoch=0, iteration=0)412    model = DummyModel()413    to_save = {"model": model}414    for _ in range(4):415        engine.state.epoch += 1416        h(engine, to_save)417    expected = [418        "{}_{}_val_loss={:.7}.pth".format(_PREFIX, "model", scores[e - 1])419        for e in [1, 3]420    ]421    assert sorted(os.listdir(dirname)) == expected422def test_with_engine(dirname):423    def update_fn(_1, _2):424        pass425    name = "model"426    engine = Engine(update_fn)427    handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=2)428    model = DummyModel()429    to_save = {"model": model}430    engine.add_event_handler(Events.EPOCH_COMPLETED, handler, to_save)431    engine.run([0], max_epochs=4)432    expected = ["{}_{}_{}.pth".format(_PREFIX, name, i) for i in [3, 4]]433    assert sorted(os.listdir(dirname)) == expected434def test_with_state_dict(dirname):435    def update_fn(_1, _2):436        pass437    engine = Engine(update_fn)438    handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1)439    model = DummyModel()440    to_save = {"model": model}441    engine.add_event_handler(Events.EPOCH_COMPLETED, handler, to_save)442    engine.run([0], max_epochs=4)443    saved_model = os.path.join(dirname, os.listdir(dirname)[0])444    load_model = torch.load(saved_model)445    assert not isinstance(load_model, DummyModel)446    assert isinstance(load_model, dict)447    model_state_dict = model.state_dict()448    loaded_model_state_dict = load_model449    for key in model_state_dict.keys():450        assert key in loaded_model_state_dict451        model_value = model_state_dict[key]452        loaded_model_value = loaded_model_state_dict[key]453        assert model_value.numpy() == loaded_model_value.numpy()454def test_valid_state_dict_save(dirname):455    model = DummyModel()456    h = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1)457    engine = Engine(lambda e, b: None)458    engine.state = State(epoch=0, iteration=0)459    to_save = {"name": 42}460    with pytest.raises(TypeError, match=r"should have `state_dict` method"):461        h(engine, to_save)462    to_save = {"name": model}463    try:464        h(engine, to_save)465    except ValueError:466        pytest.fail("Unexpected ValueError")467def test_save_model_optimizer_lr_scheduler_with_state_dict(dirname):468    model = DummyModel()469    optim = torch.optim.SGD(model.parameters(), lr=0.001)470    lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optim, gamma=0.5)471    def update_fn(engine, batch):472        x = torch.rand((4, 1))473        optim.zero_grad()474        y = model(x)475        loss = y.pow(2.0).sum()476        loss.backward()477        optim.step()478        lr_scheduler.step()479    engine = Engine(update_fn)480    handler = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=1)481    engine.add_event_handler(482        Events.EPOCH_COMPLETED,483        handler,484        {485            "model": model,486            "optimizer": optim,487            "lr_scheduler": lr_scheduler,488        },489    )490    engine.run([0], max_epochs=4)491    saved_objects = sorted(os.listdir(dirname))492    # saved object is ['PREFIX_checkpoint_4.pth', ]493    saved_checkpoint = os.path.join(dirname, saved_objects[0])494    loaded_obj = torch.load(saved_checkpoint)495    for f in ["model", "optimizer", "lr_scheduler"]:496        assert f in loaded_obj497    loaded_model_state_dict = loaded_obj["model"]498    loaded_optimizer_state_dict = loaded_obj["optimizer"]499    loaded_lr_scheduler_state_dict = loaded_obj["lr_scheduler"]500    assert isinstance(loaded_model_state_dict, dict)501    assert isinstance(loaded_optimizer_state_dict, dict)502    assert isinstance(loaded_lr_scheduler_state_dict, dict)503    model_state_dict = model.state_dict()504    for key in model_state_dict.keys():505        assert key in loaded_model_state_dict506        model_value = model_state_dict[key]507        loaded_model_value = loaded_model_state_dict[key]508        assert model_value.numpy() == loaded_model_value.numpy()509    optim_state_dict = optim.state_dict()510    for key in optim_state_dict.keys():511        assert key in loaded_optimizer_state_dict512        optim_value = optim_state_dict[key]513        loaded_optim_value = loaded_optimizer_state_dict[key]514        assert optim_value == loaded_optim_value515    lr_scheduler_state_dict = lr_scheduler.state_dict()516    for key in lr_scheduler_state_dict.keys():517        assert key in loaded_lr_scheduler_state_dict518        lr_scheduler_value = lr_scheduler_state_dict[key]519        loaded_lr_scheduler_value = loaded_lr_scheduler_state_dict[key]520        assert lr_scheduler_value == loaded_lr_scheduler_value521def test_checkpoint_load_objects():522    with pytest.raises(TypeError, match=r"Argument checkpoint should be a dictionary"):523        Checkpoint.load_objects({}, [])524    with pytest.raises(TypeError, match=r"should have `load_state_dict` method"):525        Checkpoint.load_objects({"a": None}, {"a": None})526    model = DummyModel()527    to_load = {"model": model}528    with pytest.raises(529        ValueError, match=r"from `to_load` is not found in the checkpoint"530    ):531        Checkpoint.load_objects(to_load, {})532    model = DummyModel()533    to_load = {"model": model}534    model2 = DummyModel()535    chkpt = {"model": model2.state_dict()}536    Checkpoint.load_objects(to_load, chkpt)537    assert model.state_dict() == model2.state_dict()538def test_disksaver_wrong_input(dirname):539    with pytest.raises(ValueError, match=r"Directory path '\S+' is not found"):540        DiskSaver("/tmp/non-existing-folder", create_dir=False)541    def _test(ext):542        previous_fname = os.path.join(543            dirname, "{}_{}_{}{}".format(_PREFIX, "obj", 1, ext)544        )545        with open(previous_fname, "w") as f:546            f.write("test")547        with pytest.raises(548            ValueError, match=r"with extension '.pth' or '.pth.tar' are already present"549        ):550            DiskSaver(dirname, require_empty=True)551    _test(".pth")...

cli.py

Source:cli.py

1import yaml2import click3import cro_tax_debtors4from cro_tax_debtors.screen import Screen5from cro_tax_debtors.debtors import Debtors, CategoryDone6@click.group()7@click.pass_context8@click.option('-f', '--file_path', type=click.File(mode='r'), help='Input YAML file')9def cli(ctx, file_path):10    ctx.obj = {'data': yaml.load(file_path.read())['website']['porezna-uprava']}11@cli.command()12@click.pass_context13@click.option('-p', '--print_in_terminal', default=False, is_flag=True, help='Print in terminal')14def parse(ctx, print_in_terminal):15    screen = Screen()16    for category in ctx.obj['data']:17        if not category['enabled']:18            continue19        for page in range(1, 2000):20            try:21                spider = getattr(cro_tax_debtors.spiders, category['spider'])(category['url'].format(page))22                save_handler = getattr(cro_tax_debtors.save_handlers, category['save_handler'])(category)23                Debtors(spider, save_handler, screen, category).parse(print_in_terminal)24            except CategoryDone:25                break26@cli.command()27@click.pass_context28@click.option('-n', '--name', help='Name of the debtor')29def find(ctx, name):30    screen = Screen()31    for category in ctx.obj['data']:32        if not category['enabled']:33            continue34        save_handler = getattr(cro_tax_debtors.save_handlers, category['save_handler'])(category)35        Debtors(save_handler=save_handler, screen=screen, category_data=category).find(name)36@cli.command()37@click.pass_context38def delete(ctx):39    for category in ctx.obj['data']:40        if not category['enabled']:41            continue42        save_handler = getattr(cro_tax_debtors.save_handlers, category['save_handler'])(category)43        res = Debtors(save_handler=save_handler, category_data=category).delete()...

__init__.py

Source:__init__.py

1import bpy2from . import load_handler3from . import save_handler4def register():5    if load_handler.load_handler not in bpy.app.handlers.load_post:6        bpy.app.handlers.load_post.append(load_handler.load_handler)7    if save_handler.save_handler not in bpy.app.handlers.save_post:8        bpy.app.handlers.save_post.append(save_handler.save_handler)9def unregister():10    if load_handler.load_handler in bpy.app.handlers.load_post:11        bpy.app.handlers.load_post.remove(load_handler.load_handler)12    if save_handler.save_handler not in bpy.app.handlers.save_post:...

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