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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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