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https://gitee.com/fastnlp/fastNLP.git
synced 2024-11-30 11:17:50 +08:00
加入了callback events 的测试
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YWMditto
parent
30af3b032f
commit
b88a15dabb
@ -4,8 +4,8 @@ from functools import reduce
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from fastNLP.core.callbacks.callback_events import Events, Filter
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class TestFilter:
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class TestFilter:
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def test_params_check(self):
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# 顺利通过
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_filter1 = Filter(every=10)
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@ -80,35 +80,6 @@ class TestFilter:
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_res.append(cu_res)
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assert _res == [9]
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def test_filter_fn(self):
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from torch.optim import SGD
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from torch.utils.data import DataLoader
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from fastNLP.core.controllers.trainer import Trainer
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from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
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from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification
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model = TorchNormalModel_Classification_1(num_labels=3, feature_dimension=10)
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optimizer = SGD(model.parameters(), lr=0.0001)
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dataset = TorchNormalDataset_Classification(num_labels=3, feature_dimension=10)
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dataloader = DataLoader(dataset=dataset, batch_size=4)
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trainer = Trainer(model=model, driver="torch", device="cpu", train_dataloader=dataloader, optimizers=optimizer)
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def filter_fn(filter, trainer):
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if trainer.__heihei_test__ == 10:
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return True
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return False
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@Filter(filter_fn=filter_fn)
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def _fn(trainer, data):
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return data
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_res = []
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for i in range(100):
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trainer.__heihei_test__ = i
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cu_res = _fn(trainer, i)
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if cu_res is not None:
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_res.append(cu_res)
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assert _res == [10]
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def test_extract_filter_from_fn(self):
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@Filter(every=10)
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@ -155,3 +126,119 @@ class TestFilter:
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assert _res == [w - 1 for w in range(60, 101, 10)]
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@pytest.mark.torch
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def test_filter_fn_torch():
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from torch.optim import SGD
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from torch.utils.data import DataLoader
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from fastNLP.core.controllers.trainer import Trainer
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from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
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from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification
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model = TorchNormalModel_Classification_1(num_labels=3, feature_dimension=10)
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optimizer = SGD(model.parameters(), lr=0.0001)
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dataset = TorchNormalDataset_Classification(num_labels=3, feature_dimension=10)
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dataloader = DataLoader(dataset=dataset, batch_size=4)
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trainer = Trainer(model=model, driver="torch", device="cpu", train_dataloader=dataloader, optimizers=optimizer)
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def filter_fn(filter, trainer):
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if trainer.__heihei_test__ == 10:
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return True
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return False
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@Filter(filter_fn=filter_fn)
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def _fn(trainer, data):
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return data
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_res = []
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for i in range(100):
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trainer.__heihei_test__ = i
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cu_res = _fn(trainer, i)
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if cu_res is not None:
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_res.append(cu_res)
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assert _res == [10]
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class TestCallbackEvents:
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def test_every(self):
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# 这里是什么样的事件是不影响的，因为我们是与 Trainer 拆分开了进行测试；
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event_state = Events.on_train_begin() # 什么都不输入是应当默认 every=1；
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@Filter(every=event_state.every, once=event_state.once, filter_fn=event_state.filter_fn)
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def _fn(data):
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return data
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_res = []
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for i in range(100):
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cu_res = _fn(i)
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if cu_res is not None:
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_res.append(cu_res)
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assert _res == list(range(100))
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event_state = Events.on_train_begin(every=10)
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@Filter(every=event_state.every, once=event_state.once, filter_fn=event_state.filter_fn)
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def _fn(data):
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return data
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_res = []
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for i in range(100):
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cu_res = _fn(i)
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if cu_res is not None:
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_res.append(cu_res)
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assert _res == [w - 1 for w in range(10, 101, 10)]
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def test_once(self):
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event_state = Events.on_train_begin(once=10)
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@Filter(once=event_state.once)
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def _fn(data):
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return data
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_res = []
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for i in range(100):
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cu_res = _fn(i)
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if cu_res is not None:
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_res.append(cu_res)
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assert _res == [9]
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@pytest.mark.torch
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def test_callback_events_torch():
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from torch.optim import SGD
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from torch.utils.data import DataLoader
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from fastNLP.core.controllers.trainer import Trainer
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from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
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from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification
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model = TorchNormalModel_Classification_1(num_labels=3, feature_dimension=10)
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optimizer = SGD(model.parameters(), lr=0.0001)
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dataset = TorchNormalDataset_Classification(num_labels=3, feature_dimension=10)
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dataloader = DataLoader(dataset=dataset, batch_size=4)
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trainer = Trainer(model=model, driver="torch", device="cpu", train_dataloader=dataloader, optimizers=optimizer)
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def filter_fn(filter, trainer):
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if trainer.__heihei_test__ == 10:
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return True
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return False
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event_state = Events.on_train_begin(filter_fn=filter_fn)
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@Filter(filter_fn=event_state.filter_fn)
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def _fn(trainer, data):
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return data
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_res = []
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for i in range(100):
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trainer.__heihei_test__ = i
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cu_res = _fn(trainer, i)
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if cu_res is not None:
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_res.append(cu_res)
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assert _res == [10]
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@ -221,124 +221,6 @@ def test_trainer_event_trigger_2(
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@pytest.mark.parametrize("driver,device", [("torch", "cpu")]) # , ("torch", 6), ("torch", [6, 7])
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@pytest.mark.torch
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@magic_argv_env_context
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def test_trainer_event_trigger_3(
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model_and_optimizers: TrainerParameters,
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driver,
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device,
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n_epochs=2,
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):
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@Trainer.on(Events.on_after_trainer_initialized)
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def on_after_trainer_initialized(trainer, driver):
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print("on_after_trainer_initialized")
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@Trainer.on(Events.on_sanity_check_begin)
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def on_sanity_check_begin(trainer):
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print("on_sanity_check_begin")
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@Trainer.on(Events.on_sanity_check_end)
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def on_sanity_check_end(trainer, sanity_check_res):
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print("on_sanity_check_end")
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@Trainer.on(Events.on_train_begin)
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def on_train_begin(trainer):
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print("on_train_begin")
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@Trainer.on(Events.on_train_end)
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def on_train_end(trainer):
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print("on_train_end")
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@Trainer.on(Events.on_train_epoch_begin)
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def on_train_epoch_begin(trainer):
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if trainer.cur_epoch_idx >= 1:
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# 触发 on_exception；
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raise Exception
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print("on_train_epoch_begin")
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@Trainer.on(Events.on_train_epoch_end)
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def on_train_epoch_end(trainer):
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print("on_train_epoch_end")
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@Trainer.on(Events.on_fetch_data_begin)
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def on_fetch_data_begin(trainer):
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print("on_fetch_data_begin")
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@Trainer.on(Events.on_fetch_data_end)
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def on_fetch_data_end(trainer):
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print("on_fetch_data_end")
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@Trainer.on(Events.on_train_batch_begin)
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def on_train_batch_begin(trainer, batch, indices=None):
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print("on_train_batch_begin")
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@Trainer.on(Events.on_train_batch_end)
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def on_train_batch_end(trainer):
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print("on_train_batch_end")
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@Trainer.on(Events.on_exception)
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def on_exception(trainer, exception):
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print("on_exception")
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@Trainer.on(Events.on_before_backward)
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def on_before_backward(trainer, outputs):
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print("on_before_backward")
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@Trainer.on(Events.on_after_backward)
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def on_after_backward(trainer):
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print("on_after_backward")
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@Trainer.on(Events.on_before_optimizers_step)
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def on_before_optimizers_step(trainer, optimizers):
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print("on_before_optimizers_step")
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@Trainer.on(Events.on_after_optimizers_step)
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def on_after_optimizers_step(trainer, optimizers):
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print("on_after_optimizers_step")
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@Trainer.on(Events.on_before_zero_grad)
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def on_before_zero_grad(trainer, optimizers):
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print("on_before_zero_grad")
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@Trainer.on(Events.on_after_zero_grad)
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def on_after_zero_grad(trainer, optimizers):
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print("on_after_zero_grad")
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@Trainer.on(Events.on_evaluate_begin)
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def on_evaluate_begin(trainer):
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print("on_evaluate_begin")
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@Trainer.on(Events.on_evaluate_end)
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def on_evaluate_end(trainer, results):
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print("on_evaluate_end")
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with pytest.raises(Exception):
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with Capturing() as output:
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trainer = Trainer(
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model=model_and_optimizers.model,
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driver=driver,
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device=device,
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optimizers=model_and_optimizers.optimizers,
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train_dataloader=model_and_optimizers.train_dataloader,
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evaluate_dataloaders=model_and_optimizers.evaluate_dataloaders,
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input_mapping=model_and_optimizers.input_mapping,
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output_mapping=model_and_optimizers.output_mapping,
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metrics=model_and_optimizers.metrics,
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n_epochs=n_epochs,
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)
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trainer.run()
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if dist.is_initialized():
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dist.destroy_process_group()
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for name, member in Events.__members__.items():
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assert member.value in output[0]
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