merge

tests/core/callbacks/test_callback_events.py → tests/core/callbacks/test_callback_event.py

@@ -4,8 +4,8 @@ from functools import reduce
 from fastNLP.core.callbacks.callback_event import Event, Filter
 
 
-class TestFilter:
 
+class TestFilter:
     def test_params_check(self):
         # 顺利通过
         _filter1 = Filter(every=10)
@@ -80,35 +80,6 @@ class TestFilter:
                 _res.append(cu_res)
         assert _res == [9]
 
-    def test_filter_fn(self):
-        from torch.optim import SGD
-        from torch.utils.data import DataLoader
-        from fastNLP.core.controllers.trainer import Trainer
-        from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
-        from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification
-
-        model = TorchNormalModel_Classification_1(num_labels=3, feature_dimension=10)
-        optimizer = SGD(model.parameters(), lr=0.0001)
-        dataset = TorchNormalDataset_Classification(num_labels=3, feature_dimension=10)
-        dataloader = DataLoader(dataset=dataset, batch_size=4)
-
-        trainer = Trainer(model=model, driver="torch", device="cpu", train_dataloader=dataloader, optimizers=optimizer)
-        def filter_fn(filter, trainer):
-            if trainer.__heihei_test__ == 10:
-                return True
-            return False
-
-        @Filter(filter_fn=filter_fn)
-        def _fn(trainer, data):
-            return data
-
-        _res = []
-        for i in range(100):
-            trainer.__heihei_test__ = i
-            cu_res = _fn(trainer, i)
-            if cu_res is not None:
-                _res.append(cu_res)
-        assert _res == [10]
 
     def test_extract_filter_from_fn(self):
         @Filter(every=10)
@@ -155,3 +126,119 @@ class TestFilter:
         assert _res == [w - 1 for w in range(60, 101, 10)]
 
 
+@pytest.mark.torch
+def test_filter_fn_torch():
+    from torch.optim import SGD
+    from torch.utils.data import DataLoader
+    from fastNLP.core.controllers.trainer import Trainer
+    from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
+    from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification
+
+    model = TorchNormalModel_Classification_1(num_labels=3, feature_dimension=10)
+    optimizer = SGD(model.parameters(), lr=0.0001)
+    dataset = TorchNormalDataset_Classification(num_labels=3, feature_dimension=10)
+    dataloader = DataLoader(dataset=dataset, batch_size=4)
+
+    trainer = Trainer(model=model, driver="torch", device="cpu", train_dataloader=dataloader, optimizers=optimizer)
+    def filter_fn(filter, trainer):
+        if trainer.__heihei_test__ == 10:
+            return True
+        return False
+
+    @Filter(filter_fn=filter_fn)
+    def _fn(trainer, data):
+        return data
+
+    _res = []
+    for i in range(100):
+        trainer.__heihei_test__ = i
+        cu_res = _fn(trainer, i)
+        if cu_res is not None:
+            _res.append(cu_res)
+    assert _res == [10]
+
+
+class TestCallbackEvents:
+    def test_every(self):
+
+        # 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；
+        event_state = Events.on_train_begin()  # 什么都不输入是应当默认 every=1；
+        @Filter(every=event_state.every, once=event_state.once, filter_fn=event_state.filter_fn)
+        def _fn(data):
+            return data
+
+        _res = []
+        for i in range(100):
+            cu_res = _fn(i)
+            if cu_res is not None:
+                _res.append(cu_res)
+        assert _res == list(range(100))
+
+        event_state = Events.on_train_begin(every=10)
+        @Filter(every=event_state.every, once=event_state.once, filter_fn=event_state.filter_fn)
+        def _fn(data):
+            return data
+
+        _res = []
+        for i in range(100):
+            cu_res = _fn(i)
+            if cu_res is not None:
+                _res.append(cu_res)
+        assert _res == [w - 1 for w in range(10, 101, 10)]
+
+    def test_once(self):
+        event_state = Events.on_train_begin(once=10)
+
+        @Filter(once=event_state.once)
+        def _fn(data):
+            return data
+
+        _res = []
+        for i in range(100):
+            cu_res = _fn(i)
+            if cu_res is not None:
+                _res.append(cu_res)
+        assert _res == [9]
+
+
+@pytest.mark.torch
+def test_callback_events_torch():
+    from torch.optim import SGD
+    from torch.utils.data import DataLoader
+    from fastNLP.core.controllers.trainer import Trainer
+    from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
+    from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification
+
+    model = TorchNormalModel_Classification_1(num_labels=3, feature_dimension=10)
+    optimizer = SGD(model.parameters(), lr=0.0001)
+    dataset = TorchNormalDataset_Classification(num_labels=3, feature_dimension=10)
+    dataloader = DataLoader(dataset=dataset, batch_size=4)
+
+    trainer = Trainer(model=model, driver="torch", device="cpu", train_dataloader=dataloader, optimizers=optimizer)
+    def filter_fn(filter, trainer):
+        if trainer.__heihei_test__ == 10:
+            return True
+        return False
+
+    event_state = Events.on_train_begin(filter_fn=filter_fn)
+
+    @Filter(filter_fn=event_state.filter_fn)
+    def _fn(trainer, data):
+        return data
+
+    _res = []
+    for i in range(100):
+        trainer.__heihei_test__ = i
+        cu_res = _fn(trainer, i)
+        if cu_res is not None:
+            _res.append(cu_res)
+    assert _res == [10]
+
+
+
+
+
+
+
+
+

tests/core/controllers/test_trainer_event_trigger.py

@@ -62,10 +62,9 @@ def model_and_optimizers():
 
     return trainer_params
 
-
+@pytest.mark.torch
 @pytest.mark.parametrize("driver,device", [("torch", "cpu")])  # , ("torch", 6), ("torch", [6, 7])
 @pytest.mark.parametrize("callbacks", [[RecordTrainerEventTriggerCallback()]])
-@pytest.mark.torch
 @magic_argv_env_context
 def test_trainer_event_trigger_1(
         model_and_optimizers: TrainerParameters,
@@ -102,125 +101,10 @@ def test_trainer_event_trigger_1(
                 if isinstance(v, staticmethod):
                     assert k in output[0]
 
-@pytest.mark.parametrize("driver,device", [("torch", "cpu")])  # , ("torch", 6), ("torch", [6, 7])
 @pytest.mark.torch
-@magic_argv_env_context
-def test_trainer_event_trigger_2(
-        model_and_optimizers: TrainerParameters,
-        driver,
-        device,
-        n_epochs=2,
-):
-
-    @Trainer.on(Event.on_after_trainer_initialized())
-    def on_after_trainer_initialized(trainer, driver):
-        print("on_after_trainer_initialized")
-
-    @Trainer.on(Event.on_sanity_check_begin())
-    def on_sanity_check_begin(trainer):
-        print("on_sanity_check_begin")
-
-    @Trainer.on(Event.on_sanity_check_end())
-    def on_sanity_check_end(trainer, sanity_check_res):
-        print("on_sanity_check_end")
-
-    @Trainer.on(Event.on_train_begin())
-    def on_train_begin(trainer):
-        print("on_train_begin")
-
-    @Trainer.on(Event.on_train_end())
-    def on_train_end(trainer):
-        print("on_train_end")
-
-    @Trainer.on(Event.on_train_epoch_begin())
-    def on_train_epoch_begin(trainer):
-        if trainer.cur_epoch_idx >= 1:
-            # 触发 on_exception；
-            raise Exception
-        print("on_train_epoch_begin")
-
-    @Trainer.on(Event.on_train_epoch_end())
-    def on_train_epoch_end(trainer):
-        print("on_train_epoch_end")
-
-    @Trainer.on(Event.on_fetch_data_begin())
-    def on_fetch_data_begin(trainer):
-        print("on_fetch_data_begin")
-
-    @Trainer.on(Event.on_fetch_data_end())
-    def on_fetch_data_end(trainer):
-        print("on_fetch_data_end")
-
-    @Trainer.on(Event.on_train_batch_begin())
-    def on_train_batch_begin(trainer, batch, indices=None):
-        print("on_train_batch_begin")
-
-    @Trainer.on(Event.on_train_batch_end())
-    def on_train_batch_end(trainer):
-        print("on_train_batch_end")
-
-    @Trainer.on(Event.on_exception())
-    def on_exception(trainer, exception):
-        print("on_exception")
-
-    @Trainer.on(Event.on_before_backward())
-    def on_before_backward(trainer, outputs):
-        print("on_before_backward")
-
-    @Trainer.on(Event.on_after_backward())
-    def on_after_backward(trainer):
-        print("on_after_backward")
-
-    @Trainer.on(Event.on_before_optimizers_step())
-    def on_before_optimizers_step(trainer, optimizers):
-        print("on_before_optimizers_step")
-
-    @Trainer.on(Event.on_after_optimizers_step())
-    def on_after_optimizers_step(trainer, optimizers):
-        print("on_after_optimizers_step")
-
-    @Trainer.on(Event.on_before_zero_grad())
-    def on_before_zero_grad(trainer, optimizers):
-        print("on_before_zero_grad")
-
-    @Trainer.on(Event.on_after_zero_grad())
-    def on_after_zero_grad(trainer, optimizers):
-        print("on_after_zero_grad")
-
-    @Trainer.on(Event.on_evaluate_begin())
-    def on_evaluate_begin(trainer):
-        print("on_evaluate_begin")
-
-    @Trainer.on(Event.on_evaluate_end())
-    def on_evaluate_end(trainer, results):
-        print("on_evaluate_end")
-
-    with pytest.raises(Exception):
-        with Capturing() as output:
-            trainer = Trainer(
-                model=model_and_optimizers.model,
-                driver=driver,
-                device=device,
-                optimizers=model_and_optimizers.optimizers,
-                train_dataloader=model_and_optimizers.train_dataloader,
-                evaluate_dataloaders=model_and_optimizers.evaluate_dataloaders,
-                input_mapping=model_and_optimizers.input_mapping,
-                output_mapping=model_and_optimizers.output_mapping,
-                metrics=model_and_optimizers.metrics,
-
-                n_epochs=n_epochs,
-            )
-
-            trainer.run()
-        Event_attrs = Event.__dict__
-        for k, v in Event_attrs.items():
-            if isinstance(v, staticmethod):
-                assert k in output[0]
-
 @pytest.mark.parametrize("driver,device", [("torch", "cpu")])  # , ("torch", 6), ("torch", [6, 7])
-@pytest.mark.torch
 @magic_argv_env_context
-def test_trainer_event_trigger_3(
+def test_trainer_event_trigger_2(
         model_and_optimizers: TrainerParameters,
         driver,
         device,
@@ -327,7 +211,6 @@ def test_trainer_event_trigger_3(
             )
 
             trainer.run()
-
         Event_attrs = Event.__dict__
         for k, v in Event_attrs.items():
             if isinstance(v, staticmethod):