Add LambdaCallback

CHANGELOG.md

@@ -29,6 +29,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added `R2Score` metric ([#5241](https://github.com/PyTorchLightning/pytorch-lightning/pull/5241))
 
+
+- Added `LambdaCallback` ([#5347](https://github.com/PyTorchLightning/pytorch-lightning/pull/5347))
+
+
 - Added `BackboneLambdaFinetuningCallback` ([#5377](https://github.com/PyTorchLightning/pytorch-lightning/pull/5377))
 
 

docs/source/callbacks.rst

@@ -98,6 +98,7 @@ Lightning has a few built-in callbacks.
     EarlyStopping
     GPUStatsMonitor
     GradientAccumulationScheduler
+    LambdaCallback
     LearningRateMonitor
     ModelCheckpoint
     ProgressBar

pytorch_lightning/callbacks/__init__.py

@@ -16,17 +16,19 @@
 from pytorch_lightning.callbacks.finetuning import BackboneLambdaFinetuningCallback, BaseFinetuningCallback
 from pytorch_lightning.callbacks.gpu_stats_monitor import GPUStatsMonitor
 from pytorch_lightning.callbacks.gradient_accumulation_scheduler import GradientAccumulationScheduler
+from pytorch_lightning.callbacks.lambda_function import LambdaCallback
 from pytorch_lightning.callbacks.lr_monitor import LearningRateMonitor
 from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
 from pytorch_lightning.callbacks.progress import ProgressBar, ProgressBarBase
 
 __all__ = [
+    'BackboneLambdaFinetuningCallback',
+    'BaseFinetuningCallback',
     'Callback',
     'EarlyStopping',
     'GPUStatsMonitor',
     'GradientAccumulationScheduler',
-    'BaseFinetuningCallback',
-    'BackboneLambdaFinetuningCallback',
+    'LambdaCallback',
     'LearningRateMonitor',
     'ModelCheckpoint',
     'ProgressBar',

pytorch_lightning/callbacks/lambda_function.py

@@ -0,0 +1,158 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+r"""
+Lambda Callback
+^^^^^^^^^^^^^^^
+
+Create a simple callback on the fly using lambda functions.
+
+"""
+
+from typing import Callable, Optional
+
+from pytorch_lightning.callbacks.base import Callback
+
+
+class LambdaCallback(Callback):
+    r"""
+    Create a simple callback on the fly using lambda functions.
+
+    Args:
+        **kwargs: hooks supported by :class:`~pytorch_lightning.callbacks.base.Callback`
+
+    Example::
+
+        >>> from pytorch_lightning import Trainer
+        >>> from pytorch_lightning.callbacks import LambdaCallback
+        >>> trainer = Trainer(callbacks=[LambdaCallback(setup=lambda *args: print('setup'))])
+    """
+
+    def __init__(
+        self,
+        on_before_accelerator_backend_setup: Optional[Callable] = None,
+        setup: Optional[Callable] = None,
+        teardown: Optional[Callable] = None,
+        on_init_start: Optional[Callable] = None,
+        on_init_end: Optional[Callable] = None,
+        on_fit_start: Optional[Callable] = None,
+        on_fit_end: Optional[Callable] = None,
+        on_sanity_check_start: Optional[Callable] = None,
+        on_sanity_check_end: Optional[Callable] = None,
+        on_train_batch_start: Optional[Callable] = None,
+        on_train_batch_end: Optional[Callable] = None,
+        on_train_epoch_start: Optional[Callable] = None,
+        on_train_epoch_end: Optional[Callable] = None,
+        on_validation_epoch_start: Optional[Callable] = None,
+        on_validation_epoch_end: Optional[Callable] = None,
+        on_test_epoch_start: Optional[Callable] = None,
+        on_test_epoch_end: Optional[Callable] = None,
+        on_epoch_start: Optional[Callable] = None,
+        on_epoch_end: Optional[Callable] = None,
+        on_batch_start: Optional[Callable] = None,
+        on_validation_batch_start: Optional[Callable] = None,
+        on_validation_batch_end: Optional[Callable] = None,
+        on_test_batch_start: Optional[Callable] = None,
+        on_test_batch_end: Optional[Callable] = None,
+        on_batch_end: Optional[Callable] = None,
+        on_train_start: Optional[Callable] = None,
+        on_train_end: Optional[Callable] = None,
+        on_pretrain_routine_start: Optional[Callable] = None,
+        on_pretrain_routine_end: Optional[Callable] = None,
+        on_validation_start: Optional[Callable] = None,
+        on_validation_end: Optional[Callable] = None,
+        on_test_start: Optional[Callable] = None,
+        on_test_end: Optional[Callable] = None,
+        on_keyboard_interrupt: Optional[Callable] = None,
+        on_save_checkpoint: Optional[Callable] = None,
+        on_load_checkpoint: Optional[Callable] = None,
+        on_after_backward: Optional[Callable] = None,
+        on_before_zero_grad: Optional[Callable] = None,
+    ):
+        if on_before_accelerator_backend_setup is not None:
+            self.on_before_accelerator_backend_setup = on_before_accelerator_backend_setup
+        if setup is not None:
+            self.setup = setup
+        if teardown is not None:
+            self.teardown = teardown
+        if on_init_start is not None:
+            self.on_init_start = on_init_start
+        if on_init_end is not None:
+            self.on_init_end = on_init_end
+        if on_fit_start is not None:
+            self.on_fit_start = on_fit_start
+        if on_fit_end is not None:
+            self.on_fit_end = on_fit_end
+        if on_sanity_check_start is not None:
+            self.on_sanity_check_start = on_sanity_check_start
+        if on_sanity_check_end is not None:
+            self.on_sanity_check_end = on_sanity_check_end
+        if on_train_batch_start is not None:
+            self.on_train_batch_start = on_train_batch_start
+        if on_train_batch_end is not None:
+            self.on_train_batch_end = on_train_batch_end
+        if on_train_epoch_start is not None:
+            self.on_train_epoch_start = on_train_epoch_start
+        if on_train_epoch_end is not None:
+            self.on_train_epoch_end = on_train_epoch_end
+        if on_validation_epoch_start is not None:
+            self.on_validation_epoch_start = on_validation_epoch_start
+        if on_validation_epoch_end is not None:
+            self.on_validation_epoch_end = on_validation_epoch_end
+        if on_test_epoch_start is not None:
+            self.on_test_epoch_start = on_test_epoch_start
+        if on_test_epoch_end is not None:
+            self.on_test_epoch_end = on_test_epoch_end
+        if on_epoch_start is not None:
+            self.on_epoch_start = on_epoch_start
+        if on_epoch_end is not None:
+            self.on_epoch_end = on_epoch_end
+        if on_batch_start is not None:
+            self.on_batch_start = on_batch_start
+        if on_validation_batch_start is not None:
+            self.on_validation_batch_start = on_validation_batch_start
+        if on_validation_batch_end is not None:
+            self.on_validation_batch_end = on_validation_batch_end
+        if on_test_batch_start is not None:
+            self.on_test_batch_start = on_test_batch_start
+        if on_test_batch_end is not None:
+            self.on_test_batch_end = on_test_batch_end
+        if on_batch_end is not None:
+            self.on_batch_end = on_batch_end
+        if on_train_start is not None:
+            self.on_train_start = on_train_start
+        if on_train_end is not None:
+            self.on_train_end = on_train_end
+        if on_pretrain_routine_start is not None:
+            self.on_pretrain_routine_start = on_pretrain_routine_start
+        if on_pretrain_routine_end is not None:
+            self.on_pretrain_routine_end = on_pretrain_routine_end
+        if on_validation_start is not None:
+            self.on_validation_start = on_validation_start
+        if on_validation_end is not None:
+            self.on_validation_end = on_validation_end
+        if on_test_start is not None:
+            self.on_test_start = on_test_start
+        if on_test_end is not None:
+            self.on_test_end = on_test_end
+        if on_keyboard_interrupt is not None:
+            self.on_keyboard_interrupt = on_keyboard_interrupt
+        if on_save_checkpoint is not None:
+            self.on_save_checkpoint = on_save_checkpoint
+        if on_load_checkpoint is not None:
+            self.on_load_checkpoint = on_load_checkpoint
+        if on_after_backward is not None:
+            self.on_after_backward = on_after_backward
+        if on_before_zero_grad is not None:
+            self.on_before_zero_grad = on_before_zero_grad

tests/callbacks/test_lambda_function.py

@@ -0,0 +1,60 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import inspect
+
+from pytorch_lightning import seed_everything, Trainer
+from pytorch_lightning.callbacks import Callback, LambdaCallback
+from tests.base.boring_model import BoringModel
+
+
+def test_lambda_call(tmpdir):
+    seed_everything(42)
+
+    class CustomModel(BoringModel):
+        def on_train_epoch_start(self):
+            if self.current_epoch > 1:
+                raise KeyboardInterrupt
+
+    checker = set()
+    hooks = [m for m, _ in inspect.getmembers(Callback, predicate=inspect.isfunction)]
+    hooks_args = {h: (lambda x: lambda *args: checker.add(x))(h) for h in hooks}
+    hooks_args["on_save_checkpoint"] = (lambda x: lambda *args: [checker.add(x)])("on_save_checkpoint")
+
+    model = CustomModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        limit_train_batches=1,
+        limit_val_batches=1,
+        callbacks=[LambdaCallback(**hooks_args)],
+    )
+    results = trainer.fit(model)
+    assert results
+
+    model = CustomModel()
+    ckpt_path = trainer.checkpoint_callback.best_model_path
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=3,
+        limit_train_batches=1,
+        limit_val_batches=1,
+        limit_test_batches=1,
+        resume_from_checkpoint=ckpt_path,
+        callbacks=[LambdaCallback(**hooks_args)],
+    )
+    results = trainer.fit(model)
+    trainer.test(model)
+
+    assert results
+    assert checker == set(hooks)