Fix ReduceLROnPlateau

CHANGELOG.md

@@ -46,6 +46,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed a bug where `ObjectDetector`, `InstanceSegmentation`, and `KeypointDetector` would log train and validation metrics with the same name ([#1252](https://github.com/PyTorchLightning/lightning-flash/pull/1252))
 
+- Fixed a bug where using `ReduceLROnPlateau` would raise an error ([#1251](https://github.com/PyTorchLightning/lightning-flash/pull/1251))
+
 ## [0.7.0] - 2022-02-15
 
 ### Added

docs/source/general/optimization.rst

@@ -15,37 +15,42 @@ Setting an optimizer to a task
 Each task has a built-in method :func:`~flash.core.model.Task.available_optimizers` which will list all the optimizers
 registered with Flash.
 
+.. doctest::
+
     >>> from flash.core.classification import ClassificationTask
-    >>> ClassificationTask.available_optimizers()
-    ['adadelta', ..., 'sgd']
+    >>> ClassificationTask.available_optimizers()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    [...'adadelta', ..., 'sgd'...]
 
 To train / finetune a :class:`~flash.core.model.Task` of your choice, just pass on a string.
 
-.. code-block:: python
-
-    from flash.image import ImageClassifier
+.. doctest::
 
-    model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer="Adam", learning_rate=1e-4)
+    >>> from flash.image import ImageClassifier
+    >>> model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer="Adam", learning_rate=1e-4)
+    >>> model.configure_optimizers()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    Adam ...
 
 
 In order to customize specific parameters of the Optimizer, pass along a dictionary of kwargs with the string as a tuple.
 
-.. code-block:: python
+.. doctest::
 
-    from flash.image import ImageClassifier
-
-    model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer=("Adam", {"amsgrad": True}), learning_rate=1e-4)
+    >>> from flash.image import ImageClassifier
+    >>> model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer=("Adam", {"amsgrad": True}), learning_rate=1e-4)
+    >>> model.configure_optimizers()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    Adam ( ... amsgrad: True ...)
 
 
 An alternative to customizing an optimizer using a tuple is to pass it as a callable.
 
-.. code-block:: python
-
-    from functools import partial
-    from torch.optim import Adam
-    from flash.image import ImageClassifier
+.. doctest::
 
-    model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer=partial(Adam, amsgrad=True), learning_rate=1e-4)
+    >>> from functools import partial
+    >>> from torch.optim import Adam
+    >>> from flash.image import ImageClassifier
+    >>> model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer=partial(Adam, amsgrad=True), learning_rate=1e-4)
+    >>> model.configure_optimizers()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    Adam ( ... amsgrad: True ...)
 
 
 Setting a Learning Rate Scheduler
@@ -54,53 +59,60 @@ Setting a Learning Rate Scheduler
 Each task has a built-in method :func:`~flash.core.model.Task.available_lr_schedulers` which will list all the learning
 rate schedulers registered with Flash.
 
-    >>> from flash.core.classification import ClassificationTask
-    >>> ClassificationTask.available_lr_schedulers()
-    ['lambdalr', ..., 'cosineannealingwarmrestarts']
+.. doctest::
 
-To train / finetune a :class:`~flash.core.model.Task` of your choice, just pass on a string.
+    >>> from flash.core.classification import ClassificationTask
+    >>> ClassificationTask.available_lr_schedulers()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    [...'cosineannealingwarmrestarts', ..., 'lambdalr'...]
 
-.. code-block:: python
+To train / finetune a :class:`~flash.core.model.Task` with a scheduler of your choice, just pass in the name:
 
-    from flash.image import ImageClassifier
+.. doctest::
 
-    model = ImageClassifier(
-        num_classes=10, backbone="resnet18", optimizer="Adam", learning_rate=1e-4, lr_scheduler="constant_schedule"
-    )
+    >>> from flash.image import ImageClassifier
+    >>> model = ImageClassifier(
+    ...     num_classes=10, backbone="resnet18", optimizer="Adam", learning_rate=1e-4, lr_scheduler="constant_schedule"
+    ... )
+    >>> model.configure_optimizers()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    ([Adam ...], [{'scheduler': ...}])
 
 .. note:: ``"constant_schedule"`` and a few other lr schedulers will be available only if you have installed the ``transformers`` library from Hugging Face.
 
 
 In order to customize specific parameters of the LR Scheduler, pass along a dictionary of kwargs with the string as a tuple.
 
-.. code-block:: python
-
-    from flash.image import ImageClassifier
+.. doctest::
 
-    model = ImageClassifier(
-        num_classes=10,
-        backbone="resnet18",
-        optimizer="Adam",
-        learning_rate=1e-4,
-        lr_scheduler=("StepLR", {"step_size": 10}),
-    )
+    >>> from flash.image import ImageClassifier
+    >>> model = ImageClassifier(
+    ...     num_classes=10,
+    ...     backbone="resnet18",
+    ...     optimizer="Adam",
+    ...     learning_rate=1e-4,
+    ...     lr_scheduler=("StepLR", {"step_size": 10}),
+    ... )
+    >>> scheduler = model.configure_optimizers()[1][0]["scheduler"]
+    >>> scheduler.step_size  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    10
 
 
 An alternative to customizing the LR Scheduler using a tuple is to pass it as a callable.
 
-.. code-block:: python
-
-    from functools import partial
-    from torch.optim.lr_scheduler import CyclicLR
-    from flash.image import ImageClassifier
+.. doctest::
 
-    model = ImageClassifier(
-        num_classes=10,
-        backbone="resnet18",
-        optimizer="Adam",
-        learning_rate=1e-4,
-        lr_scheduler=partial(CyclicLR, step_size_up=1500, mode="exp_range", gamma=0.5),
-    )
+    >>> from functools import partial
+    >>> from torch.optim.lr_scheduler import CyclicLR
+    >>> from flash.image import ImageClassifier
+    >>> model = ImageClassifier(
+    ...     num_classes=10,
+    ...     backbone="resnet18",
+    ...     optimizer="SGD",
+    ...     learning_rate=1e-4,
+    ...     lr_scheduler=partial(CyclicLR, base_lr=0.001, max_lr=0.1, mode="exp_range", gamma=0.5),
+    ... )
+    >>> scheduler = model.configure_optimizers()[1][0]["scheduler"]
+    >>> (scheduler.mode, scheduler.gamma)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    ('exp_range', 0.5)
 
 
 Additionally, the ``lr_scheduler`` parameter also accepts the Lightning Scheduler configuration which can be passed on using a tuple.
@@ -136,17 +148,18 @@ When there are schedulers in which the ``.step()`` method is conditioned on a va
 Flash requires that the Lightning Scheduler configuration contains the keyword ``"monitor"`` set to the metric name that the scheduler should be conditioned on.
 Below is an example for this:
 
-.. code-block:: python
-
-    from flash.image import ImageClassifier
+.. doctest::
 
-    model = ImageClassifier(
-        num_classes=10,
-        backbone="resnet18",
-        optimizer="Adam",
-        learning_rate=1e-4,
-        lr_scheduler=("reducelronplateau", {"mode": "max"}, {"monitor": "val_accuracy"}),
-    )
+    >>> from flash.image import ImageClassifier
+    >>> model = ImageClassifier(
+    ...     num_classes=10,
+    ...     backbone="resnet18",
+    ...     optimizer="Adam",
+    ...     learning_rate=1e-4,
+    ...     lr_scheduler=("reducelronplateau", {"mode": "max"}, {"monitor": "val_accuracy"}),
+    ... )
+    >>> model.configure_optimizers()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    ([Adam ...], [{'scheduler': ..., 'monitor': 'val_accuracy', ...}])
 
 
 .. note:: Do not set the ``"scheduler"`` key in the Lightning Scheduler configuration, it will overridden with an instance of the provided scheduler key.
@@ -159,18 +172,18 @@ Flash registry also provides the flexiblty of registering functions. This featur
 
 Using the ``optimizers`` and ``lr_schedulers`` decorator pertaining to each :class:`~flash.core.model.Task`, custom optimizer and LR scheduler recipes can be pre-registered.
 
-.. code-block:: python
-
-    import torch
-    from flash.image import ImageClassifier
+.. doctest::
 
-
-    @ImageClassifier.lr_schedulers
-    def my_flash_steplr_recipe(optimizer):
-        return torch.optim.lr_scheduler.StepLR(optimizer, step_size=10)
-
-
-    model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer="Adam", lr_scheduler="my_flash_steplr_recipe")
+    >>> import torch
+    >>> from flash.image import ImageClassifier
+    >>> @ImageClassifier.lr_schedulers
+    ... def my_flash_steplr_recipe(optimizer):
+    ...     return torch.optim.lr_scheduler.StepLR(optimizer, step_size=10)
+    ...
+    >>> model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer="Adam", lr_scheduler="my_flash_steplr_recipe")
+    >>> scheduler = model.configure_optimizers()[1][0]["scheduler"]
+    >>> scheduler.step_size  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    10
 
 
 Provider specific requirements
@@ -185,13 +198,41 @@ In order to use them in Flash, just provide ``num_warmup_steps`` as float betwee
 that will be used as warmup steps. Flash's :class:`~flash.core.trainer.Trainer` will take care of computing the number of training steps and
 number of warmup steps based on the flags that are set in the Trainer.
 
-.. code-block:: python
-
-    from flash.image import ImageClassifier
-
-    model = ImageClassifier(
-        backbone="resnet18",
-        num_classes=2,
-        optimizer="Adam",
-        lr_scheduler=("cosine_schedule_with_warmup", {"num_warmup_steps": 0.1}),
-    )
+.. testsetup::
+
+    import numpy as np
+    from PIL import Image
+
+    rand_image = Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))
+    _ = [rand_image.save(f"image_{i}.png") for i in range(1, 4)]
+    _ = [rand_image.save(f"predict_image_{i}.png") for i in range(1, 4)]
+
+.. doctest::
+
+    >>> from flash import Trainer
+    >>> from flash.image import ImageClassifier, ImageClassificationData
+    >>> datamodule = ImageClassificationData.from_files(
+    ...     train_files=["image_1.png", "image_2.png", "image_3.png"],
+    ...     train_targets=["cat", "dog", "cat"],
+    ...     predict_files=["predict_image_1.png", "predict_image_2.png", "predict_image_3.png"],
+    ...     transform_kwargs=dict(image_size=(128, 128)),
+    ...     batch_size=2,
+    ... )
+    >>> model = ImageClassifier(
+    ...     backbone="resnet18",
+    ...     num_classes=datamodule.num_classes,
+    ...     optimizer="Adam",
+    ...     lr_scheduler=("cosine_schedule_with_warmup", {"num_warmup_steps": 0.1}),
+    ... )
+    >>> trainer = Trainer(fast_dev_run=True)
+    >>> trainer.fit(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    Training...
+    >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    Predicting...
+
+.. testcleanup::
+
+    import os
+
+    _ = [os.remove(f"image_{i}.png") for i in range(1, 4)]
+    _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]

flash/core/model.py

@@ -774,12 +774,6 @@ def _instantiate_lr_scheduler(self, optimizer: Optimizer) -> Dict[str, Any]:
         # 2) If return value is a dictionary, check for the lr_scheduler_config `only keys` and return the config.
         lr_scheduler: Union[_LRScheduler, Dict[str, Any]] = lr_scheduler_fn(optimizer, **lr_scheduler_kwargs)
 
-        if not isinstance(lr_scheduler, (_LRScheduler, Dict)):
-            raise MisconfigurationException(
-                f"Please make sure that your custom configuration outputs either an LR Scheduler or a scheduler"
-                f" configuration with keys belonging to {list(default_scheduler_config.keys())}."
-            )
-
         if isinstance(lr_scheduler, Dict):
             dummy_config = default_scheduler_config
             if not all(config_key in dummy_config.keys() for config_key in lr_scheduler.keys()):

tests/core/test_model.py

@@ -24,7 +24,6 @@
 import pytorch_lightning as pl
 import torch
 from pytorch_lightning.callbacks import Callback
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from torch import nn, Tensor
 from torch.nn import functional as F
 from torch.utils.data import DataLoader
@@ -443,18 +442,6 @@ def test_errors_and_exceptions_optimizers_and_schedulers():
         task = ClassificationTask(model, optimizer="Adam", lr_scheduler="not_a_valid_key")
         task.configure_optimizers()
 
-    @ClassificationTask.lr_schedulers
-    def i_will_create_a_misconfiguration_exception(optimizer):
-        return "Done. Created."
-
-    with pytest.raises(MisconfigurationException):
-        task = ClassificationTask(model, optimizer="Adam", lr_scheduler="i_will_create_a_misconfiguration_exception")
-        task.configure_optimizers()
-
-    with pytest.raises(MisconfigurationException):
-        task = ClassificationTask(model, optimizer="Adam", lr_scheduler=i_will_create_a_misconfiguration_exception)
-        task.configure_optimizers()
-
     with pytest.raises(TypeError):
         task = ClassificationTask(model, optimizer="Adam", lr_scheduler=["not", "a", "valid", "type"])
         task.configure_optimizers()
@@ -465,8 +452,6 @@ def i_will_create_a_misconfiguration_exception(optimizer):
         )
         task.configure_optimizers()
 
-    pass
-
 
 def test_classification_task_metrics():
     train_dataset = FixedDataset([0, 1])