feat: check that user callbacks are not those used by CAREamics

src/careamics/careamist.py

@@ -211,6 +211,23 @@ def _define_callbacks(self, callbacks: Optional[list[Callback]] = None) -> None:
         """
         self.callbacks = [] if callbacks is None else callbacks
 
+        # check that user callbacks are not any of the CAREamics callbacks
+        for c in self.callbacks:
+            if isinstance(c, ModelCheckpoint) or isinstance(c, EarlyStopping):
+                raise ValueError(
+                    "ModelCheckpoint and EarlyStopping callbacks are already defined "
+                    "in CAREamics and should only be modified through the "
+                    "training configuration (see TrainingConfig)."
+                )
+
+            if isinstance(c, HyperParametersCallback) or isinstance(
+                c, ProgressBarCallback
+            ):
+                raise ValueError(
+                    "HyperParameter and ProgressBar callbacks are defined internally "
+                    "and should not be passed as callbacks."
+                )
+
         # checkpoint callback saves checkpoints during training
         self.callbacks.extend(
             [

tests/test_careamist.py

@@ -4,9 +4,11 @@
 import numpy as np
 import pytest
 import tifffile
-from pytorch_lightning.callbacks import Callback
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import Callback, EarlyStopping, ModelCheckpoint
 
 from careamics import CAREamist, Configuration, save_configuration
+from careamics.callbacks import HyperParametersCallback, ProgressBarCallback
 from careamics.config.support import SupportedAlgorithm, SupportedData
 
 
@@ -740,7 +742,7 @@ def test_add_custom_callback(tmp_path, minimum_configuration):
     """Test that custom callback can be added to the CAREamist."""
 
     # define a custom callback
-    class MyPrintingCallback(Callback):
+    class MyCallback(Callback):
         def __init__(self):
             super().__init__()
 
@@ -753,13 +755,12 @@ def on_train_start(self, trainer, pl_module):
         def on_train_end(self, trainer, pl_module):
             self.has_ended = True
 
-    my_callback = MyPrintingCallback()
+    my_callback = MyCallback()
     assert not my_callback.has_started
     assert not my_callback.has_ended
 
     # training data
     train_array = random_array((32, 32))
-    val_array = random_array((32, 32))
 
     # create configuration
     config = Configuration(**minimum_configuration)
@@ -775,8 +776,46 @@ def on_train_end(self, trainer, pl_module):
     assert not my_callback.has_ended
 
     # train CAREamist
-    careamist.train(train_source=train_array, val_source=val_array)
+    careamist.train(train_source=train_array)
 
     # check the state of the callback
     assert my_callback.has_started
     assert my_callback.has_ended
+
+
+def test_error_passing_careamics_callback(tmp_path, minimum_configuration):
+    """Test that an error is thrown if we pass known callbacks to CAREamist."""
+    # create configuration
+    config = Configuration(**minimum_configuration)
+    config.training_config.num_epochs = 1
+    config.data_config.axes = "YX"
+    config.data_config.batch_size = 2
+    config.data_config.data_type = SupportedData.ARRAY.value
+    config.data_config.patch_size = (8, 8)
+
+    # Lightning callbacks
+    model_ckp = ModelCheckpoint()
+
+    with pytest.raises(ValueError):
+        CAREamist(source=config, work_dir=tmp_path, callbacks=[model_ckp])
+
+    early_stp = EarlyStopping(
+        Trainer(
+            max_epochs=1,
+            default_root_dir=tmp_path,
+        )
+    )
+
+    with pytest.raises(ValueError):
+        CAREamist(source=config, work_dir=tmp_path, callbacks=[early_stp])
+
+    # CAREamics callbacks
+    progress_bar = ProgressBarCallback()
+
+    with pytest.raises(ValueError):
+        CAREamist(source=config, work_dir=tmp_path, callbacks=[progress_bar])
+
+    hyper_params = HyperParametersCallback(config=config)
+
+    with pytest.raises(ValueError):
+        CAREamist(source=config, work_dir=tmp_path, callbacks=[hyper_params])