Fix disable validation

docs/notebooks/tutorials/intro_3_loss_optim.ipynb

@@ -450,6 +450,35 @@
     "\n",
     "# After the training is over the metrics can be accessed with the dictionary .metrics"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Disable validation loop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to disable the validation loop you need to:\n",
+    "1. tell the `DictModule` not to split the dataset, with `lengths=[1.0]`\n",
+    "2. pass the two options below to the `lightning.trainer`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from mlcolvar.data import DictModule\n",
+    "\n",
+    "#datamodule = DictModule(dataset,lengths=[1.0])\n",
+    "\n",
+    "trainer = lightning.Trainer(limit_val_batches=0, num_sanity_val_steps=0)"
+   ]
   }
  ],
  "metadata": {

mlcolvar/data/datamodule.py

@@ -170,6 +170,10 @@ def train_dataloader(self):
     def val_dataloader(self):
         """Return validation dataloader."""
         self._check_setup()
+        if len(self.lengths) < 2:
+            raise NotImplementedError(
+                "Validation dataset not available, you need to pass two lengths to datamodule."
+            )
         if self.valid_loader is None:
             self.valid_loader = DictLoader(
                 self._dataset_split[1],
@@ -182,7 +186,7 @@ def test_dataloader(self):
         """Return test dataloader."""
         self._check_setup()
         if len(self.lengths) < 3:
-            raise ValueError(
+            raise NotImplementedError(
                 "Test dataset not available, you need to pass three lengths to datamodule."
             )
         if self.test_loader is None:
@@ -202,7 +206,8 @@ def teardown(self, stage: str):
     def __repr__(self) -> str:
         string = f"DictModule(dataset -> {self.dataset.__repr__()}"
         string += f",\n\t\t     train_loader -> DictLoader(length={self.lengths[0]}, batch_size={self.batch_size[0]}, shuffle={self.shuffle[0]})"
-        string += f",\n\t\t     valid_loader -> DictLoader(length={self.lengths[1]}, batch_size={self.batch_size[1]}, shuffle={self.shuffle[1]})"
+        if len(self.lengths) >= 2:
+            string += f",\n\t\t     valid_loader -> DictLoader(length={self.lengths[1]}, batch_size={self.batch_size[1]}, shuffle={self.shuffle[1]})"
         if len(self.lengths) >= 3:
             string += f",\n\t\t\ttest_loader =DictLoader(length={self.lengths[2]}, batch_size={self.batch_size[2]}, shuffle={self.shuffle[2]})"
         string += f")"

mlcolvar/tests/test_utils_data_datamodule.py

@@ -25,7 +25,7 @@
 # =============================================================================
 
 
-@pytest.mark.parametrize("lengths", [[0.8, 0.2], [0.7, 0.2, 0.1]])
+@pytest.mark.parametrize("lengths", [[1.0], [0.8, 0.2], [0.7, 0.2, 0.1]])
 @pytest.mark.parametrize("fields", [[], ["labels", "weights"]])
 @pytest.mark.parametrize("random_split", [True, False])
 def test_dictionary_data_module_split(lengths, fields, random_split):
@@ -75,7 +75,7 @@ def test_dictionary_data_module_split(lengths, fields, random_split):
 
     # An error is raised if the length of the test set has not been specified.
     if len(lengths) < 3:
-        with pytest.raises(ValueError, match="you need to pass three lengths"):
+        with pytest.raises(NotImplementedError, match="you need to pass three lengths"):
             datamodule.test_dataloader()
 
 

mlcolvar/utils/trainer.py

@@ -26,7 +26,7 @@ def __init__(self):
         super().__init__()
         self.metrics = {"epoch": []}
 
-    def on_validation_epoch_end(self, trainer, pl_module):
+    def on_train_epoch_end(self, trainer, pl_module):
         metrics = trainer.callback_metrics
         if not trainer.sanity_checking:
             self.metrics["epoch"].append(trainer.current_epoch)