Merge pull request #43 from ENSTA-U2IS/dev

🐛 Fix docs & Add docs check before merge

Author: o-laurent

.github/workflows/build-docs.yml

@@ -3,6 +3,9 @@ on:
   push:
     branches:
       - main
+  pull_request:
+    branches:
+      - main
   schedule:
     - cron: "00 12 * * 0" # Every Sunday noon (preserve the cache folders)
   workflow_dispatch:
@@ -61,6 +64,7 @@ jobs:
 
     - name: Deploy
       uses: peaceiris/actions-gh-pages@v3
+      if: ${{ github.event_name != 'pull_request' }}
       with:
         deploy_key: ${{ secrets.ACTIONS_DEPLOY_KEY }}
         external_repository: torch-uncertainty/torch-uncertainty.github.io

.github/workflows/run-tests.yml

@@ -6,6 +6,9 @@ on:
       - main
       - dev
   pull_request:
+    branches:
+      - main
+      - dev
   schedule:
     - cron: "42 7 * * 0"
   workflow_dispatch:

auto_tutorials_source/tutorial_bayesian.py

@@ -84,9 +84,10 @@ def optim_lenet(model: nn.Module) -> dict:
 # We mock the arguments for the trainer
 with ArgvContext(
     "file.py",
-    "--max_epochs 1",
-    "--enable_progress_bar=False",
-    "--verbose=False",
+    "--max_epochs",
+    "1",
+    "--enable_progress_bar",
+    "False",
 ):
     args = init_args(datamodule=MNISTDataModule)
 

auto_tutorials_source/tutorial_scaler.py

@@ -71,15 +71,16 @@
 #
 # When computing the ECE, you need to provide the likelihoods associated with the inputs.
 # To do this, just call PyTorch's softmax.
+#
+# To avoid lengthy computations (without GPU), we restrict the calibration computation to a subset
+# of the test set.
 
 from torch.utils.data import DataLoader, random_split
 
 # Split datasets
 dataset = dm.test
-cal_dataset, test_dataset = random_split(dataset, [1000, len(dataset) - 1000])
-cal_dataloader, test_dataloader = DataLoader(cal_dataset, batch_size=32), DataLoader(
-    test_dataset, batch_size=32
-)
+cal_dataset, test_dataset, other = random_split(dataset, [1000, 1000, len(dataset) - 2000])
+test_dataloader = DataLoader(test_dataset, batch_size=32)
 
 # Initialize the ECE
 ece = CalibrationError(task="multiclass", num_classes=100)
@@ -105,7 +106,7 @@
 
 # Fit the scaler on the calibration dataset
 scaler = TemperatureScaler()
-scaler = scaler.fit(model=model, calib_loader=cal_dataloader)
+scaler = scaler.fit(model=model, calibration_set=cal_dataset)
 
 # %%
 # 6. Iterating Again to Compute the Improved ECE

docs/source/api.rst

@@ -194,7 +194,6 @@ Metrics
     BrierScore
     Disagreement
     Entropy
-    JensenShannonDivergence
     MutualInformation
     NegativeLogLikelihood