Add error on missing module to NoTrainInceptionV3 (#2143)

(cherry picked from commit c08fe0b)

CHANGELOG.md

@@ -13,6 +13,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 -
 
+- Added error if `NoTrainInceptionV3` is being initialized without `torch-fidelity` not being installed ([#2143](https://github.com/Lightning-AI/torchmetrics/pull/2143))
+
+
 ### Changed
 
 - Change default state of `SpectralAngleMapper` and `UniversalImageQualityIndex` to be tensors ([#2089](https://github.com/Lightning-AI/torchmetrics/pull/2089))

src/torchmetrics/image/fid.py

@@ -55,6 +55,12 @@ def __init__(
         features_list: List[str],
         feature_extractor_weights_path: Optional[str] = None,
     ) -> None:
+        if not _TORCH_FIDELITY_AVAILABLE:
+            raise ModuleNotFoundError(
+                "NoTrainInceptionV3 module requires that `Torch-fidelity` is installed."
+                " Either install as `pip install torchmetrics[image]` or `pip install torch-fidelity`."
+            )
+
         super().__init__(name, features_list, feature_extractor_weights_path)
         # put into evaluation mode
         self.eval()

tests/unittests/image/test_fid.py

@@ -19,12 +19,21 @@
 import torch
 from torch.nn import Module
 from torch.utils.data import Dataset
-from torchmetrics.image.fid import FrechetInceptionDistance
+from torchmetrics.image.fid import FrechetInceptionDistance, NoTrainInceptionV3
 from torchmetrics.utilities.imports import _TORCH_FIDELITY_AVAILABLE, _TORCH_GREATER_EQUAL_1_9
 
 torch.manual_seed(42)
 
 
+@pytest.mark.skipif(_TORCH_FIDELITY_AVAILABLE, reason="test only works if torch-fidelity is not installed")
+def test_no_train_network_missing_torch_fidelity():
+    """Assert that NoTrainInceptionV3 raises an error if torch-fidelity is not installed."""
+    with pytest.raises(
+        ModuleNotFoundError, match="NoTrainInceptionV3 module requires that `Torch-fidelity` is installed.*"
+    ):
+        NoTrainInceptionV3(name="inception-v3-compat", features_list=["2048"])
+
+
 @pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_9, reason="test requires torch>=1.9")
 @pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="test requires torch-fidelity")
 def test_no_train():