feat(models): for similarity, support transformers model

xaitk_saliency_demo/app/config.py

@@ -8,6 +8,10 @@
         # Task => model
         "model_active": "SimilarityResNet50",
         "model_available": [
+            {
+                "title": "microsoft/resnet-50",
+                "value": "transformers:similarity:microsoft/resnet-50",
+            },
             {"title": "ResNet-50", "value": "SimilarityResNet50"},
             {"title": "AlexNet", "value": "SimilarityAlexNet"},
             {"title": "VGG-16", "value": "SimilarityVgg16"},
@@ -39,15 +43,15 @@
             {"title": "Sliding Window Stack", "value": "SlidingWindowStack"},
         ],
         # Task => model
-        "model_active": "transformers:microsoft/resnet-50",
+        "model_active": "transformers:classification:microsoft/resnet-50",
         "model_available": [
             {
                 "title": "microsoft/resnet-50",
-                "value": "transformers:microsoft/resnet-50",
+                "value": "transformers:classification:microsoft/resnet-50",
             },
             {
                 "title": "google/vit-base-patch16-224",
-                "value": "transformers:google/vit-base-patch16-224",
+                "value": "transformers:classification:google/vit-base-patch16-224",
             },
             {"title": "ResNet-50", "value": "ClassificationResNet50"},
             {"title": "AlexNet", "value": "ClassificationAlexNet"},

xaitk_saliency_demo/app/ml/models.py

@@ -120,10 +120,18 @@ class TransformersModel(AbstractModel):
     def is_transformers_model(model_name: str) -> bool:
         return model_name.startswith(TransformersModel.TRANSFORMERS_PREFIX)
 
+    @staticmethod
+    def get_task(model_name: str) -> str:
+        return model_name.split(":")[1]
+
+    @staticmethod
+    def get_hub_id(model_name: str) -> str:
+        return model_name.split(":")[-1]
+
     def __init__(self, server, model_name, device=None):
         if device is None:
             device = DEVICE
-        hub_id = model_name[len(self.TRANSFORMERS_PREFIX) :]
+        hub_id = TransformersModel.get_hub_id(model_name)
         model = pipeline(
             model=hub_id,
             device=device,
@@ -252,6 +260,25 @@ def __init__(self, server):
         )
 
 
+class SimilarityTransformers(TransformersModel, SimilarityRun):
+    def __init__(self, server, model_name):
+        super().__init__(server, model_name)
+        self._model.model.config.output_hidden_states = True
+
+    def predict(self, input) -> np.ndarray:
+        image = Image.fromarray(input)
+        processed = self._model.image_processor(images=image, return_tensors="pt")
+        device = next(self._model.model.parameters()).device
+        processed = {
+            k: (v.to(device) if hasattr(v, "to") else v) for k, v in processed.items()
+        }
+        outputs = self._model.model(**processed)
+
+        feature_descriptor = outputs.hidden_states[-1].mean(dim=1)
+
+        return feature_descriptor[0].cpu().detach().numpy().flatten()
+
+
 # -----------------------------------------------------------------------------
 # Detection
 # -----------------------------------------------------------------------------
@@ -349,7 +376,13 @@ def get_model(server, model_name):
         return MODEL_INSTANCES[server][model_name]
 
     if TransformersModel.is_transformers_model(model_name):
-        model = TransformersClassificationModel(server, model_name)
+        task = model_name.split(":")[1]
+        if task == "classification":
+            model = TransformersClassificationModel(server, model_name)
+        elif task == "similarity":
+            model = SimilarityTransformers(server, model_name)
+        else:
+            raise ValueError(f"Unknown transformers task: {task}")
     else:
         model = globals()[model_name](server)
     MODEL_INSTANCES[server][model_name] = model

xaitk_saliency_demo/app/ml/xai.py

@@ -124,6 +124,22 @@ def get_config(self) -> Dict[str, Any]:
         return {}
 
 
+class TransformersDescrModel(ImageDescriptorGenerator):
+    def __init__(self, model):
+        self.model = model
+
+    @torch.no_grad()
+    def generate_arrays_from_images(
+        self, img_mat_iter: Iterable[np.ndarray]
+    ) -> Iterable[np.ndarray]:
+        for img in img_mat_iter:
+            yield self.model.predict(img).squeeze()
+
+    def get_config(self) -> Dict[str, Any]:
+        # Required by a parent class. Will not be used in this context.
+        return {}
+
+
 # -----------------------------------------------------------------------------
 class Saliency:
     def __init__(self, model, name, params):
@@ -160,7 +176,12 @@ def run(self, input, *_):
 class SimilaritySaliency(Saliency):
     def run(self, reference, query):
         self._saliency.fill = FILL
-        sal = self._saliency(reference, [query], DescrModel(self._model))
+        model = (
+            DescrModel(self._model)
+            if not isinstance(self._model, TransformersModel)
+            else TransformersDescrModel(self._model)
+        )
+        sal = self._saliency(reference, [query], model)
         return {
             "type": "similarity",
             "saliency": sal,