Add integration test

tests/intg/test_classification_timm.py

@@ -11,6 +11,7 @@
 import openvino as ov
 import pytest
 
+from openvino_xai import insert_xai
 from openvino_xai.common.parameters import Method, Task
 from openvino_xai.explainer.explainer import Explainer, ExplainMode
 from openvino_xai.explainer.utils import (
@@ -363,6 +364,7 @@ def test_ovc_model_white_box(self, model_id):
         assert explanation is not None
         assert explanation.shape[-1] > 1 and explanation.shape[-2] > 1
         print(f"{model_id}: Generated classification saliency maps with shape {explanation.shape}.")
+        self.clear_cache()
 
     @pytest.mark.parametrize(
         "model_id",
@@ -441,6 +443,61 @@ def test_model_format(self, model_id, explain_mode, model_format):
         assert explanation is not None
         assert explanation.shape[-1] > 1 and explanation.shape[-2] > 1
         print(f"{model_id}: Generated classification saliency maps with shape {explanation.shape}.")
+        self.clear_cache()
+
+    @pytest.mark.parametrize(
+        "model_id",
+        [
+            "resnet18.a1_in1k",
+            "efficientnet_b0.ra_in1k",
+            "vit_tiny_patch16_224.augreg_in21k",
+            "deit_tiny_patch16_224.fb_in1k",
+        ],
+    )
+    def test_torch_insert_xai_with_layer(self, model_id: str):
+        xai_cfg = {
+            "resnet18.a1_in1k": ("layer4", Method.RECIPROCAM),
+            "efficientnet_b0.ra_in1k": ("bn2", Method.RECIPROCAM),
+            "vit_tiny_patch16_224.augreg_in21k": ("blocks.9.norm1", Method.VITRECIPROCAM),
+            "deit_tiny_patch16_224.fb_in1k": ("blocks.9.norm1", Method.VITRECIPROCAM),
+        }
+
+        model_dir = self.data_dir / "timm_models" / "converted_models"
+        model, model_cfg = self.get_timm_model(model_id, model_dir)
+
+        image = cv2.imread("tests/assets/cheetah_person.jpg")
+        image = cv2.resize(image, dsize=model_cfg["input_size"][1:])
+        image = cv2.cvtColor(image, code=cv2.COLOR_BGR2RGB)
+        mean = np.array(model.default_cfg["mean"])
+        std = np.array(model.default_cfg["std"])
+        image_norm = (image / 255.0 - mean) / std
+        image_norm = image_norm.transpose((2, 0, 1))  # HWC -> CHW
+        image_norm = image_norm[None, :]  # CHW -> 1CHW
+        target_class = self.supported_num_classes[model_cfg["num_classes"]]
+
+        xai_model: torch.nn.Module = insert_xai(
+            model,
+            task=Task.CLASSIFICATION,
+            target_layer=xai_cfg[model_id][0],
+            explain_method=xai_cfg[model_id][1],
+        )
+
+        with torch.no_grad():
+            xai_model.eval()
+            xai_output = xai_model(torch.from_numpy(image_norm).float())
+            xai_logit = xai_output["prediction"]
+            xai_prob = torch.softmax(xai_logit, dim=-1)
+            xai_label = xai_prob.argmax(dim=-1)[0]
+        assert xai_label.item() == target_class
+        assert xai_prob[0, xai_label].item() > 0.0
+
+        saliency_map: np.ndarray = xai_output["saliency_map"].numpy(force=True)
+        saliency_map = saliency_map.squeeze(0)
+        assert saliency_map.shape[-1] > 1 and saliency_map.shape[-2] > 1
+        assert saliency_map.min() < saliency_map.max()
+        assert saliency_map.dtype == np.uint8
+
+        self.clear_cache()
 
     def check_for_saved_map(self, model_id, directory):
         for target in self.supported_num_classes.values():