Merge pull request #83 from orobix/feature/upgrade_anomalib_126

Feature/upgrade anomalib 126

CHANGELOG.md

@@ -2,6 +2,16 @@
 # Changelog
 All notable changes to this project will be documented in this file.
 
+### [1.3.6]
+
+#### Fixed
+
+- Changed matplotlib backend in anomaly visualizer to solve slowdown on some devices
+
+#### Updated
+
+- Update anomalib to [v0.7.0+obx.1.2.6] (Efficient_ad now keeps maps always on gpu during forward)
+
 ### [1.3.5]
 
 #### Fixed

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "quadra"
-version = "1.3.5"
+version = "1.3.6"
 description = "Deep Learning experiment orchestration library"
 authors = [
   { name = "Alessandro Polidori", email = "[email protected]" },
@@ -66,7 +66,7 @@ dependencies = [
   "h5py==3.8.*",
   "timm==0.6.12", # required by smp
   "segmentation-models-pytorch==0.3.2",
-  "anomalib@git+https://github.com/orobix/[email protected]+obx.1.2.5",
+  "anomalib@git+https://github.com/orobix/[email protected]+obx.1.2.6",
   "xxhash==3.2.*",
 ]
 
@@ -121,7 +121,7 @@ repository = "https://github.com/orobix/quadra"
 
 # Adapted from https://realpython.com/pypi-publish-python-package/#version-your-package
 [tool.bumpver]
-current_version = "1.3.5"
+current_version = "1.3.6"
 version_pattern = "MAJOR.MINOR.PATCH"
 commit_message = "build: Bump version {old_version} -> {new_version}"
 commit = true

quadra/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.3.5"
+__version__ = "1.3.6"
 
 
 def get_version():

quadra/callbacks/anomalib.py

@@ -47,6 +47,8 @@ def add_image(self, image: np.ndarray, title: str, color_map: Optional[str] = No
 
     def generate(self):
         """Generate the image."""
+        default_plt_backend = plt.get_backend()
+        plt.switch_backend("Agg")
         num_cols = len(self.images)
         figure_size = (num_cols * 3, 3)
         self.figure, self.axis = plt.subplots(1, num_cols, figsize=figure_size)
@@ -58,6 +60,7 @@ def generate(self):
             axis.axes.yaxis.set_visible(False)
             axis.imshow(image_dict["image"], image_dict["color_map"], vmin=0, vmax=255)
             axis.title.set_text(image_dict["title"])
+        plt.switch_backend(default_plt_backend)
 
     def show(self):
         """Show image on a matplotlib figure."""
@@ -168,11 +171,17 @@ def on_test_batch_end(
             normalize = True  # raw anomaly maps. Still need to normalize
 
         if self.threshold_type == "pixel":
-            threshold = pl_module.pixel_metrics.F1Score.threshold
+            if hasattr(pl_module.pixel_metrics.F1Score, "threshold"):
+                threshold = pl_module.pixel_metrics.F1Score.threshold
+            else:
+                raise AttributeError("Metric has no threshold attribute")
         else:
-            threshold = pl_module.image_metrics.F1Score.threshold
+            if hasattr(pl_module.image_metrics.F1Score, "threshold"):
+                threshold = pl_module.image_metrics.F1Score.threshold
+            else:
+                raise AttributeError("Metric has no threshold attribute")
 
-        for (filename, image, true_mask, anomaly_map, gt_label, pred_label, anomaly_score) in tqdm(
+        for filename, image, true_mask, anomaly_map, gt_label, pred_label, anomaly_score in tqdm(
             zip(
                 outputs["image_path"],
                 outputs["image"],
@@ -193,7 +202,10 @@ def on_test_batch_end(
                 continue
 
             heat_map = superimpose_anomaly_map(anomaly_map, image, normalize=normalize)
-            pred_mask = compute_mask(anomaly_map, threshold)
+            if isinstance(threshold, float):
+                pred_mask = compute_mask(anomaly_map, threshold)
+            else:
+                raise TypeError("Threshold should be float")
             vis_img = mark_boundaries(image, pred_mask, color=(1, 0, 0), mode="thick")
             visualizer = Visualizer()