Fix icevision task metric names (#1252)

Co-authored-by: Kushashwa Ravi Shrimali <kushashwaravishrimali@gmail.com>

CHANGELOG.md

@@ -12,7 +12,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
-<<<<<<< HEAD
 - Fixed examples (question answering), where NLTK's `punkt` module needs to be downloaded first. ([#1215](https://github.com/PyTorchLightning/lightning-flash/pull/1215/files))
 - Fixed normalizing inputs to video classification ([#1213](https://github.com/PyTorchLightning/lightning-flash/pull/1213))
 - Fixed a bug where `pretraining_transforms` in the `ImageEmbedder` was never called. ([1196](https://github.com/PyTorchLightning/lightning-flash/pull/1196))
@@ -21,6 +20,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed DDP spawn support for `ObjectDetector`, `InstanceSegmentation`, and `KeypointDetector` ([#1222](https://github.com/PyTorchLightning/lightning-flash/pull/1222))
 - Fixed a bug where `InstanceSegmentation` would fail if samples had an inconsistent number of bboxes, labels, and masks (these will now be treated as negative samples) ([#1222](https://github.com/PyTorchLightning/lightning-flash/pull/1222))
 - Fixed a bug where collate functions were never called in the `ImageEmbedder` class. ([#1217](https://github.com/PyTorchLightning/lightning-flash/pull/1217))
+- Fixed a bug where `ObjectDetector`, `InstanceSegmentation`, and `KeypointDetector` would log train and validation metrics with the same name ([#1252](https://github.com/PyTorchLightning/lightning-flash/pull/1252))
 
 ### Removed
 

flash/core/data/utils.py

@@ -39,6 +39,7 @@
     RunningStage.VALIDATING: "val",
     RunningStage.PREDICTING: "predict",
     RunningStage.SERVING: "serve",
+    RunningStage.SANITY_CHECKING: "val",
 }
 _STAGES_PREFIX_VALUES = {"train", "test", "val", "predict", "serve"}
 

flash/core/integrations/icevision/adapter.py

@@ -40,8 +40,8 @@ class SimpleCOCOMetric(COCOMetric):
     def finalize(self) -> Dict[str, float]:
         logs = super().finalize()
         return {
-            "Precision (IoU=0.50:0.95,area=all)": logs["AP (IoU=0.50:0.95) area=all"],
-            "Recall (IoU=0.50:0.95,area=all,maxDets=100)": logs["AR (IoU=0.50:0.95) area=all maxDets=100"],
+            "precision (IoU=0.50:0.95,area=all)": logs["AP (IoU=0.50:0.95) area=all"],
+            "recall (IoU=0.50:0.95,area=all,maxDets=100)": logs["AR (IoU=0.50:0.95) area=all maxDets=100"],
         }
 
 

flash/core/integrations/icevision/backbones.py

@@ -15,24 +15,27 @@
 
 from torch import nn
 
+from flash.core.data.utils import _STAGES_PREFIX
 from flash.core.registry import FlashRegistry
 from flash.core.utilities.imports import _ICEVISION_AVAILABLE
 
 if _ICEVISION_AVAILABLE:
     from icevision.backbones import BackboneConfig
 
 
-def _log_with_prog_bar_override(self, name, value, **kwargs):
+def _log_with_name_and_prog_bar_override(self, name, value, **kwargs):
     if "prog_bar" not in kwargs:
         kwargs["prog_bar"] = True
-    return self._original_log(name.split("/")[-1], value, **kwargs)
+    metric = name.split("/")[-1]
+    metric = f"{_STAGES_PREFIX[self.trainer.state.stage]}_{metric}"
+    return self._original_log(metric, value, **kwargs)
 
 
 def icevision_model_adapter(model_type):
     adapter = model_type.lightning.ModelAdapter
     if not hasattr(adapter, "_original_log"):
         adapter._original_log = adapter.log
-        adapter.log = _log_with_prog_bar_override
+        adapter.log = _log_with_name_and_prog_bar_override
     return adapter