test mAP metic

maestro/trainer/models/florence_2/metrics.py

@@ -92,14 +92,13 @@ def get_ground_truths_and_predictions(
     dataset: DetectionDataset,
     processor: AutoProcessor,
     model: AutoModelForCausalLM,
-    split_name: str,
     device: torch.device,
 ) -> Tuple[List[sv.Detections], List[sv.Detections], List[str]]:
     classes = extract_classes(dataset=dataset)
     targets = []
     predictions = []
     post_processed_text_outputs = []
-    for idx in tqdm(list(range(len(dataset))), desc=f"Generating {split_name} predictions..."):
+    for idx in tqdm(list(range(len(dataset))), desc="Generating predictions..."):
         image, data = dataset.dataset[idx]
         prefix = data["prefix"]
         suffix = data["suffix"]

maestro/trainer/models/florence_2/training.py

@@ -19,6 +19,7 @@
     save_metric_plots, BaseMetric
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
 from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
+from maestro.trainer.models.florence_2.metrics import MeanAveragePrecisionMetric, get_ground_truths_and_predictions
 from maestro.trainer.models.paligemma.training import LoraInitLiteral
 
 DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
@@ -340,6 +341,29 @@ def run_validation_epoch(
         )
         print(f"Average Validation Loss: {avg_val_loss}")
 
+        # TODO: standardize the calculation of metrics input to run inference only once
+
+        for metric in configuration.metrics:
+            if isinstance(metric, MeanAveragePrecisionMetric):
+                targets, predictions, _ = get_ground_truths_and_predictions(
+                    dataset=loader.dataset,
+                    processor=processor,
+                    model=model,
+                    device=configuration.device,
+                )
+                map_result = metric.compute(targets=targets, predictions=predictions)
+                for map_key, map_value in map_result.items():
+                    metrics_tracker.register(
+                        metric=map_key,
+                        epoch=epoch_number,
+                        step=1,
+                        value=map_value,
+                    )
+                    print(f"Validation {map_key}: {map_value:.4f}")
+            else:
+                # Handle other metric types
+                pass
+
 
 def save_model(
     target_dir: str,