update: docstring

hemm/metrics/spatial_relationship/spatial_relationship_2d.py

@@ -11,35 +11,32 @@ class SpatialRelationshipMetric2D(weave.Scorer):
     """Spatial relationship metric for image generation as proposed in Section 4.2 from the paper
     [T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation](https://arxiv.org/pdf/2307.06350).
 
-    ??? example "Sample usage"
+    !!! example "Sample usage"
         ```python
-        import wandb
+        import asyncio
         import weave
 
-        from hemm.eval_pipelines import BaseDiffusionModel, EvaluationPipeline
-        from hemm.metrics.image_quality import LPIPSMetric, PSNRMetric, SSIMMetric
+        from hemm.models import DiffusersModel
+        from hemm.metrics.spatial_relationship.judges import DETRSpatialRelationShipJudge
 
-        # Initialize Weave and WandB
-        wandb.init(project="image-quality-leaderboard", job_type="evaluation")
+        # Initialize Weave
         weave.init(project_name="image-quality-leaderboard")
 
-        # Initialize the diffusion model to be evaluated as a `weave.Model` using `BaseWeaveModel`
-        model = BaseDiffusionModel(diffusion_model_name_or_path="CompVis/stable-diffusion-v1-4")
-
-        # Add the model to the evaluation pipeline
-        evaluation_pipeline = EvaluationPipeline(model=model)
+        # Initialize the diffusion model to be evaluated as a `weave.Model`
+        model = DiffusersModel(diffusion_model_name_or_path="CompVis/stable-diffusion-v1-4")
 
         # Define the judge model for 2d spatial relationship metric
         judge = DETRSpatialRelationShipJudge(
             model_address=detr_model_address, revision=detr_revision
         )
 
-        # Add 2d spatial relationship Metric to the evaluation pipeline
+        # Define 2d spatial relationship Metric to the evaluation pipeline
         metric = SpatialRelationshipMetric2D(judge=judge, name="2d_spatial_relationship_score")
-        evaluation_pipeline.add_metric(metric)
 
         # Evaluate!
-        evaluation_pipeline(dataset="t2i_compbench_spatial_prompts:v0")
+        dataset = weave.ref("2d-spatial-t2i_compbench_spatial_prompts-mscoco:v0").get()
+        evaluation = weave.Evaluation(dataset=dataset, scorers=[metric])
+        asyncio.run(evaluation.evaluate(model))
         ```
 
     Args: