Update ImageRewardDB.py

ImageRewardDB.py

@@ -107,11 +107,14 @@ class ImageRewardDB(datasets.GeneratorBasedBuilder):
             "validation": 2,
             "test": 2
         }
+        BUILDER_CONFIGS.append(
+            ImageRewardDBConfig(name=f"{num_k}k_group", part_ids=part_ids, description=f"This is a {num_k}k-scale groups of ImageRewardDB")
+        )
         BUILDER_CONFIGS.append(
             ImageRewardDBConfig(name=f"{num_k}k", part_ids=part_ids, description=f"This is a {num_k}k-scale ImageRewardDB")
         )
         BUILDER_CONFIGS.append(
-            ImageRewardDBConfig(name=f"{num_k}k_group", part_ids=part_ids, description=f"This is a {num_k}k-scale group of ImageRewardDB")
+            ImageRewardDBConfig(name=f"{num_k}k_pair", part_ids=part_ids, description=f"This is a {num_k}k-scale pairs of ImageRewardDB")
         )
 
     DEFAULT_CONFIG_NAME = "8k"  # It's not mandatory to have a default configuration. Just use one if it make sense.
@@ -130,6 +133,16 @@ class ImageRewardDB(datasets.GeneratorBasedBuilder):
                     "fidelity_rating": datasets.Sequence(datasets.Value("int8"))
                 }
             )
+        elif "pair" in self.config.name:
+            features = datasets.Features(
+                {
+                    "prompt_id": datasets.Value("string"),
+                    "prompt": datasets.Value("string"),
+                    "classification": datasets.Value("string"),
+                    "img_better": datasets.Image(),
+                    "img_worse": datasets.Image()
+                }
+            )
         else:
             features = datasets.Features(
                 {
@@ -216,10 +229,10 @@ class ImageRewardDB(datasets.GeneratorBasedBuilder):
         assert num_data_dirs == len(json_paths)
         
         #Iterate throug all extracted zip folders for images
-        # metadata_table = pd.read_parquet(metadata_path)
+        metadata_table = pd.read_parquet(metadata_path)
         for index, json_path in enumerate(json_paths):
             json_data = json.load(open(json_path, "r", encoding="utf-8"))
-            if "group" in self.config.name:
+            if "group" in self.config.name or "pair" in self.config.name:
                 group_num = 0
                 image_path = []
                 rank = []
@@ -235,8 +248,7 @@ class ImageRewardDB(datasets.GeneratorBasedBuilder):
                         prompt = sample["prompt"]
                         classification = sample["classification"]
                         image_amount_in_total = sample["image_amount_in_total"]
-                    # image_path.append(sample["image_path"])
-                    image_path.append(os.path.join(data_dirs[index], str(sample["image_path"]).split("/")[-1]))
+                    image_path.append(sample["image_path"])
                     rank.append(sample["rank"])
                     overall_rating.append(sample["overall_rating"])
                     image_text_alignment_rating.append(sample["image_text_alignment_rating"])
@@ -244,19 +256,51 @@ class ImageRewardDB(datasets.GeneratorBasedBuilder):
                     group_num += 1
                     if group_num == image_amount_in_total:
                         group_num = 0
-                        yield prompt_id, ({
-                            "prompt_id": prompt_id,
-                            "prompt": prompt,
-                            "classification": classification,
-                            "image": [{
-                                "path": image_path[idx],
-                                "bytes": open(image_path[idx], "rb").read()
-                            } for idx in range(image_amount_in_total)],
-                            "rank": rank,
-                            "overall_rating": overall_rating,
-                            "image_text_alignment_rating": image_text_alignment_rating,
-                            "fidelity_rating": fidelity_rating,
-                        })
+                        if "group" in self.config.name:
+                            yield prompt_id, ({
+                                "prompt_id": prompt_id,
+                                "prompt": prompt,
+                                "classification": classification,
+                                "image": [{
+                                    "path": image_path[idx],
+                                    "bytes": open(image_path[idx], "rb").read()
+                                } for idx in range(image_amount_in_total)],
+                                "rank": rank,
+                                "overall_rating": overall_rating,
+                                "image_text_alignment_rating": image_text_alignment_rating,
+                                "fidelity_rating": fidelity_rating,
+                            })
+                        else:
+                            for idx in range(image_amount_in_total):
+                                for idy in range(idx+1, image_amount_in_total):
+                                    if rank[idx] < rank[idy]:
+                                        yield prompt_id, ({
+                                            "prompt_id": prompt_id,
+                                            "prompt": prompt,
+                                            "classification": classification,
+                                            "img_better": {
+                                                "path": image_path[idx],
+                                                "bytes": open(image_path[idx], "rb").read()
+                                            },
+                                            "img_worse": {
+                                                "path": image_path[idy],
+                                                "bytes": open(image_path[idy], "rb").read()
+                                            }
+                                        })
+                                    elif rank[idx] > rank[idy]:
+                                        yield prompt_id, ({
+                                            "prompt_id": prompt_id,
+                                            "prompt": prompt,
+                                            "classification": classification,
+                                            "img_better": {
+                                                "path": image_path[idy],
+                                                "bytes": open(image_path[idy], "rb").read()
+                                            },
+                                            "img_worse": {
+                                                "path": image_path[idx],
+                                                "bytes": open(image_path[idx], "rb").read()
+                                            }
+                                        })
             else:
                 for example in json_data:
                     image_path = os.path.join(data_dirs[index], str(example["image_path"]).split("/")[-1])