Upload ImageRewardDB.py with huggingface_hub

ImageRewardDB.py

@@ -43,14 +43,14 @@ To build the ImageRewadDB, we design a pipeline tailored for it, establishing cr
 annotator training, optimizing labeling experience, and ensuring quality validation. \
 """
 
-_HOMEPAGE = "https://huggingface.co/datasets/THUDM/ImageRewardDB"
+_HOMEPAGE = "https://huggingface.co/datasets/wuyuchen/ImageRewardDB"
 _VERSION = datasets.Version("1.0.0")
 
 _LICENSE = "Apache License 2.0"
 
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_REPO_ID = "THUDM/ImageRewardDB"
+_REPO_ID = "wuyuchen/ImageRewardDB"
 _URLS = {}
 _PART_IDS = {
     "train": 32,
@@ -107,56 +107,26 @@ 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_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.
 
     def _info(self):
-        if "group" in self.config.name:
-            features = datasets.Features(
-                {
-                    "prompt_id": datasets.Value("string"),
-                    "prompt": datasets.Value("string"),
-                    "classification": datasets.Value("string"),
-                    "image": datasets.Sequence(datasets.Image()),
-                    "rank": datasets.Sequence(datasets.Value("int8")),
-                    "overall_rating": datasets.Sequence(datasets.Value("int8")),
-                    "image_text_alignment_rating": datasets.Sequence(datasets.Value("int8")),
-                    "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(
-                {
-                    "image": datasets.Image(),
-                    "prompt_id": datasets.Value("string"),
-                    "prompt": datasets.Value("string"),
-                    "classification": datasets.Value("string"),
-                    "image_amount_in_total": datasets.Value("int8"),
-                    "rank": datasets.Value("int8"),
-                    "overall_rating": datasets.Value("int8"),
-                    "image_text_alignment_rating": datasets.Value("int8"),
-                    "fidelity_rating": datasets.Value("int8")
-                }
-            )
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "prompt_id": datasets.Value("string"),
+                "prompt": datasets.Value("string"),
+                "classification": datasets.Value("string"),
+                "image_amount_in_total": datasets.Value("int8"),
+                "rank": datasets.Value("int8"),
+                "overall_rating": datasets.Value("int8"),
+                "image_text_alignment_rating": datasets.Value("int8"),
+                "fidelity_rating": datasets.Value("int8")
+            }
+        )
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
@@ -229,93 +199,22 @@ 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 or "pair" in self.config.name:
-                group_num = 0
-                image_path = []
-                rank = []
-                overall_rating, image_text_alignment_rating, fidelity_rating = [], [], []
-                for sample in json_data:
-                    if group_num == 0:
-                        image_path.clear()
-                        rank.clear()
-                        overall_rating.clear()
-                        image_text_alignment_rating.clear()
-                        fidelity_rating.clear()
-                        prompt_id = sample["prompt_id"]
-                        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]))
-                    rank.append(sample["rank"])
-                    overall_rating.append(sample["overall_rating"])
-                    image_text_alignment_rating.append(sample["image_text_alignment_rating"])
-                    fidelity_rating.append(sample["fidelity_rating"])
-                    group_num += 1
-                    if group_num == image_amount_in_total:
-                        group_num = 0
-                        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])
-                    yield example["image_path"], {
-                        "image": {
-                            "path": image_path,
-                            "bytes": open(image_path, "rb").read()
-                        },
-                        "prompt_id": example["prompt_id"],
-                        "prompt": example["prompt"],
-                        "classification": example["classification"],
-                        "image_amount_in_total": example["image_amount_in_total"],
-                        "rank": example["rank"],
-                        "overall_rating": example["overall_rating"],
-                        "image_text_alignment_rating": example["image_text_alignment_rating"],
-                        "fidelity_rating": example["fidelity_rating"]
-                    }
+           json_data = json.load(open(json_path, "r", encoding="utf-8"))
+           for example in json_data:
+               image_path = os.path.join(data_dirs[index], str(example["image_path"]).split("/")[-1])
+               yield example["image_path"], {
+                   "image": {
+                       "path": image_path,
+                       "bytes": open(image_path, "rb").read()
+                   },
+                   "prompt_id": example["prompt_id"],
+                   "prompt": example["prompt"],
+                   "classification": example["classification"],
+                   "image_amount_in_total": example["image_amount_in_total"],
+                   "rank": example["rank"],
+                   "overall_rating": example["overall_rating"],
+                   "image_text_alignment_rating": example["image_text_alignment_rating"],
+                   "fidelity_rating": example["fidelity_rating"]
+               }