Datasets:

THUDM
/

ImageRewardDB

+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+import pandas as pd
+import json
+import os
+import datasets
+from huggingface_hub import hf_hub_url
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://huggingface.co/datasets/wuyuchen/ImageRewardDB"
+_VERSION = datasets.Version("1.0.0")
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+# TODO: Add link to the official dataset URLs here
+# 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 = "wuyuchen/ImageRewardDB"
+_URLS = {}
+_PART_IDS = {
+    "train": 32,
+    "validation": 2,
+    "test": 2
+}
+for name in list(_PART_IDS.keys()):
+    _URLS[name] = {}
+    for i in range(1, _PART_IDS[name]+1):
+        _URLS[name][i] = hf_hub_url(
+            _REPO_ID,
+            filename=f"images/{name}/{name}_{id}.zip",
+            repo_type="dataset"
+        )
+    _URLS[name]["metadata"] = hf_hub_url(
+        _REPO_ID,
+        filename=f"metadata-{name}.parquet",
+        repo_type="dataset"
+    )
+class ImageRewardDBConfig(datasets.BuilderConfig):
+    '''BuilderConfig for ImageRewardDB'''
+    def __init__(self, part_ids, **kwargs):
+        '''BuilderConfig for ImageRewardDB
+        Args:
+            part_ids([int]): A list of part_ids.
+            **kwargs: keyword arguments forwarded to super
+        '''
+        super(ImageRewardDBConfig, self).__init__(version=_VERSION, **kwargs)
+        self.part_ids = part_ids
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class ImageRewardDB(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = []
+    for num_k in [1,2,4,8]:
+        part_ids = {
+            "train": 4*num_k,
+            "validation": 2,
+            "test": 2
+        }
+        BUILDER_CONFIGS.append(
+            ImageRewardDBConfig(name=f"{num_k}k", part_ids=part_ids, version=_VERSION, description=f"This is a {num_k}k-scale ImageRewardDB")
+        )
+    DEFAULT_CONFIG_NAME = "8k"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        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,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        data_dirs = {name: [] for name in list(_PART_IDS.keys())}
+        json_paths = {name: [] for name in list(_PART_IDS.keys())}
+        metadata_paths = {name: [] for name in list(_PART_IDS.keys())}
+        for key in list(self.config.part_ids.keys()):
+            for i in range(1, self.config.part_ids[key]+1):
+                data_dir = dl_manager.download_and_extract(_URLS[key][i])
+                data_dirs[key].append(data_dir)
+                json_paths[key].append(os.path.join(data_dir, f"{key}_{i}.json"))
+            metadata_paths[key] = dl_manager.download(_URLS[key]["metadata"])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "split": "train",
+                    "data_dirs": data_dirs["train"],
+                    "json_paths": json_paths["train"],
+                    "metadata_path": metadata_paths["train"]
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "split": "validation",
+                    "data_dirs": data_dirs["validation"],
+                    "json_paths": json_paths["validation"],
+                    "metadata_path": metadata_paths["validation"]
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "split": "test",
+                    "data_dirs": data_dirs["test"],
+                    "json_paths": json_paths["test"],
+                    "metadata_path": metadata_paths["test"]
+                },
+            ),
+        ]
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, split, data_dirs, json_paths, metadata_path):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        num_data_dirs = len(data_dirs)
+        assert num_data_dirs == len(json_paths)
+        #Iterate throug all extracted zip folders for images
+        for index, json_path in enumerate(json_paths):
+           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"]
+               }