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# coding=utf-8
# Copyright 2022 the HuggingFace Datasets Authors.
#
# 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.
import os
import pandas as pd
import datasets
import json
import ast
from huggingface_hub import hf_hub_url
_INPUT_CSV = "flickr_annotations_30k.csv"
_INPUT_IMAGES = "flickr30k-images"
_REPO_ID = "robinhad/flickr-test"
_JSON_KEYS = ["raw", "sentids"]
class Dataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="TEST", version=VERSION, description="test"),
]
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"image": datasets.Image(),
"caption": [datasets.Value("string")],
"sentids": [datasets.Value("string")],
"split": datasets.Value("string"),
"img_id": datasets.Value("string"),
"filename": datasets.Value("string"),
}
),
# task_templates=[],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
repo_id = _REPO_ID
data_dir = dl_manager.download_and_extract(
{
"examples_csv": hf_hub_url(
repo_id=repo_id, repo_type="dataset", filename=_INPUT_CSV
),
"images_dir": hf_hub_url(
repo_id=repo_id,
repo_type="dataset",
filename=f"{_INPUT_IMAGES}.zip",
),
}
)
return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]
def _generate_examples(self, examples_csv, images_dir):
"""Yields examples."""
df = pd.read_csv(examples_csv)
for c in _JSON_KEYS:
df[c] = df[c].apply(ast.literal_eval)
for r_idx, r in df.iterrows():
r_dict = r.to_dict()
image_path = os.path.join(images_dir, _INPUT_IMAGES, r_dict["filename"])
r_dict["image"] = image_path
r_dict["caption"] = r_dict.pop("raw")
yield r_idx, r_dict
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