Delete VGGFace2.py

VGGFace2.py

@@ -1,186 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Datasets Authors and ProgramComputer.
-#
-# 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.
-
-# Lint as: python3
-"""VGGFace2 audio-visual human speech dataset."""
-
-import json
-import os
-import re
-from urllib.parse import urlparse, parse_qs
-from getpass import getpass
-from hashlib import sha256
-from itertools import repeat
-from multiprocessing import Manager, Pool, Process
-from pathlib import Path
-from shutil import copyfileobj
-from warnings import catch_warnings, filterwarnings
-from urllib3.exceptions import InsecureRequestWarning
-
-import pandas as pd
-import requests
-
-import datasets
-
-_DESCRIPTION = "VGGFace2 is a large-scale face recognition dataset. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession."
-_CITATION = """\
-@article{DBLP:journals/corr/abs-1710-08092,
-  author       = {Qiong Cao and
-                  Li Shen and
-                  Weidi Xie and
-                  Omkar M. Parkhi and
-                  Andrew Zisserman},
-  title        = {VGGFace2: {A} dataset for recognising faces across pose and age},
-  journal      = {CoRR},
-  volume       = {abs/1710.08092},
-  year         = {2017},
-  url          = {http://arxiv.org/abs/1710.08092},
-  eprinttype    = {arXiv},
-  eprint       = {1710.08092},
-  timestamp    = {Wed, 04 Aug 2021 07:50:14 +0200},
-  biburl       = {https://dblp.org/rec/journals/corr/abs-1710-08092.bib},
-  bibsource    = {dblp computer science bibliography, https://dblp.org}
-}
-"""
-
-
-
-_URLS = {
-    "default": {
-        "train": "https://huggingface.co/datasets/ProgramComputer/VGGFace2/resolve/main/data/vggface2_train.tar.gz",
-        "test": "https://huggingface.co/datasets/ProgramComputer/VGGFace2/resolve/main/data/vggface2_test.tar.gz",
-    }
-}
-
-
-
-class VGGFace2(datasets.GeneratorBasedBuilder):
-    """VGGFace2 is dataset contains faces from Google Search"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig( version=VERSION
-        )
-    ]
-
-    def _info(self):
-        features = {
-            "image": datasets.Image(),
-            "image_id": datasets.Value("string"),
-            "class_id": datasets.Value("string"),
-            "identity": datasets.Value("string"),
-            'gender': datasets.Value("string"),
-            'sample_num':datasets.Value("uint64"), 
-            'flag':datasets.Value("bool"),
-            "male": datasets.Value("bool"),
-            "black_hair": datasets.Value("bool"),
-            "gray_hair": datasets.Value("bool"),
-            "blond_hair": datasets.Value("bool"),
-            "long_hair": datasets.Value("bool"),
-            "mustache_or_beard": datasets.Value("bool"),
-            "wearing_hat": datasets.Value("bool"),
-            "eyeglasses": datasets.Value("bool"),
-            "sunglasses": datasets.Value("bool"),
-            "mouth_open": datasets.Value("bool"),
-        }
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            supervised_keys=datasets.info.SupervisedKeysData("file", "class_id"),
-            features=datasets.Features(features),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        targets = (
-            ["01-Male.txt", "02-Black_Hair.txt","03-Brown_Hair.txt","04-Gray_Hair.txt","05-Blond_Hair.txt","06-Long_Hair.txt","07-Mustache_or_Beard.txt","08-Wearing_Hat.txt","09-Eyeglasses.txt","10-Sunglasses.txt","11-Mouth_Open.txt"]
-        )
-        target_dict =   dict(
-                (
-                    re.sub(r"^\d+-|\.txt$","",target),
-                    f"https://raw.githubusercontent.com/ox-vgg/vgg_face2/master/attributes/{target}",
-                )
-                for target in targets
-            )
-        target_dict['identity'] = "https://huggingface.co/datasets/ProgramComputer/VGGFace2/raw/main/meta/identity_meta.csv"
-        metadata = dl_manager.download(
-            target_dict
-        )
-
-        mapped_paths_train = dl_manager.iter_archive( 
-                        _URLS["default"]["train"]
-        )
-        mapped_paths_test = dl_manager.iter_archive( 
-                        _URLS["default"]["test"]
-        )
-        return [
-            datasets.SplitGenerator(
-                name="train",
-                gen_kwargs={
-                    "paths": mapped_paths_train,
-                    "meta_paths": metadata,
-                },
-            ),
-            datasets.SplitGenerator(
-                name="test",
-                gen_kwargs={
-                    "paths": mapped_paths_test,
-                    "meta_paths": metadata,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, paths, meta_paths):
-        key = 0
-        meta = pd.read_csv(
-                meta_paths["identity"],
-                sep=", "
-            )
-        for key,conf in [(k,v) for (k,v) in meta_paths.items() if k != "identity"]:
-            
-            temp = pd.read_csv(conf,sep='\t', header=None)
-            temp.columns = ['Image_Path', key]
-            
-            temp['Class_ID'] = temp['Image_Path'].str.split('/').str[0]
-            #temp['Image_Name'] = temp['Image_Path'].str.split('/').str[1]
-            
-            temp.drop(columns=['Image_Path'], inplace=True)
-            
-            meta = meta.merge(temp, on='Class_ID', how='left')
-        for file_path, file_obj in paths:
-
-                label = file_path.split("/")[2]
-                yield file_path, {
-                        "image": {"path": file_path, "bytes": file_obj.read()},
-            # "image_id": datasets.Value("string"),
-            # "class_id": datasets.Value("string"),
-            # "identity": datasets.Value("string"),
-            # 'gender': dataset.Value("string"),
-            # 'sample_num':dataset.Value("uint64"), 
-            # 'flag':dataset.Value("bool"),
-            # "male": datasets.Value("bool"),
-            # "black_hair": datasets.Value("bool"),
-            # "gray_hair": datasets.Value("bool"),
-            # "blond_hair": datasets.Value("bool"),
-            # "long_hair": datasets.Value("bool"),
-            # "mustache_or_beard": datasets.Value("bool"),
-            # "wearing_hat": datasets.Value("bool"),
-            # "eyeglasses": datasets.Value("bool"),
-            # "sunglasses": datasets.Value("bool"),
-            #"mouth_open": datasets.Value("bool")
-                    }
-                key+= 1
-