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"""MagnaTagATune dataset.""" |
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import os |
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import json |
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import gzip |
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import shutil |
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import pathlib |
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import logging |
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import textwrap |
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import datasets |
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import itertools |
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import typing as tp |
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import pandas as pd |
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import urllib.request |
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from pathlib import Path |
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from copy import deepcopy |
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from tqdm.auto import tqdm |
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from rich import print |
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from rich.logging import RichHandler |
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logger = logging.getLogger(__name__) |
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logger.addHandler(RichHandler()) |
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logger.setLevel(logging.INFO) |
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SAMPLE_RATE = 16_000 |
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VERSION = "0.0.1" |
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DEFAULT_XDG_CACHE_HOME = "~/.cache" |
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XDG_CACHE_HOME = os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME) |
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DEFAULT_HF_CACHE_HOME = os.path.join(XDG_CACHE_HOME, "huggingface") |
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HF_CACHE_HOME = os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME)) |
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DEFAULT_HF_DATASETS_CACHE = os.path.join(HF_CACHE_HOME, "datasets") |
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HF_DATASETS_CACHE = Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE)) |
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CLASSES = [ |
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"guitar", "classical", "slow", "techno", "strings", "drums", "electronic", "rock", "fast", "piano", |
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"ambient", "beat", "violin", "vocal", "synth", "female", "indian", "opera", "male", "singing", "vocals", |
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"no vocals", "harpsichord", "loud", "quiet", "flute", "woman", "male vocal", "no vocal", "pop", "soft", |
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"sitar", "solo", "man", "classic", "choir", "voice", "new age", "dance", "male voice", "female vocal", |
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"beats", "harp", "cello", "no voice", "weird", "country", "metal", "female voice", "choral" |
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] |
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CLASSES = sorted(CLASSES) |
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class MagnaTagATuneConfig(datasets.BuilderConfig): |
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"""BuilderConfig for MagnaTagATune.""" |
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def __init__(self, features, **kwargs): |
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super(MagnaTagATuneConfig, self).__init__(version=datasets.Version(VERSION, ""), **kwargs) |
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self.features = features |
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class MagnaTagATune(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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MagnaTagATuneConfig( |
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features=datasets.Features( |
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{ |
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"file": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=SAMPLE_RATE), |
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"sound": datasets.Sequence(datasets.Value("string")), |
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"label": datasets.Sequence(datasets.features.ClassLabel(names=CLASSES)), |
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} |
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), |
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name="top50", |
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description="", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "top50" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description="", |
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features=self.config.features, |
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supervised_keys=None, |
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homepage="", |
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citation="", |
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task_templates=None, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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zip_file_url = "https://huggingface.co/datasets/confit/magnatagatune/resolve/main/mp3.zip" |
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_filename = zip_file_url.split('/')[-1] |
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_save_path = os.path.join( |
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HF_DATASETS_CACHE, 'confit___magnatagatune/top50', VERSION, _filename |
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) |
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download_file(zip_file_url, _save_path) |
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logger.info(f"`{_filename}` is downloaded to {_save_path}") |
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archive_path = dl_manager.extract(_save_path) |
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logger.info(f"`{_filename}` is now extracted to {archive_path}") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path, "split": "train"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={"archive_path": archive_path, "split": "validation"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"} |
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), |
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] |
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def _generate_examples(self, archive_path, split=None): |
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df = pd.read_csv( |
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'https://huggingface.co/datasets/confit/magnatagatune/resolve/main/annotations_final.csv', sep="\t" |
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) |
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df = df[df[CLASSES].sum(axis=1) > 0].reset_index(drop=True) |
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df = df[CLASSES + ["mp3_path", "clip_id"]] |
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train_ids_df = pd.read_csv( |
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"https://huggingface.co/datasets/confit/magnatagatune/resolve/main/train_gt_mtt.tsv", sep="\t", header=None |
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) |
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train_ids = train_ids_df[0].tolist() |
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train_df = df[df["clip_id"].isin(train_ids)].reset_index(drop=True) |
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validation_ids_df = pd.read_csv( |
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"https://huggingface.co/datasets/confit/magnatagatune/resolve/main/val_gt_mtt.tsv", sep="\t", header=None |
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) |
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validation_ids = validation_ids_df[0].tolist() |
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validation_df = df[df["clip_id"].isin(validation_ids)].reset_index(drop=True) |
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test_ids_df = pd.read_csv( |
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"https://huggingface.co/datasets/confit/magnatagatune/resolve/main/test_gt_mtt.tsv", sep="\t", header=None |
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) |
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test_ids = test_ids_df[0].tolist() |
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test_df = df[df["clip_id"].isin(test_ids)].reset_index(drop=True) |
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extensions = ['.mp3'] |
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_, _walker = fast_scandir(archive_path, extensions, recursive=True) |
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result = df.apply(lambda row: [col for col in df.columns if row[col] == 1], axis=1).tolist() |
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if split == 'train': |
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fileid2class = {} |
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for idx, row in train_df.iterrows(): |
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fileid = os.path.join(archive_path, str(row['mp3_path'])) |
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class_ = result[idx] |
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fileid2class[fileid] = class_ |
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elif split == 'validation': |
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fileid2class = {} |
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for idx, row in validation_df.iterrows(): |
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fileid = os.path.join(archive_path, str(row['mp3_path'])) |
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class_ = result[idx] |
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fileid2class[fileid] = class_ |
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elif split == 'test': |
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fileid2class = {} |
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for idx, row in test_df.iterrows(): |
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fileid = os.path.join(archive_path, str(row['mp3_path'])) |
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class_ = result[idx] |
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fileid2class[fileid] = class_ |
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for guid, audio_path in enumerate(_walker): |
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if audio_path not in fileid2class: |
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continue |
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tags = fileid2class.get(audio_path) |
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yield guid, { |
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"id": str(guid), |
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"file": audio_path, |
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"audio": audio_path, |
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"sound": tags, |
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"label": tags, |
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} |
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def fast_scandir(path: str, exts: tp.List[str], recursive: bool = False): |
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subfolders, files = [], [] |
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try: |
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for f in os.scandir(path): |
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try: |
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if f.is_dir(): |
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subfolders.append(f.path) |
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elif f.is_file(): |
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if os.path.splitext(f.name)[1].lower() in exts: |
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files.append(f.path) |
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except Exception: |
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pass |
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except Exception: |
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pass |
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if recursive: |
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for path in list(subfolders): |
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sf, f = fast_scandir(path, exts, recursive=recursive) |
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subfolders.extend(sf) |
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files.extend(f) |
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return subfolders, files |
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def download_file( |
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source, |
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dest, |
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unpack=False, |
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dest_unpack=None, |
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replace_existing=False, |
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write_permissions=False, |
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): |
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"""Downloads the file from the given source and saves it in the given |
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destination path. |
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Arguments |
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--------- |
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source : path or url |
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Path of the source file. If the source is an URL, it downloads it from |
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the web. |
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dest : path |
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Destination path. |
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unpack : bool |
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If True, it unpacks the data in the dest folder. |
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dest_unpack: path |
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Path where to store the unpacked dataset |
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replace_existing : bool |
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If True, replaces the existing files. |
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write_permissions: bool |
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When set to True, all the files in the dest_unpack directory will be granted write permissions. |
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This option is active only when unpack=True. |
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""" |
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class DownloadProgressBar(tqdm): |
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"""DownloadProgressBar class.""" |
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def update_to(self, b=1, bsize=1, tsize=None): |
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"""Needed to support multigpu training.""" |
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if tsize is not None: |
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self.total = tsize |
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self.update(b * bsize - self.n) |
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dest_dir = pathlib.Path(dest).resolve().parent |
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dest_dir.mkdir(parents=True, exist_ok=True) |
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if "http" not in source: |
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shutil.copyfile(source, dest) |
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elif not os.path.isfile(dest) or ( |
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os.path.isfile(dest) and replace_existing |
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): |
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print(f"Downloading {source} to {dest}") |
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with DownloadProgressBar( |
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unit="B", |
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unit_scale=True, |
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miniters=1, |
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desc=source.split("/")[-1], |
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) as t: |
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urllib.request.urlretrieve( |
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source, filename=dest, reporthook=t.update_to |
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) |
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else: |
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print(f"{dest} exists. Skipping download") |
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if unpack: |
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if dest_unpack is None: |
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dest_unpack = os.path.dirname(dest) |
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print(f"Extracting {dest} to {dest_unpack}") |
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if ( |
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source.endswith(".tar.gz") |
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or source.endswith(".tgz") |
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or source.endswith(".gz") |
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): |
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out = dest.replace(".gz", "") |
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with gzip.open(dest, "rb") as f_in: |
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with open(out, "wb") as f_out: |
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shutil.copyfileobj(f_in, f_out) |
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else: |
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shutil.unpack_archive(dest, dest_unpack) |
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if write_permissions: |
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set_writing_permissions(dest_unpack) |
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def set_writing_permissions(folder_path): |
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""" |
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This function sets user writing permissions to all the files in the given folder. |
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Arguments |
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--------- |
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folder_path : folder |
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Folder whose files will be granted write permissions. |
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""" |
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for root, dirs, files in os.walk(folder_path): |
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for file_name in files: |
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file_path = os.path.join(root, file_name) |
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os.chmod(file_path, 0o666) |