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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,128 @@ | ||
| #!/usr/bin/env python3 | ||
| import hashlib | ||
| import os | ||
|
|
||
| from typing import Optional | ||
| from urllib.request import urlopen, Request | ||
| from pathlib import Path | ||
| from zipfile import ZipFile | ||
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| REPO_BASE_DIR = Path(__file__).absolute().parent.parent | ||
| DATA_DIR = REPO_BASE_DIR / "_data" | ||
| BEGINNER_DATA_DIR = REPO_BASE_DIR / "beginner_source" / "data" | ||
| INTERMEDIATE_DATA_DIR = REPO_BASE_DIR / "intermediate_source" / "data" | ||
| ADVANCED_DATA_DIR = REPO_BASE_DIR / "advanced_source" / "data" | ||
| PROTOTYPE_DATA_DIR = REPO_BASE_DIR / "prototype_source" / "data" | ||
| FILES_TO_RUN = os.getenv("FILES_TO_RUN") | ||
|
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||
|
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| def size_fmt(nbytes: int) -> str: | ||
| """Returns a formatted file size string""" | ||
| KB = 1024 | ||
| MB = 1024 * KB | ||
| GB = 1024 * MB | ||
| if abs(nbytes) >= GB: | ||
| return f"{nbytes * 1.0 / GB:.2f} Gb" | ||
| elif abs(nbytes) >= MB: | ||
| return f"{nbytes * 1.0 / MB:.2f} Mb" | ||
| elif abs(nbytes) >= KB: | ||
| return f"{nbytes * 1.0 / KB:.2f} Kb" | ||
| return str(nbytes) + " bytes" | ||
|
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| def download_url_to_file(url: str, | ||
| dst: Optional[str] = None, | ||
| prefix: Optional[Path] = None, | ||
| sha256: Optional[str] = None) -> Path: | ||
| dst = dst if dst is not None else Path(url).name | ||
| dst = dst if prefix is None else str(prefix / dst) | ||
| if Path(dst).exists(): | ||
| print(f"Skip downloading {url} as {dst} already exists") | ||
| return Path(dst) | ||
| file_size = None | ||
| u = urlopen(Request(url, headers={"User-Agent": "tutorials.downloader"})) | ||
| meta = u.info() | ||
| if hasattr(meta, 'getheaders'): | ||
| content_length = meta.getheaders("Content-Length") | ||
| else: | ||
| content_length = meta.get_all("Content-Length") | ||
| if content_length is not None and len(content_length) > 0: | ||
| file_size = int(content_length[0]) | ||
| sha256_sum = hashlib.sha256() | ||
| with open(dst, "wb") as f: | ||
| while True: | ||
| buffer = u.read(32768) | ||
| if len(buffer) == 0: | ||
| break | ||
| sha256_sum.update(buffer) | ||
| f.write(buffer) | ||
| digest = sha256_sum.hexdigest() | ||
| if sha256 is not None and sha256 != digest: | ||
| Path(dst).unlink() | ||
| raise RuntimeError(f"Downloaded {url} has unexpected sha256sum {digest} should be {sha256}") | ||
| print(f"Downloaded {url} sha256sum={digest} size={size_fmt(file_size)}") | ||
| return Path(dst) | ||
|
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|
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| def unzip(archive: Path, tgt_dir: Path) -> None: | ||
| with ZipFile(str(archive), "r") as zip_ref: | ||
| zip_ref.extractall(str(tgt_dir)) | ||
|
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||
|
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| def download_hymenoptera_data(): | ||
| # transfer learning tutorial data | ||
| z = download_url_to_file("https://download.pytorch.org/tutorial/hymenoptera_data.zip", | ||
| prefix=DATA_DIR, | ||
| sha256="fbc41b31d544714d18dd1230b1e2b455e1557766e13e67f9f5a7a23af7c02209", | ||
| ) | ||
| unzip(z, BEGINNER_DATA_DIR) | ||
|
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|
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| def download_nlp_data() -> None: | ||
| # nlp tutorial data | ||
| z = download_url_to_file("https://download.pytorch.org/tutorial/data.zip", | ||
| prefix=DATA_DIR, | ||
| sha256="fb317e80248faeb62dc25ef3390ae24ca34b94e276bbc5141fd8862c2200bff5", | ||
| ) | ||
| # This will unzip all files in data.zip to intermediate_source/data/ folder | ||
| unzip(z, INTERMEDIATE_DATA_DIR.parent) | ||
|
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|
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| def download_dcgan_data() -> None: | ||
| # Download dataset for beginner_source/dcgan_faces_tutorial.py | ||
| z = download_url_to_file("https://s3.amazonaws.com/pytorch-tutorial-assets/img_align_celeba.zip", | ||
| prefix=DATA_DIR, | ||
| sha256="46fb89443c578308acf364d7d379fe1b9efb793042c0af734b6112e4fd3a8c74", | ||
| ) | ||
| unzip(z, BEGINNER_DATA_DIR / "celeba") | ||
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| def download_lenet_mnist() -> None: | ||
| # Download model for beginner_source/fgsm_tutorial.py | ||
| download_url_to_file("https://docs.google.com/uc?export=download&id=1HJV2nUHJqclXQ8flKvcWmjZ-OU5DGatl", | ||
| prefix=BEGINNER_DATA_DIR, | ||
| dst="lenet_mnist_model.pth", | ||
| sha256="cb5f8e578aef96d5c1a2cc5695e1aa9bbf4d0fe00d25760eeebaaac6ebc2edcb", | ||
| ) | ||
|
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|
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| def main() -> None: | ||
| DATA_DIR.mkdir(exist_ok=True) | ||
| BEGINNER_DATA_DIR.mkdir(exist_ok=True) | ||
| ADVANCED_DATA_DIR.mkdir(exist_ok=True) | ||
| INTERMEDIATE_DATA_DIR.mkdir(exist_ok=True) | ||
| PROTOTYPE_DATA_DIR.mkdir(exist_ok=True) | ||
|
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| if FILES_TO_RUN is None or "transfer_learning_tutorial" in FILES_TO_RUN: | ||
| download_hymenoptera_data() | ||
| nlp_tutorials = ["seq2seq_translation_tutorial", "char_rnn_classification_tutorial", "char_rnn_generation_tutorial"] | ||
| if FILES_TO_RUN is None or any(x in FILES_TO_RUN for x in nlp_tutorials): | ||
| download_nlp_data() | ||
| if FILES_TO_RUN is None or "dcgan_faces_tutorial" in FILES_TO_RUN: | ||
| download_dcgan_data() | ||
| if FILES_TO_RUN is None or "fgsm_tutorial" in FILES_TO_RUN: | ||
| download_lenet_mnist() | ||
|
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||
|
|
||
| if __name__ == "__main__": | ||
| main() |
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