Upload create_nano_dataset.py with huggingface_hub

create_nano_dataset.py

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+import argparse
+import os
+import random
+import shutil
+from tqdm import tqdm
+import webdataset as wds
+from huggingface_hub import HfApi, HfFolder
+
+def sample_and_copy_files(source_dir, output_dir, num_samples, seed, scan_report_interval):
+    """
+    Scans a source directory, randomly samples image files, and copies them 
+    to an output directory, preserving the original structure.
+
+    IMPORTANT: This script is currently hardcoded to only sample images from
+    corruption severity level 5.
+    """
+    print(f"1. Scanning for image files in '{source_dir}'...")
+    all_files = []
+    count = 0
+    for root, _, files in os.walk(source_dir):
+        # HARDCODED FILTER: Only include files from severity level 5 directories.
+        # We check if '5' is a component of the directory path.
+        path_parts = root.split(os.sep)
+        if '5' in path_parts:
+            for file in files:
+                if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):
+                    all_files.append(os.path.join(root, file))
+                    count += 1
+                    if count > 0 and count % scan_report_interval == 0:
+                        print(f"   ...scanned {count} images (from severity 5)...")
+    
+    if not all_files:
+        print(f"Error: No image files found for severity level 5 in '{source_dir}'. Exiting.")
+        return False
+    
+    print(f"Found {len(all_files)} total images.")
+
+    # Sort the file list to ensure reproducibility across different systems.
+    # The order of files returned by os.walk is not guaranteed to be the same.
+    all_files.sort()
+
+    print(f"2. Randomly sampling {num_samples} images (seed={seed})...")
+    # Set the random seed for reproducibility.
+    # Note: For 100% identical results, the same version of Python should be used
+    # (e.g., this script was created using Python 3.10.14), as the underlying
+    # algorithm for the 'random' module can change between versions.
+    random.seed(seed)
+    num_to_sample = min(num_samples, len(all_files))
+    sampled_files = random.sample(all_files, num_to_sample)
+    print(f"Selected {len(sampled_files)} files to copy.")
+
+    print(f"3. Copying files to '{output_dir}'...")
+    if os.path.exists(output_dir):
+        print(f"Output directory '{output_dir}' already exists. Removing it.")
+        shutil.rmtree(output_dir)
+    os.makedirs(output_dir)
+
+    for file_path in tqdm(sampled_files, desc="Copying files"):
+        relative_path = os.path.relpath(file_path, source_dir)
+        dest_path = os.path.join(output_dir, relative_path)
+        os.makedirs(os.path.dirname(dest_path), exist_ok=True)
+        shutil.copy(file_path, dest_path)
+    
+    print("File copying complete.")
+    return True
+
+def generate_readme_content(repo_id, seed, python_version, tar_filename, script_filename):
+    """
+    Generates the content for the README.md file for the Hugging Face dataset.
+    """
+    return f"""---
+license: mit
+tags:
+- image-classification
+- computer-vision
+- imagenet-c
+---
+
+# Nano ImageNet-C (Severity 5)
+
+This is a randomly sampled subset of the ImageNet-C dataset, containing 5,000 images exclusively from corruption **severity level 5**. It is designed for efficient testing and validation of model robustness.
+
+这是一个从 ImageNet-C 数据集中随机抽样的子集，包含 5000 张仅来自损坏等级为 **5** 的图像。它旨在用于高效地测试和验证模型的鲁棒性。
+
+## How to Generate / 如何生成
+
+This dataset was generated using the `{script_filename}` script included in this repository. To ensure reproducibility, the following parameters were used:
+
+本数据集使用此仓库中包含的 `{script_filename}` 脚本生成。为确保可复现性，生成时使用了以下参数：
+
+- **Source Dataset / 源数据集**: The full ImageNet-C dataset is required. / 需要完整的 ImageNet-C 数据集。
+- **Random Seed / 随机种子**: `{seed}`
+- **Python Version / Python 版本**: `{python_version}`
+
+## Dataset Structure / 数据集结构
+
+The dataset is provided as a single `.tar` file named `{tar_filename}` in the `webdataset` format. The internal structure preserves the original ImageNet-C hierarchy: `corruption_type/class_name/image.jpg`.
+
+数据集以 `webdataset` 格式打包在名为 `{tar_filename}` 的单个 `.tar` 文件中。其内部结构保留了原始 ImageNet-C 的层次结构：`corruption_type/class_name/image.jpg`。
+
+## Citation / 引用
+
+If you use this dataset, please cite the original ImageNet-C paper:
+
+如果您使用此数据集，请引用原始 ImageNet-C 的论文：
+
+```bibtex
+@inproceedings{{danhendrycks2019robustness,
+  title={{Benchmarking Neural Network Robustness to Common Corruptions and Perturbations}},
+  author={{Dan Hendrycks and Thomas Dietterich}},
+  booktitle={{International Conference on Learning Representations}},
+  year={{2019}},
+  url={{https://openreview.net/forum?id=HJz6tiCqYm}},
+}}
+```
+"""
+
+def package_with_webdataset(output_dir, tar_path):
+    """
+    Packages the contents of a directory into a single .tar file using webdataset.
+    """
+    print(f"4. Packaging '{output_dir}' into '{tar_path}'...")
+    
+    with wds.TarWriter(tar_path) as sink:
+        for root, _, files in tqdm(list(os.walk(output_dir)), desc="Packaging files"):
+            for file in files:
+                file_path = os.path.join(root, file)
+                with open(file_path, "rb") as stream:
+                    content = stream.read()
+                
+                relative_path = os.path.relpath(file_path, output_dir)
+                key, ext = os.path.splitext(relative_path)
+                extension = ext.lstrip('.')
+                
+                sink.write({
+                    "__key__": key,
+                    extension: content
+                })
+    
+    print("Packaging complete.")
+
+def upload_to_hf(tar_path, readme_path, script_path, repo_id):
+    """
+    Uploads a file to a specified Hugging Face Hub repository.
+    """
+    print(f"5. Uploading files to Hugging Face Hub repository: {repo_id}...")
+    
+    if HfFolder.get_token() is None:
+        print("Hugging Face token not found. Please log in using `huggingface-cli login` first.")
+        return
+
+    try:
+        api = HfApi()
+        print(f"Creating repository '{repo_id}' (if it doesn't exist)...")
+        api.create_repo(repo_id, repo_type="dataset", exist_ok=True)
+        
+        print("Uploading README.md...")
+        api.upload_file(
+            path_or_fileobj=readme_path,
+            path_in_repo="README.md",
+            repo_id=repo_id,
+            repo_type="dataset"
+        )
+        
+        script_filename = os.path.basename(script_path)
+        print(f"Uploading generation script '{script_filename}'...")
+        api.upload_file(
+            path_or_fileobj=script_path,
+            path_in_repo=script_filename,
+            repo_id=repo_id,
+            repo_type="dataset"
+        )
+
+        tar_filename = os.path.basename(tar_path)
+        print(f"Uploading dataset file '{tar_filename}'...")
+        api.upload_file(
+            path_or_fileobj=tar_path,
+            path_in_repo=tar_filename,
+            repo_id=repo_id,
+            repo_type="dataset"
+        )
+        print("Upload successful!")
+        print(f"Dataset available at: https://huggingface.co/datasets/{repo_id}")
+    except Exception as e:
+        print(f"An error occurred during upload: {e}")
+
+def main():
+    """
+    Main function to orchestrate the dataset creation, packaging, and upload process.
+    """
+    parser = argparse.ArgumentParser(description="Create, package, and upload a smaller version of an image dataset.")
+    parser.add_argument("--source_dir", type=str, default="./data/ImageNet-C", help="Path to the source dataset.")
+    parser.add_argument("--output_dir", type=str, default="./data/nano-ImageNet-C", help="Path to save the new sampled dataset.")
+    parser.add_argument("--num_samples", type=int, default=5000, help="Number of images to sample.")
+    parser.add_argument("--seed", type=int, default=7600, help="Random seed for reproducibility.")
+    parser.add_argument("--repo_id", type=str, default="niuniandaji/nano-imagenet-c", help="The Hugging Face Hub repository ID.")
+    parser.add_argument("--tar_path", type=str, default="./data/nano-ImageNet-C.tar", help="Path to save the final webdataset archive.")
+    parser.add_argument("--scan_report_interval", type=int, default=50000, help="How often to report progress during file scanning.")
+    args = parser.parse_args()
+
+    print("--- Starting Dataset Creation Process ---")
+    print("IMPORTANT: The script is configured to sample ONLY from severity level 5.")
+    
+    # Generate README content
+    script_filename = os.path.basename(__file__)
+    readme_content = generate_readme_content(
+        args.repo_id, 
+        args.seed, 
+        "3.10.14", 
+        os.path.basename(args.tar_path), 
+        script_filename
+    )
+    
+    # Write README to a local file
+    readme_path = "README.md"
+    with open(readme_path, "w", encoding="utf-8") as f:
+        f.write(readme_content)
+    print(f"Generated README.md for the dataset.")
+
+    if sample_and_copy_files(args.source_dir, args.output_dir, args.num_samples, args.seed, args.scan_report_interval):
+        package_with_webdataset(args.output_dir, args.tar_path)
+        upload_to_hf(args.tar_path, readme_path, script_filename, args.repo_id)
+
+    print("--- Process Finished ---")
+
+if __name__ == "__main__":
+    main()