tfd-utils 0.4.1

TensorFlow utilities for efficient TFRecord processing and random access

tfd-utils

A lightweight Python library for efficient random access to TensorFlow TFRecord files and tar archives, without requiring TensorFlow.

Key Features

• Unified API: Access TFRecord files and tar archives through the same interface.
• Random Access: Access any record by key in O(1) time without reading the entire file.
• Automatic Index Caching: Index is built once and cached to disk; rebuilt automatically when files change.
• Lightweight & Standalone: TFRecord support requires only numpy, protobuf, and crc32c. No TensorFlow needed.
• Full TensorFlow Compatibility: Write with tfd_utils, read with TensorFlow (or vice versa). 100% compatible.
• Multiple File Support: Single files, lists of files, or glob patterns.
• Tar-to-TFRecord Conversion: CLI tool to batch-convert tar archives to TFRecord format with parallel workers and optional source deletion.

Installation

pip install tfd-utils

Usage

TFRecord Random Access

from tfd_utils import TFRecordRandomAccess

reader = TFRecordRandomAccess("data.tfrecord")
# or multiple files / glob patterns
reader = TFRecordRandomAccess(["train_*.tfrecord", "val_*.tfrecord"])

image_bytes = reader.get_feature("record_1", "image")
record = reader["record_1"]   # all features as Example protobuf
print(f"Total records: {len(reader)}")

Tar Archive Random Access

Tar archives are expected to contain paired files sharing the same stem:

sa_000001.jpg   →  key='sa_000001', feature='jpg'
sa_000001.json  →  key='sa_000001', feature='json'

Both uncompressed (.tar) and compressed (.tar.gz, etc.) archives are supported.

from tfd_utils import TarRandomAccess

reader = TarRandomAccess("archive.tar")
# or glob / list of tars
reader = TarRandomAccess("sa1b/*.tar")

jpg_bytes  = reader.get_feature("sa_000001", "jpg")
json_bytes = reader.get_feature("sa_000001", "json")
record     = reader["sa_000001"]   # {'jpg': bytes, 'json': bytes}
print(f"Total records: {len(reader)}")

Member paths with subdirectory prefixes are handled automatically: ./subdir/foo.jpg → key subdir/foo.

Example: SA-1B Dataset

SA-1B tars are gzip-compressed and contain paired .jpg / .json files per image:

import json
from PIL import Image
import io
from tfd_utils import TarRandomAccess

# Point to one or more SA-1B tar files (compressed tars are supported)
reader = TarRandomAccess("/path/to/sa1b/sa_000020.tar")
# or load multiple shards at once
reader = TarRandomAccess("/path/to/sa1b/*.tar")

# Each key is the image ID (e.g. 'sa_226692')
keys = reader.get_keys()
print(f"Images in this shard: {len(keys)}")

key = keys[0]

# Load the JPEG image
jpg_bytes = reader.get_feature(key, "jpg")
image = Image.open(io.BytesIO(jpg_bytes))

# Load the annotation (segmentation masks, bounding boxes, …)
json_bytes = reader.get_feature(key, "json")
annotation = json.loads(json_bytes)
print(f"Image size : {annotation['image']['width']}x{annotation['image']['height']}")
print(f"Masks      : {len(annotation['annotations'])}")

Writing TFRecords

from tfd_utils.writer import TFRecordWriter
from tfd_utils.pb2 import Example, Features, Feature, BytesList

with TFRecordWriter("data.tfrecord") as writer:
    example = Example(features=Features(feature={
        'key':   Feature(bytes_list=BytesList(value=[b'record_1'])),
        'image': Feature(bytes_list=BytesList(value=[b'<image bytes>'])),
    }))
    writer.write(example.SerializeToString())

Common API (both readers)

reader.get_record(key)                    # full record
reader.get_feature(key, feature_name)     # single feature
reader.get_feature_list(key, feature_name)
reader.get_keys()                         # all keys
reader.get_stats()                        # total_records, total_files, ...
reader.contains_key(key)
reader.rebuild_index()

key in reader                             # __contains__
reader[key]                               # __getitem__ (raises KeyError if missing)
len(reader)                               # __len__

with TarRandomAccess("archive.tar") as r: # context manager
    ...

Advanced Options

# TFRecord: custom key feature name (default 'key')
reader = TFRecordRandomAccess("file.tfrecord", key_feature_name="id")

# Both: custom index file location
reader = TFRecordRandomAccess("file.tfrecord", index_file="my.index")
reader = TarRandomAccess("archive.tar", index_file="my.tar_index")

# Both: control parallelism
reader = TarRandomAccess("*.tar", max_workers=8, use_multiprocessing=True)

CLI

tfd list    /path/to/data.tfrecord
tfd extract /path/to/data.tfrecord record_key
tfd get     /path/to/data.tfrecord:record_key:feature_name

Converting Tar Archives to TFRecord

The tfd convert command converts tar archive(s) to TFRecord files. Each record stores all file extensions as bytes features plus a key feature containing the file stem.

# Convert a single tar
tfd convert /path/to/archive.tar

# Convert all tars in a directory, write to a different output directory
tfd convert /path/to/sa1b/ --output-dir /path/to/output/

# Glob pattern
tfd convert '/path/to/sa1b/sa_0000*.tar' --output-dir /path/to/output/

# Delete each source tar after successful conversion
tfd convert /path/to/sa1b/ --output-dir /path/to/output/ --delete

# Control parallelism (default: 16 workers)
tfd convert /path/to/sa1b/ --output-dir /path/to/output/ --workers 32

Each input foo.tar produces foo.tfrecord in the output directory (default: same directory as the source). A TFRecord produced from SA-1B tars contains these features per record:

Feature	Type	Content
key	bytes	File stem, e.g. sa_226692
jpg	bytes	Raw JPEG image bytes
json	bytes	Annotation JSON (masks, boxes…)

from tfd_utils import TFRecordRandomAccess
import json

reader = TFRecordRandomAccess("/path/to/output/sa_000000.tfrecord")
jpg_bytes  = reader.get_feature("sa_226692", "jpg")
json_bytes = reader.get_feature("sa_226692", "json")
annotation = json.loads(json_bytes)

TensorFlow Interoperability

import tensorflow as tf

dataset = tf.data.TFRecordDataset("data.tfrecord")
for record in dataset:
    example = tf.train.Example()
    example.ParseFromString(record.numpy())

License

MIT License