| |
|
|
| import argparse, asyncio, os |
| from itertools import islice |
|
|
| from aiostream import stream |
| import h5py as h5 |
| import numpy as np |
| import pandas as pd |
| from tqdm.asyncio import trange |
|
|
| from .loader import datafile_path, gen_samples, samples_count |
| from .crawler import run |
|
|
| script_dir = os.path.dirname(__file__) |
| outfile_path = os.path.join(script_dir, "../data/lld-processed.h5") |
|
|
|
|
| async def gen_processor( |
| batch_size: int, limit: int, datafile_path: str = datafile_path |
| ): |
| count = min(limit, samples_count) |
| batch_size = min(limit, batch_size) |
|
|
| samples = gen_samples(datafile_path=datafile_path) |
| steps = count // batch_size |
|
|
| for step in trange(steps): |
| batch = list(islice(samples, step * batch_size, (step + 1) * batch_size)) |
|
|
| urls = [f"http://{sample['meta_data/names'].decode()}.com" for sample in batch] |
| descriptions = await run(urls, batch_size) |
|
|
| for sample, description in zip(batch, descriptions): |
| name = (sample["meta_data/names"].decode(),) |
| images = sample["data"] |
|
|
| data = ( |
| images, |
| description, |
| name, |
| ) |
|
|
| yield data |
|
|
|
|
| async def preprocess( |
| batch_size: int = 100, |
| limit: int = samples_count + 1, |
| datafile_path: str = datafile_path, |
| ): |
|
|
| columns = ["images", "description", "name"] |
|
|
| processor = gen_processor(batch_size, limit, datafile_path=datafile_path) |
|
|
| chunk_size = 1000 |
| async with stream.chunks(processor, chunk_size).stream() as chunks: |
| async for chunk in chunks: |
| df_chunk = pd.DataFrame(chunk, columns=columns) |
| df_chunk.to_hdf(outfile_path, "data", data_columns=columns, mode="a") |
|
|
|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser() |
|
|
| parser.add_argument( |
| "--datafile_path", |
| help="Path to downloaded archive", |
| type=str, |
| default=datafile_path, |
| ) |
|
|
| parser.add_argument( |
| "--limit", |
| help="Limit to total records processed", |
| type=int, |
| default=samples_count + 1, |
| ) |
|
|
| parser.add_argument( |
| "--batch_size", |
| help="Batch size", |
| type=int, |
| nargs="?", |
| const=10_000, |
| default=10_000, |
| ) |
|
|
| args = parser.parse_args() |
|
|
| asyncio.run(preprocess(batch_size=args.batch_size, limit=args.limit)) |
|
|
