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