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24ee802 6117d42 24ee802 6117d42 24ee802 6117d42 24ee802 6117d42 24ee802 6117d42 24ee802 1d55733 24ee802 6117d42 24ee802 6117d42 24ee802 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | # llg/preprocess.py
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))
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