PaddleX/zhch/mp_infer_orignal.py

import argparse
import sys
from multiprocessing import Manager, Process
from pathlib import Path
from queue import Empty

from paddlex import create_pipeline
from paddlex.utils.device import constr_device, parse_device


def worker(pipeline_name_or_config_path, device, task_queue, batch_size, output_dir):
    pipeline = create_pipeline(pipeline_name_or_config_path, device=device)

    should_end = False
    batch = []

    while not should_end:
        try:
            input_path = task_queue.get_nowait()
        except Empty:
            should_end = True
        else:
            batch.append(input_path)

        if batch and (len(batch) == batch_size or should_end):
            try:
                for result in pipeline.predict(batch):
                    input_path = Path(result["input_path"])
                    if result.get("page_index") is not None:
                        output_path = f"{input_path.stem}_{result['page_index']}.json"
                    else:
                        output_path = f"{input_path.stem}.json"
                    output_path = str(Path(output_dir, output_path))
                    result.save_to_json(output_path)
                    print(f"Processed {repr(str(input_path))}")
            except Exception as e:
                print(
                    f"Error processing {batch} on {repr(device)}: {e}", file=sys.stderr
                )
            batch.clear()


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--pipeline", type=str, required=True, help="Pipeline name or config path."
    )
    parser.add_argument("--input_dir", type=str, required=True, help="Input directory.")
    parser.add_argument(
        "--device",
        type=str,
        required=True,
        help="Specifies the devices for performing parallel inference.",
    )
    parser.add_argument(
        "--output_dir", type=str, default="output", help="Output directory."
    )
    parser.add_argument(
        "--instances_per_device",
        type=int,
        default=1,
        help="Number of pipeline instances per device.",
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        default=1,
        help="Inference batch size for each pipeline instance.",
    )
    parser.add_argument(
        "--input_glob_pattern",
        type=str,
        default="*",
        help="Pattern to find the input files.",
    )
    args = parser.parse_args()

    input_dir = Path(args.input_dir).resolve()
    print(f"Input directory: {input_dir}")
    if not input_dir.exists():
        print(f"The input directory does not exist: {input_dir}", file=sys.stderr)
        return 2
    if not input_dir.is_dir():
        print(f"{repr(str(input_dir))} is not a directory.", file=sys.stderr)
        return 2

    output_dir = Path(args.output_dir).resolve()
    print(f"Output directory: {output_dir}")
    if output_dir.exists() and not output_dir.is_dir():
        print(f"{repr(str(output_dir))} is not a directory.", file=sys.stderr)
        return 2
    output_dir.mkdir(parents=True, exist_ok=True)

    device_type, device_ids = parse_device(args.device)
    if device_ids is None or len(device_ids) == 1:
        print(
            "Please specify at least two devices for performing parallel inference.",
            file=sys.stderr,
        )
        return 2

    if args.batch_size <= 0:
        print("Batch size must be greater than 0.", file=sys.stderr)
        return 2

    with Manager() as manager:
        task_queue = manager.Queue()
        for img_path in input_dir.glob(args.input_glob_pattern):
            task_queue.put(str(img_path))

        processes = []
        for device_id in device_ids:
            for _ in range(args.instances_per_device):
                device = constr_device(device_type, [device_id])
                p = Process(
                    target=worker,
                    args=(
                        args.pipeline,
                        device,
                        task_queue,
                        args.batch_size,
                        str(output_dir),
                    ),
                )
                p.start()
                processes.append(p)

        for p in processes:
            p.join()

    print("All done")

    return 0


if __name__ == "__main__":
    sys.exit(main())