Source code for monailabel.interfaces.tasks.batch_infer

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# Unless required by applicable law or agreed to in writing, software
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import copy
import logging
import multiprocessing
import time
from concurrent.futures import ThreadPoolExecutor
from enum import Enum
from typing import Callable

import torch

from monailabel.interfaces.datastore import Datastore, DefaultLabelTag
from monailabel.utils.others.generic import handle_torch_linalg_multithread, name_to_device, remove_file

logger = logging.getLogger(__name__)

[docs]class BatchInferImageType(str, Enum): IMAGES_ALL = "all" IMAGES_LABELED = "labeled" IMAGES_UNLABELED = "unlabeled"
[docs]class BatchInferTask: """ Basic Batch Infer Task """
[docs] def get_images(self, request, datastore: Datastore): """ Override this method to get all eligible images for your task to run batch infer """ images = request.get("images", BatchInferImageType.IMAGES_ALL) label_tag = request.get("label_tag") labels = request.get("labels") if isinstance(images, str): if images == BatchInferImageType.IMAGES_LABELED: return datastore.get_labeled_images(label_tag, labels) if images == BatchInferImageType.IMAGES_UNLABELED: return datastore.get_unlabeled_images(label_tag, labels) return datastore.list_images() return images
def __call__(self, request, datastore: Datastore, infer: Callable): image_ids = sorted(self.get_images(request, datastore)) max_batch_size = request.get("max_batch_size", 0) label_tag = request.get("label_tag", DefaultLabelTag.ORIGINAL) f"Total number of images for batch inference: {len(image_ids)}; Max Batch Size: {max_batch_size}; Label Tag: {label_tag}" ) start = time.time() if max_batch_size > 0: image_ids = image_ids[:max_batch_size] multi_gpu = request.get("multi_gpu", True) multi_gpus = request.get("gpus", "all") gpus = ( list(range(torch.cuda.device_count())) if not multi_gpus or multi_gpus == "all" else multi_gpus.split(",") ) device = name_to_device(request.get("device", "cuda")) device_ids = [f"cuda:{id}" for id in gpus] if multi_gpu else [device] result: dict = {} infer_tasks = [] for idx, image_id in enumerate(image_ids): req = copy.deepcopy(request) req["_id"] = idx req["_image_id"] = image_id req["image"] = image_id req["save_label"] = True req["label_tag"] = label_tag req["logging"] = request.get("logging", "INFO") req["device"] = device_ids[idx % len(device_ids)] infer_tasks.append(req) result[image_id] = None total = len(infer_tasks) max_workers = request.get("max_workers", 0) max_workers = max_workers if max_workers else max(1, multiprocessing.cpu_count() // 4) max_workers = min(max_workers, multiprocessing.cpu_count()) if len(infer_tasks) > 1 and (max_workers == 0 or max_workers > 1):"MultiGpu: {multi_gpu}; Using Device(s): {device_ids}; Max Workers: {max_workers}") futures = {} with ThreadPoolExecutor(max_workers if max_workers else None, "WSI Infer") as executor: for t in infer_tasks: futures[t["_id"]] = t, executor.submit(run_infer_task, t, datastore, infer) for tid, (t, future) in futures.items(): image_id = t["_image_id"] try: res = future.result() result[image_id] = res except Exception: logger.warning(f"Failed to finish Infer Task: {tid} => {image_id}", exc_info=True) result[image_id] = {} finished = len([a for a in result.values() if a is not None])"{tid} => {image_id} => {t['device']} => {finished} / {total}") else: for t in infer_tasks: tid = t["_id"] res = run_infer_task(t, datastore, infer) image_id = t["_image_id"] result[image_id] = res finished = tid + 1"{tid} => {image_id} => {t['device']} => {finished} / {total}") latency_total = time.time() - start"Batch Infer Time Taken: {latency_total:.4f}") return result
[docs]def run_infer_task(req, datastore, infer): handle_torch_linalg_multithread(req) image_id = req.get("image")"Running inference for image id {image_id}") r = infer(req, datastore) if r.get("file"): remove_file(r.get("file")) r.pop("file") return r