jeonghyo.k
3946ff6a25
- prediction/image/ FastAPI 서버 Docker 환경 구성 - Dockerfile: PyTorch 2.1 + CUDA 12.1 기반 GPU 이미지 - docker-compose.yml: GPU 할당 + 데이터 볼륨 마운트 - requirements.txt: 서버 의존성 목록 - .env.example: 환경변수 템플릿 - DOCKER_USAGE.md: 빌드/실행/API 사용법 문서 - Dockerfile에 .dockerignore 제외 폴더 mkdir -p 추가 - .gitignore: prediction/image 결과물 및 모델 가중치(.pth) 제외 추가 - dbInsert_csv.py, dbInsert_shp.py 삭제 (미사용 DB 로직) - api.py: dbInsert import 및 주석 처리된 DB 호출 코드 제거 - aerialRouter.ts: req.params 타입 오류 수정
121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os.path as osp
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import time
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import mmcv
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import numpy as np
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import torch
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from mmcv import Config
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from mmcv.parallel import MMDataParallel
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from mmcv.runner import load_checkpoint, wrap_fp16_model
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from mmseg.datasets import build_dataloader, build_dataset
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from mmseg.models import build_segmentor
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def parse_args():
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parser = argparse.ArgumentParser(description='MMSeg benchmark a model')
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parser.add_argument('config', help='test config file path')
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parser.add_argument('checkpoint', help='checkpoint file')
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parser.add_argument(
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'--log-interval', type=int, default=50, help='interval of logging')
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parser.add_argument(
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'--work-dir',
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help=('if specified, the results will be dumped '
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'into the directory as json'))
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parser.add_argument('--repeat-times', type=int, default=1)
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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cfg = Config.fromfile(args.config)
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timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
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if args.work_dir is not None:
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mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
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json_file = osp.join(args.work_dir, f'fps_{timestamp}.json')
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else:
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# use config filename as default work_dir if cfg.work_dir is None
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work_dir = osp.join('./work_dirs',
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osp.splitext(osp.basename(args.config))[0])
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mmcv.mkdir_or_exist(osp.abspath(work_dir))
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json_file = osp.join(work_dir, f'fps_{timestamp}.json')
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repeat_times = args.repeat_times
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# set cudnn_benchmark
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torch.backends.cudnn.benchmark = False
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cfg.model.pretrained = None
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cfg.data.test.test_mode = True
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benchmark_dict = dict(config=args.config, unit='img / s')
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overall_fps_list = []
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for time_index in range(repeat_times):
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print(f'Run {time_index + 1}:')
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# build the dataloader
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# TODO: support multiple images per gpu (only minor changes are needed)
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dataset = build_dataset(cfg.data.test)
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=1,
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workers_per_gpu=cfg.data.workers_per_gpu,
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dist=False,
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shuffle=False)
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# build the model and load checkpoint
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cfg.model.train_cfg = None
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model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
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fp16_cfg = cfg.get('fp16', None)
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if fp16_cfg is not None:
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wrap_fp16_model(model)
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if 'checkpoint' in args and osp.exists(args.checkpoint):
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load_checkpoint(model, args.checkpoint, map_location='cpu')
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model = MMDataParallel(model, device_ids=[0])
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model.eval()
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# the first several iterations may be very slow so skip them
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num_warmup = 5
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pure_inf_time = 0
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total_iters = 200
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# benchmark with 200 image and take the average
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for i, data in enumerate(data_loader):
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torch.cuda.synchronize()
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start_time = time.perf_counter()
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with torch.no_grad():
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model(return_loss=False, rescale=True, **data)
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torch.cuda.synchronize()
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elapsed = time.perf_counter() - start_time
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if i >= num_warmup:
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pure_inf_time += elapsed
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if (i + 1) % args.log_interval == 0:
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fps = (i + 1 - num_warmup) / pure_inf_time
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print(f'Done image [{i + 1:<3}/ {total_iters}], '
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f'fps: {fps:.2f} img / s')
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if (i + 1) == total_iters:
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fps = (i + 1 - num_warmup) / pure_inf_time
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print(f'Overall fps: {fps:.2f} img / s\n')
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benchmark_dict[f'overall_fps_{time_index + 1}'] = round(fps, 2)
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overall_fps_list.append(fps)
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break
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benchmark_dict['average_fps'] = round(np.mean(overall_fps_list), 2)
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benchmark_dict['fps_variance'] = round(np.var(overall_fps_list), 4)
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print(f'Average fps of {repeat_times} evaluations: '
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f'{benchmark_dict["average_fps"]}')
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print(f'The variance of {repeat_times} evaluations: '
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f'{benchmark_dict["fps_variance"]}')
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mmcv.dump(benchmark_dict, json_file, indent=4)
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if __name__ == '__main__':
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main()
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