mmdetection里的测速脚本

由于大论文里需要对各个算法进行测速，因此抛开官方文档的使用说明，记录一下我是怎么使用mmdetection里的脚本进行测速的。

mmdetection版本：2.23.0

一、新版本benchmark.py（需要分布式）

打开tools/analysis_tools/benchmark.py

这里主要需要几个参数：训练的config文件和checkpoint文件

（这边建议config用work_dir保存下来的，跟checkpoint对齐，防止后续修改了config文件夹里的config跟当时训练的不对齐）

因此直接输入命令或者写和sh脚本：

python -m torch.distributed.launch --nproc_per_node=1 --master_port=29500 tools/analysis_tools/benchmark.py \
{CONFIG} \
{CHECKPOINT} \
--launcher pytorch

注意这里一定要加分布式相关的代码，不然会报错，源码里规定了只支持分布式推理测速。。

结果：

二、不用分布式的老版本推理benchmark.py

也可以采用老版本的benchmark.py文件进行推理，这边给出源码，可以自行建个py文件：

import argparse
import time

import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model

from mmdet.datasets import (build_dataloader, build_dataset,
                            replace_ImageToTensor)
from mmdet.models import build_detector


def parse_args():
    parser = argparse.ArgumentParser(description='MMDet benchmark a model')
    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint', help='checkpoint file')
    parser.add_argument(
        '--log-interval', default=50, help='interval of logging')
    parser.add_argument(
        '--fuse-conv-bn',
        action='store_true',
        help='Whether to fuse conv and bn, this will slightly increase'
        'the inference speed')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    args = parser.parse_args()
    return args
 
 
def main():
    args = parse_args()
 
    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)
    # import modules from string list.
    if cfg.get('custom_imports', None):
        from mmcv.utils import import_modules_from_strings
        import_modules_from_strings(**cfg['custom_imports'])
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    cfg.model.pretrained = None
    cfg.data.test.test_mode = True
 
    # build the dataloader
    samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
    if samples_per_gpu > 1:
        # Replace 'ImageToTensor' to 'DefaultFormatBundle'
        cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
    dataset = build_dataset(cfg.data.test)
    data_loader = build_dataloader(
        dataset,
        samples_per_gpu=1,
        workers_per_gpu=cfg.data.workers_per_gpu,
        dist=False,
        shuffle=False)
 
    # build the model and load checkpoint
    cfg.model.train_cfg = None
    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    load_checkpoint(model, args.checkpoint, map_location='cpu')
    if args.fuse_conv_bn:
        model = fuse_conv_bn(model)
 
    model = MMDataParallel(model, device_ids=[0])
 
    model.eval()
 
    # the first several iterations may be very slow so skip them
    num_warmup = 5
    pure_inf_time = 0
 
    # benchmark with 2000 image and take the average
    for i, data in enumerate(data_loader):
 
        torch.cuda.synchronize()
        start_time = time.perf_counter()
 
        with torch.no_grad():
            model(return_loss=False, rescale=True, **data)
 
        torch.cuda.synchronize()
        elapsed = time.perf_counter() - start_time
 
        if i >= num_warmup:
            pure_inf_time += elapsed
            if (i + 1) % args.log_interval == 0:
                fps = (i + 1 - num_warmup) / pure_inf_time
                print(f'Done image [{i + 1:<3}/ 2000], fps: {fps:.1f} img / s')
 
        if (i + 1) == 2000:
            pure_inf_time += elapsed
            fps = (i + 1 - num_warmup) / pure_inf_time
            print(f'Overall fps: {fps:.1f} img / s')
            break
 
 
if __name__ == '__main__':
    main()
 

主要就是不需要设置前面乱七八糟的分布式参数了，直接可以运行，简单上手：

python tools/analysis_tools/benchmark_old.py \
{CONFIG} \
{CHECKPOINT} 

结果：

确实会慢一点，不太懂分布式的底层原理，就先这样吧，能用新版本就新版本吧，多几个参数。

三、合并卷积+BN层

benchmark.py里有个参数：

据说会加快推理速度，试试

加入后（新版本）

python -m torch.distributed.launch --nproc_per_node=1 --master_port=29500 tools/analysis_tools/benchmark.py \
{CONFIG} \
{CHECKPOINT} \
--launcher pytorch --fuse-conv-bn

结果：

?没啥区别。有空再来研究底层原理吧，就先这样