benchmark.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import time
import torch
from mmcv import Config
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model
import sys

sys.path.append(".")
from projects.mmdet3d_plugin.datasets.builder import build_dataloader
from projects.mmdet3d_plugin.datasets import custom_build_dataset

# from mmdet3d.datasets import build_dataloader, build_dataset
from mmdet.models import build_detector

from tools.fuse_conv_bn import fuse_module


def parse_args():
    parser = argparse.ArgumentParser(description="MMDet benchmark a model")
    parser.add_argument("config", help="test config file path")
    parser.add_argument("--checkpoint", default=None, help="checkpoint file")
    parser.add_argument("--samples", default=2000, help="samples to benchmark")
    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",
    )
    args = parser.parse_args()
    return args


def get_max_memory(model):
    device = getattr(model, "output_device", None)
    mem = torch.cuda.max_memory_allocated(device=device)
    mem_mb = torch.tensor(
        [mem / (1024 * 1024)], dtype=torch.int, device=device
    )
    return mem_mb.item()


def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # 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
    # TODO: support multiple images per gpu (only minor changes are needed)
    print(cfg.data.test)
    dataset = custom_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)
    if args.checkpoint is not None:
        load_checkpoint(model, args.checkpoint, map_location="cpu")
    if args.fuse_conv_bn:
        model = fuse_module(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 several samples and take the average
    max_memory = 0
    for i, data in enumerate(data_loader):
        # torch.cuda.synchronize()
        with torch.no_grad():
            start_time = time.perf_counter()
            model(return_loss=False, rescale=True, **data)

            torch.cuda.synchronize()
            elapsed = time.perf_counter() - start_time
            max_memory = max(max_memory, get_max_memory(model))

        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}/ {args.samples}], "
                    f"fps: {fps:.1f} img / s, "
                    f"gpu mem: {max_memory} M"
                )

        if (i + 1) == args.samples:
            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()