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# 基准测试
这里我们对 MMDetection3D 和其他开源 3D 目标检测代码库中模型的训练速度和测试速度进行了基准测试。
## 配置
- 硬件：8 NVIDIA Tesla V100 (32G) GPUs, Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
- 软件：Python 3.7, CUDA 10.1, cuDNN 7.6.5, PyTorch 1.3, numba 0.48.0.
- 模型：由于不同代码库所实现的模型种类有所不同，在基准测试中我们选择了 SECOND、PointPillars、Part-A2 和 VoteNet 几种模型，分别与其他代码库中的相应模型实现进行了对比。
- 度量方法：我们使用整个训练过程中的平均吞吐量作为度量方法，并跳过每个 epoch 的前 50 次迭代以消除训练预热的影响。
## 主要结果
对于模型的训练速度（样本/秒），我们将 MMDetection3D 与其他实现了相同模型的代码库进行了对比。结果如下所示，表格内的数字越大，代表模型的训练速度越快。代码库中不支持的模型使用 `×` 进行标识。
| 模型 | MMDetection3D | OpenPCDet | votenet | Det3D |
| :-----------------: | :-----------: | :-------: | :-----: | :---: |
| VoteNet | 358 | × | 77 | × |
| PointPillars-car | 141 | × | × | 140 |
| PointPillars-3class | 107 | 44 | × | × |
| SECOND | 40 | 30 | × | × |
| Part-A2 | 17 | 14 | × | × |
## 测试细节
### 为了计算速度所做的修改
- __MMDetection3D__：我们尝试使用与其他代码库中尽可能相同的配置，具体配置细节见 [基准测试配置](https://github.com/open-mmlab/MMDetection3D/blob/main/configs/benchmark)。
- __Det3D__：为了与 Det3D 进行比较，我们使用了 commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7) 所对应的代码版本。
- __OpenPCDet__：为了与 OpenPCDet 进行比较，我们使用了 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 所对应的代码版本。
为了计算训练速度，我们在 `./tools/train_utils/train_utils.py` 文件中添加了用于记录运行时间的代码。我们对每个 epoch 的训练速度进行计算，并报告所有 epoch 的平均速度。
<details>
<summary>
（为了使用相同方法进行测试所做的具体修改 - 点击展开）
</summary>
```diff
diff --git a/tools/train_utils/train_utils.py b/tools/train_utils/train_utils.py
index 91f21dd..021359d 100644
--- a/tools/train_utils/train_utils.py
+++ b/tools/train_utils/train_utils.py
@@ -2,6 +2,7 @@ import torch
import os
import glob
import tqdm
+import datetime
from torch.nn.utils import clip_grad_norm_
@@ -13,7 +14,10 @@ def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, ac
if rank == 0:
pbar = tqdm.tqdm(total=total_it_each_epoch, leave=leave_pbar, desc='train', dynamic_ncols=True)
+ start_time = None
for cur_it in range(total_it_each_epoch):
+ if cur_it > 49 and start_time is None:
+ start_time = datetime.datetime.now()
try:
batch = next(dataloader_iter)
except StopIteration:
@@ -55,9 +59,11 @@ def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, ac
tb_log.add_scalar('learning_rate', cur_lr, accumulated_iter)
for key, val in tb_dict.items():
tb_log.add_scalar('train_' + key, val, accumulated_iter)
+ endtime = datetime.datetime.now()
+ speed = (endtime - start_time).seconds / (total_it_each_epoch - 50)
if rank == 0:
pbar.close()
- return accumulated_iter
+ return accumulated_iter, speed
def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_cfg,
@@ -65,6 +71,7 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
lr_warmup_scheduler=None, ckpt_save_interval=1, max_ckpt_save_num=50,
merge_all_iters_to_one_epoch=False):
accumulated_iter = start_iter
+ speeds = []
with tqdm.trange(start_epoch, total_epochs, desc='epochs', dynamic_ncols=True, leave=(rank == 0)) as tbar:
total_it_each_epoch = len(train_loader)
if merge_all_iters_to_one_epoch:
@@ -82,7 +89,7 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
cur_scheduler = lr_warmup_scheduler
else:
cur_scheduler = lr_scheduler
- accumulated_iter = train_one_epoch(
+ accumulated_iter, speed = train_one_epoch(
model, optimizer, train_loader, model_func,
lr_scheduler=cur_scheduler,
accumulated_iter=accumulated_iter, optim_cfg=optim_cfg,
@@ -91,7 +98,7 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
total_it_each_epoch=total_it_each_epoch,
dataloader_iter=dataloader_iter
)
-
+ speeds.append(speed)
# save trained model
trained_epoch = cur_epoch + 1
if trained_epoch % ckpt_save_interval == 0 and rank == 0:
@@ -107,6 +114,8 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
save_checkpoint(
checkpoint_state(model, optimizer, trained_epoch, accumulated_iter), filename=ckpt_name,
)
+ print(speed)
+ print(f'*******{sum(speeds) / len(speeds)}******')
def model_state_to_cpu(model_state):
```
</details>
### VoteNet
- __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
```bash
./tools/dist_train.sh configs/votenet/votenet_8xb16_sunrgbd-3d.py 8 --no-validate
```
- __votenet__：在 commit [2f6d6d3](https://github.com/facebookresearch/votenet/tree/2f6d6d36ff98d96901182e935afe48ccee82d566) 版本下，执行如下命令：
```bash
python train.py --dataset sunrgbd --batch_size 16
```
然后执行如下命令，对测试速度进行评估：
```bash
python eval.py --dataset sunrgbd --checkpoint_path log_sunrgbd/checkpoint.tar --batch_size 1 --dump_dir eval_sunrgbd --cluster_sampling seed_fps --use_3d_nms --use_cls_nms --per_class_proposal
```
注意，为了计算推理速度，我们对 `eval.py` 进行了修改。
<details>
<summary>
（为了对相同模型进行测试所做的具体修改 - 点击展开）
</summary>
```diff
diff --git a/eval.py b/eval.py
index c0b2886..04921e9 100644
--- a/eval.py
+++ b/eval.py
@@ -10,6 +10,7 @@ import os
import sys
import numpy as np
from datetime import datetime
+import time
import argparse
import importlib
import torch
@@ -28,7 +29,7 @@ parser.add_argument('--checkpoint_path', default=None, help='Model checkpoint pa
parser.add_argument('--dump_dir', default=None, help='Dump dir to save sample outputs [default: None]')
parser.add_argument('--num_point', type=int, default=20000, help='Point Number [default: 20000]')
parser.add_argument('--num_target', type=int, default=256, help='Point Number [default: 256]')
-parser.add_argument('--batch_size', type=int, default=8, help='Batch Size during training [default: 8]')
+parser.add_argument('--batch_size', type=int, default=1, help='Batch Size during training [default: 8]')
parser.add_argument('--vote_factor', type=int, default=1, help='Number of votes generated from each seed [default: 1]')
parser.add_argument('--cluster_sampling', default='vote_fps', help='Sampling strategy for vote clusters: vote_fps, seed_fps, random [default: vote_fps]')
parser.add_argument('--ap_iou_thresholds', default='0.25,0.5', help='A list of AP IoU thresholds [default: 0.25,0.5]')
@@ -132,6 +133,7 @@ CONFIG_DICT = {'remove_empty_box': (not FLAGS.faster_eval), 'use_3d_nms': FLAGS.
# ------------------------------------------------------------------------- GLOBAL CONFIG END
def evaluate_one_epoch():
+ time_list = list()
stat_dict = {}
ap_calculator_list = [APCalculator(iou_thresh, DATASET_CONFIG.class2type) \
for iou_thresh in AP_IOU_THRESHOLDS]
@@ -144,6 +146,8 @@ def evaluate_one_epoch():
# Forward pass
inputs = {'point_clouds': batch_data_label['point_clouds']}
+ torch.cuda.synchronize()
+ start_time = time.perf_counter()
with torch.no_grad():
end_points = net(inputs)
@@ -161,6 +165,12 @@ def evaluate_one_epoch():
batch_pred_map_cls = parse_predictions(end_points, CONFIG_DICT)
batch_gt_map_cls = parse_groundtruths(end_points, CONFIG_DICT)
+ torch.cuda.synchronize()
+ elapsed = time.perf_counter() - start_time
+ time_list.append(elapsed)
+
+ if len(time_list==200):
+ print("average inference time: %4f"%(sum(time_list[5:])/len(time_list[5:])))
for ap_calculator in ap_calculator_list:
ap_calculator.step(batch_pred_map_cls, batch_gt_map_cls)
```
### PointPillars-car
- __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
```bash
./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py 8 --no-validate
```
- __Det3D__：在 commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7) 版本下，使用 `kitti_point_pillars_mghead_syncbn.py` 并执行如下命令：
```bash
./tools/scripts/train.sh --launcher=slurm --gpus=8
```
注意，为了训练 PointPillars，我们对 `train.sh` 进行了修改。
<details>
<summary>
（为了对相同模型进行测试所做的具体修改 - 点击展开）
</summary>
```diff
diff --git a/tools/scripts/train.sh b/tools/scripts/train.sh
index 3a93f95..461e0ea 100755
--- a/tools/scripts/train.sh
+++ b/tools/scripts/train.sh
@@ -16,9 +16,9 @@ then
fi
# Voxelnet
-python -m torch.distributed.launch --nproc_per_node=8 ./tools/train.py examples/second/configs/ kitti_car_vfev3_spmiddlefhd_rpn1_mghead_syncbn.py --work_dir=$SECOND_WORK_DIR
+# python -m torch.distributed.launch --nproc_per_node=8 ./tools/train.py examples/second/configs/ kitti_car_vfev3_spmiddlefhd_rpn1_mghead_syncbn.py --work_dir=$SECOND_WORK_DIR
# python -m torch.distributed.launch --nproc_per_node=8 ./tools/train.py examples/cbgs/configs/ nusc_all_vfev3_spmiddleresnetfhd_rpn2_mghead_syncbn.py --work_dir=$NUSC_CBGS_WORK_DIR
# python -m torch.distributed.launch --nproc_per_node=8 ./tools/train.py examples/second/configs/ lyft_all_vfev3_spmiddleresnetfhd_rpn2_mghead_syncbn.py --work_dir=$LYFT_CBGS_WORK_DIR
# PointPillars
-# python -m torch.distributed.launch --nproc_per_node=8 ./tools/train.py ./examples/point_pillars/configs/ original_pp_mghead_syncbn_kitti.py --work_dir=$PP_WORK_DIR
+python -m torch.distributed.launch --nproc_per_node=8 ./tools/train.py ./examples/point_pillars/configs/ kitti_point_pillars_mghead_syncbn.py
```
</details>
### PointPillars-3class
- __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
```bash
./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate
```
- __OpenPCDet__：在 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 版本下，执行如下命令：
```bash
cd tools
sh scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8 --cfg_file ./cfgs/kitti_models/pointpillar.yaml --batch_size 32 --workers 32 --epochs 80
```
### SECOND
基准测试中的 SECOND 指在 [second.Pytorch](https://github.com/traveller59/second.pytorch) 首次被实现的 [SECONDv1.5](https://github.com/traveller59/second.pytorch/blob/master/second/configs/all.fhd.config)。Det3D 实现的 SECOND 中，使用了自己实现的 Multi-Group Head，因此无法将它的速度与其他代码库进行对比。
- __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
```bash
./tools/dist_train.sh configs/benchmark/hv_second_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate
```
- __OpenPCDet__：在 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 版本下，执行如下命令：
```bash
cd tools
sh ./scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8 --cfg_file ./cfgs/kitti_models/second.yaml --batch_size 32 --workers 32 --epochs 80
```
### Part-A2
- __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
```bash
./tools/dist_train.sh configs/benchmark/hv_PartA2_secfpn_4x8_cyclic_80e_pcdet_kitti-3d-3class.py 8 --no-validate
```
- __OpenPCDet__：在 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 版本下，执行如下命令以进行模型训练：
```bash
cd tools
sh ./scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8 --cfg_file ./cfgs/kitti_models/PartA2.yaml --batch_size 32 --workers 32 --epochs 80
```
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