Benchmarks

Here we benchmark the training and testing speed of models in MMDetection3D, with some other open source 3D detection codebases.

Settings

Hardwares: 8 NVIDIA Tesla V100 (32G) GPUs, Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
Software: Python 3.7, CUDA 10.1, cuDNN 7.6.5, PyTorch 1.3, numba 0.48.0.
Model: Since all the other codebases implements different models, we compare the corresponding models including SECOND, PointPillars, Part-A2, and VoteNet with them separately.
Metrics: We use the average throughput in iterations of the entire training run and skip the first 50 iterations of each epoch to skip GPU warmup time.

Main Results

We compare the training speed (samples/s) with other codebases if they implement the similar models. The results are as below, the greater the numbers in the table, the faster of the training process. The models that are not supported by other codebases are marked by ×.

Methods	MMDetection3D	OpenPCDet	votenet	Det3D
VoteNet	358	×	77	×
PointPillars-car	141	×	×	140
PointPillars-3class	107	44	×	×
SECOND	40	30	×	×
Part-A2	17	14	×	×

Details of Comparison

Modification for Calculating Speed

MMDetection3D: We try to use as similar settings as those of other codebases as possible using benchmark configs.
Det3D: For comparison with Det3D, we use the commit 519251e.

OpenPCDet: For comparison with OpenPCDet, we use the commit b32fbddb.

For training speed, we add code to record the running time in the file ./tools/train_utils/train_utils.py. We calculate the speed of each epoch, and report the average speed of all the epochs.

(diff to make it use the same method for benchmarking speed - click to expand)

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):

VoteNet

MMDetection3D: With release v0.1.0, run

./tools/dist_train.sh configs/votenet/votenet_8xb16_sunrgbd-3d.py 8 --no-validate

votenet: At commit 2f6d6d3, run

python train.py --dataset sunrgbd --batch_size 16

Then benchmark the test speed by running

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

Note that eval.py is modified to compute inference time.

(diff to benchmark the similar models - click to expand)

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: With release v0.1.0, run

./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py 8 --no-validate

Det3D: At commit 519251e, use kitti_point_pillars_mghead_syncbn.py and run

./tools/scripts/train.sh --launcher=slurm --gpus=8

Note that the config in train.sh is modified to train point pillars.

(diff to benchmark the similar models - click to expand)

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

PointPillars-3class

MMDetection3D: With release v0.1.0, run

./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate

OpenPCDet: At commit b32fbddb, run

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

For SECOND, we mean the SECONDv1.5 that was first implemented in second.Pytorch. Det3D's implementation of SECOND uses its self-implemented Multi-Group Head, so its speed is not compatible with other codebases.

MMDetection3D: With release v0.1.0, run

./tools/dist_train.sh configs/benchmark/hv_second_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate

OpenPCDet: At commit b32fbddb, run

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: With release v0.1.0, run

./tools/dist_train.sh configs/benchmark/hv_PartA2_secfpn_4x8_cyclic_80e_pcdet_kitti-3d-3class.py 8 --no-validate

OpenPCDet: At commit b32fbddb, train the model by running

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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Modification for Calculating Speed

VoteNet

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SECOND

Part-A2

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Benchmarks

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Details of Comparison

Modification for Calculating Speed

VoteNet

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SECOND

Part-A2