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pcdet benchmark doc

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docs/benchmarks.md
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...@@ -4,7 +4,6 @@ ...@@ -4,7 +4,6 @@
Here we benchmark the training and testing speed of models in MMDetection3D, Here we benchmark the training and testing speed of models in MMDetection3D,
with some other popular open source 3D detection codebases. with some other popular open source 3D detection codebases.
## Settings ## Settings
* Hardwares: 8 NVIDIA Tesla V100 (32G) GPUs, Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz * Hardwares: 8 NVIDIA Tesla V100 (32G) GPUs, Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
...@@ -13,7 +12,6 @@ with some other popular open source 3D detection codebases. ...@@ -13,7 +12,6 @@ with some other popular open source 3D detection codebases.
* 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. * 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.
Note that the throughput of a detector typically changes during training, because it depends on the predictions of the model. Note that the throughput of a detector typically changes during training, because it depends on the predictions of the model.
## Main Results ## Main Results
### VoteNet ### VoteNet
...@@ -33,7 +31,7 @@ We compare our implementation of VoteNet with [votenet](https://github.com/faceb ...@@ -33,7 +31,7 @@ We compare our implementation of VoteNet with [votenet](https://github.com/faceb
### PointPillars ### PointPillars
Since [Det3D](https://github.com/poodarchu/Det3D/) only provides PointPillars on car class while [PCDet](https://github.com/sshaoshuai/PCDet) only provides PointPillars Since [Det3D](https://github.com/poodarchu/Det3D/) only provides PointPillars on car class while [OpenPCDet](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) only provides PointPillars
on 3 classes, we compare with them separately. For performance on single class, we report the AP on moderate on 3 classes, we compare with them separately. For performance on single class, we report the AP on moderate
condition following the KITTI benchmark and compare average AP over all classes on moderate condition for condition following the KITTI benchmark and compare average AP over all classes on moderate condition for
performance on 3 classes. performance on 3 classes.
...@@ -49,46 +47,46 @@ performance on 3 classes. ...@@ -49,46 +47,46 @@ performance on 3 classes.
``` ```
```eval_rst ```eval_rst
+----------------+---------------------+--------------------+ +----------------+---------------------+--------------------+
| Implementation | Training (sample/s) | Testing (sample/s) | | Implementation | Training (sample/s) | Testing (sample/s) |
+================+=====================+====================+ +================+=====================+====================+
| MMDetection3D | 120 | | | MMDetection3D | 107 | 45 |
+----------------+---------------------+--------------------+ +----------------+---------------------+--------------------+
| PCDet | 43 | 64 | | OpenPCDet | 44 | 25 |
+----------------+---------------------+--------------------+ +----------------+---------------------+--------------------+
``` ```
### SECOND ### SECOND
[Det3D](https://github.com/poodarchu/Det3D/) provides a different SECOND on car class and we cannot train the original SECOND by modifying the config. [Det3D](https://github.com/poodarchu/Det3D/) provides a different SECOND on car class and we cannot train the original SECOND by modifying the config.
So we only compare with [PCDet](https://github.com/sshaoshuai/PCDet), which is a SECOND model on 3 classes, we report the AP on moderate So we only compare with [OpenPCDet](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2), which is a SECOND model on 3 classes, we report the AP on moderate
condition following the KITTI benchmark and compare average AP over all classes on moderate condition for condition following the KITTI benchmark and compare average AP over all classes on moderate condition for
performance on 3 classes. performance on 3 classes.
```eval_rst ```eval_rst
+----------------+---------------------+--------------------+ +----------------+---------------------+--------------------+
| Implementation | Training (sample/s) | Testing (sample/s) | | Implementation | Training (sample/s) | Testing (sample/s) |
+================+=====================+====================+ +================+=====================+====================+
| MMDetection3D | 54 | | | MMDetection3D | 40 | 27 |
+----------------+---------------------+--------------------+ +----------------+---------------------+--------------------+
| PCDet | 44 | 30 | | OpenPCDet | 30 | 32 |
+----------------+---------------------+--------------------+ +----------------+---------------------+--------------------+
``` ```
### Part-A2 ### Part-A2
We benchmark Part-A2 with that in [PCDet](https://github.com/sshaoshuai/PCDet). We report the AP on moderate condition following the KITTI benchmark We benchmark Part-A2 with that in [OpenPCDet](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2). We report the AP on moderate condition following the KITTI benchmark
and compare average AP over all classes on moderate condition for performance on 3 classes. and compare average AP over all classes on moderate condition for performance on 3 classes.
```eval_rst ```eval_rst
+----------------+---------------------+--------------------+ +----------------+---------------------+--------------------+
| Implementation | Training (sample/s) | Testing (sample/s) | | Implementation | Training (sample/s) | Testing (sample/s) |
+================+=====================+====================+ +================+=====================+====================+
| MMDetection3D | 17 | | | MMDetection3D | 17 | 11 |
+----------------+---------------------+--------------------+ +----------------+---------------------+--------------------+
| PCDet | 15 | 12 | | OpenPCDet | 14 | 13 |
+----------------+---------------------+--------------------+ +----------------+---------------------+--------------------+
``` ```
## Details of Comparison ## Details of Comparison
...@@ -96,47 +94,188 @@ and compare average AP over all classes on moderate condition for performance on ...@@ -96,47 +94,188 @@ and compare average AP over all classes on moderate condition for performance on
* __Det3D__: At commit 255c593 * __Det3D__: At commit 255c593
* __OpenPCDet__: At commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2)
* __PCDet__: At commit 2244be4
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.
<details>
<summary>
(diff to make it use the same method for benchmarking speed - click to expand)
</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>
For testing speed, we add code to record the running time in the file `./tools/eval_utils/eval_utils.py`.
<details>
<summary>
(diff to make it use the same method for benchmarking speed - click to expand)
</summary>
```diff
diff --git a/tools/eval_utils/eval_utils.py b/tools/eval_utils/eval_utils.py
index 0cbf17b..f51e687 100644
--- a/tools/eval_utils/eval_utils.py
+++ b/tools/eval_utils/eval_utils.py
@@ -49,8 +49,11 @@ def eval_one_epoch(cfg, model, dataloader, epoch_id, logger, dist_test=False, sa
if cfg.LOCAL_RANK == 0:
progress_bar = tqdm.tqdm(total=len(dataloader), leave=True, desc='eval', dynamic_ncols=True)
- start_time = time.time()
+ num_warmup = 5
+ pure_inf_time = 0
for i, batch_dict in enumerate(dataloader):
+ torch.cuda.synchronize()
+ start_time = time.perf_counter()
for key, val in batch_dict.items():
if not isinstance(val, np.ndarray):
continue
@@ -61,7 +64,14 @@ def eval_one_epoch(cfg, model, dataloader, epoch_id, logger, dist_test=False, sa
with torch.no_grad():
pred_dicts, ret_dict = model(batch_dict)
disp_dict = {}
-
+ torch.cuda.synchronize()
+ elapsed = time.perf_counter() - start_time
+ if i >= num_warmup:
+ pure_inf_time += elapsed
+ if (i + 1) == 2000:
+ pure_inf_time += elapsed
+ fps = (i + 1 - num_warmup) / pure_inf_time
+ out_str = f'Overall fps: {fps:.1f} img / s'
statistics_info(cfg, ret_dict, metric, disp_dict)
annos = dataset.generate_prediction_dicts(
batch_dict, pred_dicts, class_names,
@@ -71,7 +81,7 @@ def eval_one_epoch(cfg, model, dataloader, epoch_id, logger, dist_test=False, sa
if cfg.LOCAL_RANK == 0:
progress_bar.set_postfix(disp_dict)
progress_bar.update()
-
+ print(out_str)
if cfg.LOCAL_RANK == 0:
progress_bar.close()
```
</details>
### VoteNet ### VoteNet
* __MMDetection3D__: With release v0.1.0, run * __MMDetection3D__: With release v0.1.0, run
```
./tools/dist_train.sh configs/votenet/votenet_16x8_sunrgbd-3d-10class.py 8 --no-validate ```bash
``` ./tools/dist_train.sh configs/votenet/votenet_16x8_sunrgbd-3d-10class.py 8 --no-validate
```
* __votenet__: At commit 2f6d6d3, run * __votenet__: At commit 2f6d6d3, run
```
python train.py --dataset sunrgbd --batch_size 16
```
```bash
python train.py --dataset sunrgbd --batch_size 16
```
### PointPillars ### PointPillars
* __MMDetection3D__: With release v0.1.0, run * __MMDetection3D__: With release v0.1.0, run
```
./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_6x8_160e_pcdet_kitti-3d-3class.py 8 --no-validate ```bash
``` ./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate
* __PCDet__: At commit xxxx ```
```
./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_6x8_160e_pcdet_kitti-3d-3class.py 8 --no-validate * __OpenPCDet__: At commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2)
```
```bash
cd tools
sh scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8 --cfg_file ./cfgs/pointpillar.yaml --batch_size 32 --workers 32
```
* __MMDetection3D__: With release v0.1.0, run * __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 ```bash
``` /tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py 8 --no-validate
* __Det3D__: At commit 255c593, use kitti_point_pillars_mghead_syncbn.py and run
``` ```
* __Det3D__: At commit 255c593, use kitti_point_pillars_mghead_syncbn.py and run
```bash
./tools/scripts/train.sh --launcher=slurm --gpus=8 ./tools/scripts/train.sh --launcher=slurm --gpus=8
``` ```
Note that the config in train.sh is modified to train point pillars. Note that the config in train.sh is modified to train point pillars.
<details> <details>
<summary> <summary>
(diff to make it use the same hyperparameters - click to expand) (diff to benchmark the similar models - click to expand)
</summary> </summary>
```diff ```diff
...@@ -163,18 +302,29 @@ python train.py --dataset sunrgbd --batch_size 16 ...@@ -163,18 +302,29 @@ python train.py --dataset sunrgbd --batch_size 16
### SECOND ### SECOND
* __MMDetection3D__: With release v0.1.0, run * __MMDetection3D__: With release v0.1.0, run
```
./tools/dist_train.sh configs/benchmark/hv_second_secfpn_6x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate
```
* __PCDet__: At commit 2244be4 ```bash
./tools/dist_train.sh configs/benchmark/hv_second_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate
```
* __OpenPCDet__: At commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2)
```bash
cd tools
sh scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8 --cfg_file ./cfgs/second.yaml --batch_size 32 --workers 32
```
### Part-A2 ### Part-A2
* __MMDetection3D__: With release v0.1.0, run * __MMDetection3D__: With release v0.1.0, run
```
./tools/dist_train.sh configs/benchmark/hv_PartA2_secfpn_2x8_cyclic_80e_pcdet_kitti-3d-3class.py 8 --no-validate
```
* __PCDet__: ```bash
./tools/dist_train.sh configs/benchmark/hv_PartA2_secfpn_4x8_cyclic_80e_pcdet_kitti-3d-3class.py 8 --no-validate
```
* __OpenPCDet__: At commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2)
```bash
cd tools
sh scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8 --cfg_file ./cfgs/PartA2.yaml --batch_size 32 --workers 32
```
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