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Support Waymo Open Dataset with SoTA results #349

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README.md
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......@@ -18,12 +18,15 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
## Changelog
[2020-08-10] **NEW:** Bugfixed: The provided NuScenes models have been updated to fix the loading bugs. Please redownload it if you need to use the pretrained NuScenes models.
[2020-11-10] **NEW:** The [Waymo Open Dataset](#waymo-open-dataset-baselines) has been supported with state-of-the-art results. Currently we provide the
configs and results of `SECOND`, `PartA2` and `PV-RCNN` on the Waymo Open Dataset, and more models could be easily supported by modifying their dataset configs.
[2020-07-30] **NEW:** `OpenPCDet` v0.3.0 is released with the following features:
[2020-08-10] Bugfixed: The provided NuScenes models have been updated to fix the loading bugs. Please redownload it if you need to use the pretrained NuScenes models.
[2020-07-30] `OpenPCDet` v0.3.0 is released with the following features:
* The Point-based and Anchor-Free models ([`PointRCNN`](#KITTI-3D-Object-Detection-Baselines), [`PartA2-Free`](#KITTI-3D-Object-Detection-Baselines)) are supported now.
* The NuScenes dataset is supported with strong baseline results ([`SECOND-MultiHead (CBGS)`](#NuScenes-3D-Object-Detection-Baselines) and [`PointPillar-MultiHead`](#NuScenes-3D-Object-Detection-Baselines)).
* High efficiency than last version, support `PyTorch 1.1~1.5` and `spconv 1.0~1.2` simultaneously.
* High efficiency than last version, support **PyTorch 1.1~1.7** and **spconv 1.0~1.2** simultaneously.
[2020-07-17] Add simple visualization codes and a quick demo to test with custom data.
......@@ -87,7 +90,7 @@ Selected supported methods are shown in the below table. The results are the 3D
* All models are trained with 8 GTX 1080Ti GPUs and are available for download.
* The training time is measured with 8 TITAN XP GPUs and PyTorch 1.5.
| | training time | Car | Pedestrian | Cyclist | download |
| | training time | Car@R11 | Pedestrian@R11 | Cyclist@R11 | download |
|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:---------:|
| [PointPillar](tools/cfgs/kitti_models/pointpillar.yaml) |~1.2 hours| 77.28 | 52.29 | 62.68 | [model-18M](https://drive.google.com/file/d/1wMxWTpU1qUoY3DsCH31WJmvJxcjFXKlm/view?usp=sharing) |
| [SECOND](tools/cfgs/kitti_models/second.yaml) | ~1.7 hours | 78.62 | 52.98 | 67.15 | [model-20M](https://drive.google.com/file/d/1-01zsPOsqanZQqIIyy7FpNXStL3y4jdR/view?usp=sharing) |
......@@ -105,6 +108,22 @@ All models are trained with 8 GTX 1080Ti GPUs and are available for download.
| [PointPillar-MultiHead](tools/cfgs/nuscenes_models/cbgs_pp_multihead.yaml) | 33.87 | 26.00 | 32.07 | 28.74 | 20.15 | 44.63 | 58.23 | [model-23M](https://drive.google.com/file/d/1p-501mTWsq0G9RzroTWSXreIMyTUUpBM/view?usp=sharing) |
| [SECOND-MultiHead (CBGS)](tools/cfgs/nuscenes_models/cbgs_second_multihead.yaml) | 31.15 | 25.51 | 26.64 | 26.26 | 20.46 | 50.59 | 62.29 | [model-35M](https://drive.google.com/file/d/1bNzcOnE3u9iooBFMk2xK7HqhdeQ_nwTq/view?usp=sharing) |
### Waymo Open Dataset Baselines
We provide the setting of [`DATA_CONFIG.SAMPLED_INTERVAL`](tools/cfgs/dataset_configs/waymo_dataset.yaml) on the Waymo Open Dataset (WOD) to subsample partial samples for training and evaluation,
so you could also play with WOD by setting a smaller `DATA_CONFIG.SAMPLED_INTERVAL` even if you only have limited GPU resources.
By default, all models are trained with **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set (version 1.2).
| | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |
|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
| [SECOND](tools/cfgs/waymo_models/second.yaml) | 68.03/67.44 | 59.57/59.04 | 61.14/50.33 | 53.00/43.56 | 54.66/53.31 | 52.67/51.37 |
| [Part-A^2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 71.82/71.29 | 64.33/63.82 | 63.15/54.96 | 54.24/47.11 | 65.23/63.92 | 62.61/61.35 |
| [PV-RCNN](tools/cfgs/waymo_models/pv_rcnn.yaml) | 74.06/73.38 | 64.99/64.38 | 62.66/52.68 | 53.80/45.14 | 63.32/61.71 | 60.72/59.18 |
We could not provide the above pretrained models due to [Waymo Dataset License Agreement](https://waymo.com/open/terms/),
but you could easily achieve similar performance by training with the default configs.
### Other datasets
More datasets are on the way.
......@@ -140,31 +159,15 @@ If you find this project useful in your research, please consider cite:
```
@inproceedings{shi2020pv,
title={Pv-rcnn: Point-voxel feature set abstraction for 3d object detection},
author={Shi, Shaoshuai and Guo, Chaoxu and Jiang, Li and Wang, Zhe and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10529--10538},
@misc{openpcdet2020,
title={OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds},
author={OpenPCDet Development Team},
howpublished = {\url{https://github.com/open-mmlab/OpenPCDet}},
year={2020}
}
```
## Contribution
Welcome to be a member of the OpenPCDet development team by contributing to this repo, and feel free to contact us for any potential contributions.
@article{shi2020points,
title={From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network},
author={Shi, Shaoshuai and Wang, Zhe and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2020},
publisher={IEEE}
}
@inproceedings{shi2019pointrcnn,
title={PointRCNN: 3d Object Progposal Generation and Detection from Point Cloud},
author={Shi, Shaoshuai and Wang, Xiaogang and Li, Hongsheng},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={770--779},
year={2019}
}
```
## Contact
This project is currently maintained by Shaoshuai Shi ([@sshaoshuai](http://github.com/sshaoshuai)) and Chaoxu Guo ([@Gus-Guo](https://github.com/Gus-Guo)).
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segment-8679184381783013073_7740_000_7760_000_with_camera_labels.tfrecord
segment-8845277173853189216_3828_530_3848_530_with_camera_labels.tfrecord
segment-8888517708810165484_1549_770_1569_770_with_camera_labels.tfrecord
segment-8907419590259234067_1960_000_1980_000_with_camera_labels.tfrecord
segment-89454214745557131_3160_000_3180_000_with_camera_labels.tfrecord
segment-8956556778987472864_3404_790_3424_790_with_camera_labels.tfrecord
segment-902001779062034993_2880_000_2900_000_with_camera_labels.tfrecord
segment-9024872035982010942_2578_810_2598_810_with_camera_labels.tfrecord
segment-9041488218266405018_6454_030_6474_030_with_camera_labels.tfrecord
segment-9114112687541091312_1100_000_1120_000_with_camera_labels.tfrecord
segment-9164052963393400298_4692_970_4712_970_with_camera_labels.tfrecord
segment-9231652062943496183_1740_000_1760_000_with_camera_labels.tfrecord
segment-9243656068381062947_1297_428_1317_428_with_camera_labels.tfrecord
segment-9265793588137545201_2981_960_3001_960_with_camera_labels.tfrecord
segment-933621182106051783_4160_000_4180_000_with_camera_labels.tfrecord
segment-9443948810903981522_6538_870_6558_870_with_camera_labels.tfrecord
segment-9472420603764812147_850_000_870_000_with_camera_labels.tfrecord
segment-9579041874842301407_1300_000_1320_000_with_camera_labels.tfrecord
segment-967082162553397800_5102_900_5122_900_with_camera_labels.tfrecord
docs/DEMO.md
View file @ a6bb3580
......@@ -23,7 +23,7 @@ y-axis points towards to the left direction, and z-axis points towards to the to
...
# Save it to the file.
# The shape of points should be (num_points, 4), that is [x, y, z, intensity],
# The shape of points should be (num_points, 4), that is [x, y, z, intensity] (Only for KITTI dataset).
# If you doesn't have the intensity information, just set them to zeros.
# If you have the intensity information, you should normalize them to [0, 1].
points[:, 3] = 0
......
docs/GETTING_STARTED.md
View file @ a6bb3580
......@@ -57,6 +57,43 @@ python -m pcdet.datasets.nuscenes.nuscenes_dataset --func create_nuscenes_infos
--version v1.0-trainval
```
### Waymo Open Dataset
* Please download the official [Waymo Open Dataset](https://waymo.com/open/download/),
including the training data `training_0000.tar~training_0031.tar` and the validation
data `validation_0000.tar~validation_0007.tar`.
* Unzip all the above `xxxx.tar` files to the directory of `data/waymo/raw_data` as follows (You could get 798 *train* tfrecord and 202 *val* tfrecord ):
```
OpenPCDet
├── data
│ ├── waymo
│ │ │── ImageSets
│ │ │── raw_data
│ │ │ │── segment-xxxxxxxx.tfrecord
| | | |── ...
| | |── waymo_processed_data
│ │ │ │── segment-xxxxxxxx/
| | | |── ...
│ │ │── pcdet_gt_database_train_sampled_xx/
│ │ │── pcdet_waymo_dbinfos_train_sampled_xx.pkl
├── pcdet
├── tools
```
* Install the official `waymo-open-dataset` by running the following command:
```shell script
pip3 install --upgrade pip
# tf 2.0.0
pip3 install waymo-open-dataset-tf-2-0-0==1.2.0 --user
```
* Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours,
and you could refer to `data/waymo/waymo_processed_data` to see how many records that have been processed):
```python
python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
--cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml
```
Note that you do not need to install `waymo-open-dataset` if you have already processed the data before and do not need to evaluate with official Waymo Metrics.
## Training & Testing
......
pcdet/datasets/__init__.py
View file @ a6bb3580
......@@ -7,11 +7,13 @@ from pcdet.utils import common_utils
from .dataset import DatasetTemplate
from .kitti.kitti_dataset import KittiDataset
from .nuscenes.nuscenes_dataset import NuScenesDataset
from .waymo.waymo_dataset import WaymoDataset
__all__ = {
'DatasetTemplate': DatasetTemplate,
'KittiDataset': KittiDataset,
'NuScenesDataset': NuScenesDataset
'NuScenesDataset': NuScenesDataset,
'WaymoDataset': WaymoDataset
}
......
pcdet/datasets/waymo/waymo_dataset.py 0 → 100644
View file @ a6bb3580
# OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
# Reference https://github.com/open-mmlab/OpenPCDet
# Written by Shaoshuai Shi, Chaoxu Guo
# All Rights Reserved 2019-2020.
import os
import pickle
import copy
import numpy as np
import torch
from pathlib import Path
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import box_utils, common_utils
from ..dataset import DatasetTemplate
class WaymoDataset(DatasetTemplate):
def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
super().__init__(
dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
)
self.data_path = self.root_path / self.dataset_cfg.PROCESSED_DATA_TAG
self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
self.sample_sequence_list = [x.strip() for x in open(split_dir).readlines()]
self.infos = []
self.include_waymo_data(self.mode)
def set_split(self, split):
super().__init__(
dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training,
root_path=self.root_path, logger=self.logger
)
self.split = split
split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
self.sample_sequence_list = [x.strip() for x in open(split_dir).readlines()]
self.infos = []
self.include_waymo_data(self.mode)
def include_waymo_data(self, mode):
self.logger.info('Loading Waymo dataset')
waymo_infos = []
num_skipped_infos = 0
for k in range(len(self.sample_sequence_list)):
sequence_name = os.path.splitext(self.sample_sequence_list[k])[0]
info_path = self.data_path / sequence_name / ('%s.pkl' % sequence_name)
info_path = self.check_sequence_name_with_all_version(info_path)
if not info_path.exists():
num_skipped_infos += 1
continue
with open(info_path, 'rb') as f:
infos = pickle.load(f)
waymo_infos.extend(infos)
self.infos.extend(waymo_infos[:])
self.logger.info('Total skipped info %s' % num_skipped_infos)
self.logger.info('Total samples for Waymo dataset: %d' % (len(waymo_infos)))
if self.dataset_cfg.SAMPLED_INTERVAL[mode] > 1:
sampled_waymo_infos = []
for k in range(0, len(self.infos), self.dataset_cfg.SAMPLED_INTERVAL[mode]):
sampled_waymo_infos.append(self.infos[k])
self.infos = sampled_waymo_infos
self.logger.info('Total sampled samples for Waymo dataset: %d' % len(self.infos))
@staticmethod
def check_sequence_name_with_all_version(sequence_file):
if '_with_camera_labels' not in str(sequence_file) and not sequence_file.exists():
sequence_file = Path(str(sequence_file[:-9]) + '_with_camera_labels.tfrecord')
if '_with_camera_labels' in str(sequence_file) and not sequence_file.exists():
sequence_file = Path(str(sequence_file).replace('_with_camera_labels', ''))
return sequence_file
def get_infos(self, raw_data_path, save_path, num_workers=4, has_label=True, sampled_interval=1):
import concurrent.futures as futures
from functools import partial
from . import waymo_utils
print('---------------The waymo sample interval is %d, total sequecnes is %d-----------------'
% (sampled_interval, len(self.sample_sequence_list)))
process_single_sequence = partial(
waymo_utils.process_single_sequence,
save_path=save_path, sampled_interval=sampled_interval, has_label=has_label
)
sample_sequence_file_list = [
self.check_sequence_name_with_all_version(raw_data_path / sequence_file)
for sequence_file in self.sample_sequence_list
]
# process_single_sequence(sample_sequence_file_list[0])
with futures.ThreadPoolExecutor(num_workers) as executor:
sequence_infos = executor.map(process_single_sequence, sample_sequence_file_list)
sequence_infos = list(sequence_infos)
all_sequences_infos = [item for infos in sequence_infos for item in infos]
return all_sequences_infos
def get_lidar(self, sequence_name, sample_idx):
lidar_file = self.data_path / sequence_name / ('%04d.npy' % sample_idx)
point_features = np.load(lidar_file) # (N, 7): [x, y, z, intensity, elongation, NLZ_flag]
points_all, NLZ_flag = point_features[:, 0:5], point_features[:, 5]
points_all = points_all[NLZ_flag == -1]
points_all[:, 3] = np.tanh(points_all[:, 3])
return points_all
def __len__(self):
if self._merge_all_iters_to_one_epoch:
return len(self.infos) * self.total_epochs
return len(self.infos)
def __getitem__(self, index):
if self._merge_all_iters_to_one_epoch:
index = index % len(self.infos)
info = copy.deepcopy(self.infos[index])
pc_info = info['point_cloud']
sequence_name = pc_info['lidar_sequence']
sample_idx = pc_info['sample_idx']
points = self.get_lidar(sequence_name, sample_idx)
input_dict = {
'points': points,
'frame_id': info['frame_id'],
}
if 'annos' in info:
annos = info['annos']
annos = common_utils.drop_info_with_name(annos, name='unknown')
if self.dataset_cfg.get('INFO_WITH_FAKELIDAR', False):
gt_boxes_lidar = box_utils.boxes3d_kitti_fakelidar_to_lidar(annos['gt_boxes_lidar'])
else:
gt_boxes_lidar = annos['gt_boxes_lidar']
input_dict.update({
'gt_names': annos['name'],
'gt_boxes': gt_boxes_lidar,
'num_points_in_gt': annos.get('num_points_in_gt', None)
})
data_dict = self.prepare_data(data_dict=input_dict)
data_dict['metadata'] = info.get('metadata', info['frame_id'])
data_dict.pop('num_points_in_gt', None)
return data_dict
@staticmethod
def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
"""
Args:
batch_dict:
frame_id:
pred_dicts: list of pred_dicts
pred_boxes: (N, 7), Tensor
pred_scores: (N), Tensor
pred_labels: (N), Tensor
class_names:
output_path:
Returns:
"""
def get_template_prediction(num_samples):
ret_dict = {
'name': np.zeros(num_samples), 'score': np.zeros(num_samples),
'boxes_lidar': np.zeros([num_samples, 7])
}
return ret_dict
def generate_single_sample_dict(box_dict):
pred_scores = box_dict['pred_scores'].cpu().numpy()
pred_boxes = box_dict['pred_boxes'].cpu().numpy()
pred_labels = box_dict['pred_labels'].cpu().numpy()
pred_dict = get_template_prediction(pred_scores.shape[0])
if pred_scores.shape[0] == 0:
return pred_dict
pred_dict['name'] = np.array(class_names)[pred_labels - 1]
pred_dict['score'] = pred_scores
pred_dict['boxes_lidar'] = pred_boxes
return pred_dict
annos = []
for index, box_dict in enumerate(pred_dicts):
single_pred_dict = generate_single_sample_dict(box_dict)
single_pred_dict['frame_id'] = batch_dict['frame_id'][index]
single_pred_dict['metadata'] = batch_dict['metadata'][index]
annos.append(single_pred_dict)
return annos
def evaluation(self, det_annos, class_names, **kwargs):
if 'annos' not in self.infos[0].keys():
return 'No ground-truth boxes for evaluation', {}
def kitti_eval(eval_det_annos, eval_gt_annos):
from ..kitti.kitti_object_eval_python import eval as kitti_eval
from ..kitti import kitti_utils
map_name_to_kitti = {
'Vehicle': 'Car',
'Pedestrian': 'Pedestrian',
'Cyclist': 'Cyclist',
'Sign': 'Sign',
'Car': 'Car'
}
kitti_utils.transform_annotations_to_kitti_format(eval_det_annos, map_name_to_kitti=map_name_to_kitti)
kitti_utils.transform_annotations_to_kitti_format(
eval_gt_annos, map_name_to_kitti=map_name_to_kitti,
info_with_fakelidar=self.dataset_cfg.get('INFO_WITH_FAKELIDAR', False)
)
kitti_class_names = [map_name_to_kitti[x] for x in class_names]
ap_result_str, ap_dict = kitti_eval.get_official_eval_result(
gt_annos=eval_gt_annos, dt_annos=eval_det_annos, current_classes=kitti_class_names
)
return ap_result_str, ap_dict
def waymo_eval(eval_det_annos, eval_gt_annos):
from .waymo_eval import OpenPCDetWaymoDetectionMetricsEstimator
eval = OpenPCDetWaymoDetectionMetricsEstimator()
ap_dict = eval.waymo_evaluation(
eval_det_annos, eval_gt_annos, class_name=class_names,
distance_thresh=1000, fake_gt_infos=self.dataset_cfg.get('INFO_WITH_FAKELIDAR', False)
)
ap_result_str = '\n'
for key in ap_dict:
ap_dict[key] = ap_dict[key][0]
ap_result_str += '%s: %.4f \n' % (key, ap_dict[key])
return ap_result_str, ap_dict
eval_det_annos = copy.deepcopy(det_annos)
eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.infos]
if kwargs['eval_metric'] == 'kitti':
ap_result_str, ap_dict = kitti_eval(eval_det_annos, eval_gt_annos)
elif kwargs['eval_metric'] == 'waymo':
ap_result_str, ap_dict = waymo_eval(eval_det_annos, eval_gt_annos)
else:
raise NotImplementedError
return ap_result_str, ap_dict
def create_groundtruth_database(self, info_path, save_path, used_classes=None, split='train', sampled_interval=10,
processed_data_tag=None):
database_save_path = save_path / ('pcdet_gt_database_%s_sampled_%d' % (split, sampled_interval))
db_info_save_path = save_path / ('pcdet_waymo_dbinfos_%s_sampled_%d.pkl' % (split, sampled_interval))
database_save_path.mkdir(parents=True, exist_ok=True)
all_db_infos = {}
with open(info_path, 'rb') as f:
infos = pickle.load(f)
for k in range(0, len(infos), sampled_interval):
print('gt_database sample: %d/%d' % (k + 1, len(infos)))
info = infos[k]
pc_info = info['point_cloud']
sequence_name = pc_info['lidar_sequence']
sample_idx = pc_info['sample_idx']
points = self.get_lidar(sequence_name, sample_idx)
annos = info['annos']
names = annos['name']
difficulty = annos['difficulty']
gt_boxes = annos['gt_boxes_lidar']
num_obj = gt_boxes.shape[0]
box_idxs_of_pts = roiaware_pool3d_utils.points_in_boxes_gpu(
torch.from_numpy(points[:, 0:3]).unsqueeze(dim=0).float().cuda(),
torch.from_numpy(gt_boxes[:, 0:7]).unsqueeze(dim=0).float().cuda()
).long().squeeze(dim=0).cpu().numpy()
for i in range(num_obj):
filename = '%s_%04d_%s_%d.bin' % (sequence_name, sample_idx, names[i], i)
filepath = database_save_path / filename
gt_points = points[box_idxs_of_pts == i]
gt_points[:, :3] -= gt_boxes[i, :3]
if (used_classes is None) or names[i] in used_classes:
with open(filepath, 'w') as f:
gt_points.tofile(f)
db_path = str(filepath.relative_to(self.root_path)) # gt_database/xxxxx.bin
db_info = {'name': names[i], 'path': db_path, 'sequence_name': sequence_name,
'sample_idx': sample_idx, 'gt_idx': i, 'box3d_lidar': gt_boxes[i],
'num_points_in_gt': gt_points.shape[0], 'difficulty': difficulty[i]}
if names[i] in all_db_infos:
all_db_infos[names[i]].append(db_info)
else:
all_db_infos[names[i]] = [db_info]
for k, v in all_db_infos.items():
print('Database %s: %d' % (k, len(v)))
with open(db_info_save_path, 'wb') as f:
pickle.dump(all_db_infos, f)
def create_waymo_infos(dataset_cfg, class_names, data_path, save_path,
raw_data_tag='raw_data', processed_data_tag='waymo_processed_data', workers=4):
dataset = WaymoDataset(
dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path,
training=False, logger=common_utils.create_logger()
)
train_split, val_split = 'train', 'val'
train_filename = save_path / ('waymo_infos_%s.pkl' % train_split)
val_filename = save_path / ('waymo_infos_%s.pkl' % val_split)
print('---------------Start to generate data infos---------------')
dataset.set_split(train_split)
waymo_infos_train = dataset.get_infos(
raw_data_path=data_path / raw_data_tag,
save_path=save_path / processed_data_tag, num_workers=workers, has_label=True,
sampled_interval=1
)
with open(train_filename, 'wb') as f:
pickle.dump(waymo_infos_train, f)
print('----------------Waymo info train file is saved to %s----------------' % train_filename)
dataset.set_split(val_split)
waymo_infos_val = dataset.get_infos(
raw_data_path=data_path / raw_data_tag,
save_path=save_path / processed_data_tag, num_workers=workers, has_label=True,
sampled_interval=1
)
with open(val_filename, 'wb') as f:
pickle.dump(waymo_infos_val, f)
print('----------------Waymo info val file is saved to %s----------------' % val_filename)
print('---------------Start create groundtruth database for data augmentation---------------')
dataset.set_split(train_split)
dataset.create_groundtruth_database(
info_path=train_filename, save_path=save_path, split='train', sampled_interval=10,
used_classes=['Vehicle', 'Pedestrian', 'Cyclist']
)
print('---------------Data preparation Done---------------')
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--cfg_file', type=str, default=None, help='specify the config of dataset')
parser.add_argument('--func', type=str, default='create_waymo_infos', help='')
args = parser.parse_args()
if args.func == 'create_waymo_infos':
import yaml
from easydict import EasyDict
dataset_cfg = EasyDict(yaml.load(open(args.cfg_file)))
ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
create_waymo_infos(
dataset_cfg=dataset_cfg,
class_names=['Vehicle', 'Pedestrian', 'Cyclist'],
data_path=ROOT_DIR / 'data' / 'waymo',
save_path=ROOT_DIR / 'data' / 'waymo',
raw_data_tag='raw_data',
processed_data_tag=dataset_cfg.PROCESSED_DATA_TAG
)
pcdet/datasets/waymo/waymo_eval.py 0 → 100644
View file @ a6bb3580
# OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
# Reference https://github.com/open-mmlab/OpenPCDet
# Written by Shaoshuai Shi, Chaoxu Guo
# All Rights Reserved 2019-2020.
import numpy as np
import pickle
import tensorflow as tf
from google.protobuf import text_format
from waymo_open_dataset.metrics.python import detection_metrics
from waymo_open_dataset.protos import metrics_pb2
import argparse
tf.get_logger().setLevel('INFO')
def limit_period(val, offset=0.5, period=np.pi):
return val - np.floor(val / period + offset) * period
class OpenPCDetWaymoDetectionMetricsEstimator(tf.test.TestCase):
WAYMO_CLASSES = ['unknown', 'Vehicle', 'Pedestrian', 'Truck', 'Cyclist']
def generate_waymo_type_results(self, infos, class_names, is_gt=False, fake_gt_infos=True):
def boxes3d_kitti_fakelidar_to_lidar(boxes3d_lidar):
"""
Args:
boxes3d_fakelidar: (N, 7) [x, y, z, w, l, h, r] in old LiDAR coordinates, z is bottom center
Returns:
boxes3d_lidar: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
"""
w, l, h, r = boxes3d_lidar[:, 3:4], boxes3d_lidar[:, 4:5], boxes3d_lidar[:, 5:6], boxes3d_lidar[:, 6:7]
boxes3d_lidar[:, 2] += h[:, 0] / 2
return np.concatenate([boxes3d_lidar[:, 0:3], l, w, h, -(r + np.pi / 2)], axis=-1)
frame_id, boxes3d, obj_type, score, overlap_nlz, difficulty = [], [], [], [], [], []
for frame_index, info in enumerate(infos):
if is_gt:
box_mask = np.array([n in class_names for n in info['name']], dtype=np.bool_)
if 'num_points_in_gt' in info:
zero_difficulty_mask = info['difficulty'] == 0
info['difficulty'][(info['num_points_in_gt'] > 5) & zero_difficulty_mask] = 1
info['difficulty'][(info['num_points_in_gt'] <= 5) & zero_difficulty_mask] = 2
nonzero_mask = info['num_points_in_gt'] > 0
box_mask = box_mask & nonzero_mask
num_boxes = box_mask.sum()
box_name = info['name'][box_mask]
difficulty.append(info['difficulty'][box_mask])
score.append(np.ones(num_boxes))
if fake_gt_infos:
info['gt_boxes_lidar'] = boxes3d_kitti_fakelidar_to_lidar(info['gt_boxes_lidar'])
boxes3d.append(info['gt_boxes_lidar'][box_mask])
else:
num_boxes = len(info['boxes_lidar'])
difficulty.append([0] * num_boxes)
score.append(info['score'])
boxes3d.append(np.array(info['boxes_lidar']))
box_name = info['name']
obj_type += [self.WAYMO_CLASSES.index(name) for i, name in enumerate(box_name)]
frame_id.append(np.array([frame_index] * num_boxes))
overlap_nlz.append(np.zeros(num_boxes)) # set zero currently
frame_id = np.concatenate(frame_id).reshape(-1).astype(np.int64)
boxes3d = np.concatenate(boxes3d, axis=0)
obj_type = np.array(obj_type).reshape(-1)
score = np.concatenate(score).reshape(-1)
overlap_nlz = np.concatenate(overlap_nlz).reshape(-1)
difficulty = np.concatenate(difficulty).reshape(-1).astype(np.int8)
boxes3d[:, -1] = limit_period(boxes3d[:, -1], offset=0.5, period=np.pi * 2)
return frame_id, boxes3d, obj_type, score, overlap_nlz, difficulty
def build_config(self):
config = metrics_pb2.Config()
config_text = """
breakdown_generator_ids: OBJECT_TYPE
difficulties {
levels:1
levels:2
}
matcher_type: TYPE_HUNGARIAN
iou_thresholds: 0.0
iou_thresholds: 0.7
iou_thresholds: 0.5
iou_thresholds: 0.5
iou_thresholds: 0.5
box_type: TYPE_3D
"""
for x in range(0, 100):
config.score_cutoffs.append(x * 0.01)
config.score_cutoffs.append(1.0)
text_format.Merge(config_text, config)
return config
def build_graph(self, graph):
with graph.as_default():
self._pd_frame_id = tf.compat.v1.placeholder(dtype=tf.int64)
self._pd_bbox = tf.compat.v1.placeholder(dtype=tf.float32)
self._pd_type = tf.compat.v1.placeholder(dtype=tf.uint8)
self._pd_score = tf.compat.v1.placeholder(dtype=tf.float32)
self._pd_overlap_nlz = tf.compat.v1.placeholder(dtype=tf.bool)
self._gt_frame_id = tf.compat.v1.placeholder(dtype=tf.int64)
self._gt_bbox = tf.compat.v1.placeholder(dtype=tf.float32)
self._gt_type = tf.compat.v1.placeholder(dtype=tf.uint8)
self._gt_difficulty = tf.compat.v1.placeholder(dtype=tf.uint8)
metrics = detection_metrics.get_detection_metric_ops(
config=self.build_config(),
prediction_frame_id=self._pd_frame_id,
prediction_bbox=self._pd_bbox,
prediction_type=self._pd_type,
prediction_score=self._pd_score,
prediction_overlap_nlz=self._pd_overlap_nlz,
ground_truth_bbox=self._gt_bbox,
ground_truth_type=self._gt_type,
ground_truth_frame_id=self._gt_frame_id,
ground_truth_difficulty=self._gt_difficulty,
)
return metrics
def run_eval_ops(
self,
sess,
graph,
metrics,
prediction_frame_id,
prediction_bbox,
prediction_type,
prediction_score,
prediction_overlap_nlz,
ground_truth_frame_id,
ground_truth_bbox,
ground_truth_type,
ground_truth_difficulty,
):
sess.run(
[tf.group([value[1] for value in metrics.values()])],
feed_dict={
self._pd_bbox: prediction_bbox,
self._pd_frame_id: prediction_frame_id,
self._pd_type: prediction_type,
self._pd_score: prediction_score,
self._pd_overlap_nlz: prediction_overlap_nlz,
self._gt_bbox: ground_truth_bbox,
self._gt_type: ground_truth_type,
self._gt_frame_id: ground_truth_frame_id,
self._gt_difficulty: ground_truth_difficulty,
},
)
def eval_value_ops(self, sess, graph, metrics):
return {item[0]: sess.run([item[1][0]]) for item in metrics.items()}
def mask_by_distance(self, distance_thresh, boxes_3d, *args):
mask = np.linalg.norm(boxes_3d[:, 0:2], axis=1) < distance_thresh + 0.5
boxes_3d = boxes_3d[mask]
ret_ans = [boxes_3d]
for arg in args:
ret_ans.append(arg[mask])
return tuple(ret_ans)
def waymo_evaluation(self, prediction_infos, gt_infos, class_name, distance_thresh=100, fake_gt_infos=True):
print('Start the waymo evaluation...')
assert len(prediction_infos) == len(gt_infos), '%d vs %d' % (prediction_infos.__len__(), gt_infos.__len__())
tf.compat.v1.disable_eager_execution()
pd_frameid, pd_boxes3d, pd_type, pd_score, pd_overlap_nlz, _ = self.generate_waymo_type_results(
prediction_infos, class_name, is_gt=False
)
gt_frameid, gt_boxes3d, gt_type, gt_score, gt_overlap_nlz, gt_difficulty = self.generate_waymo_type_results(
gt_infos, class_name, is_gt=True, fake_gt_infos=fake_gt_infos
)
pd_boxes3d, pd_frameid, pd_type, pd_score, pd_overlap_nlz = self.mask_by_distance(
distance_thresh, pd_boxes3d, pd_frameid, pd_type, pd_score, pd_overlap_nlz
)
gt_boxes3d, gt_frameid, gt_type, gt_score, gt_difficulty = self.mask_by_distance(
distance_thresh, gt_boxes3d, gt_frameid, gt_type, gt_score, gt_difficulty
)
print('Number: (pd, %d) VS. (gt, %d)' % (len(pd_boxes3d), len(gt_boxes3d)))
print('Level 1: %d, Level2: %d)' % ((gt_difficulty == 1).sum(), (gt_difficulty == 2).sum()))
if pd_score.max() > 1:
# assert pd_score.max() <= 1.0, 'Waymo evaluation only supports normalized scores'
pd_score = 1 / (1 + np.exp(-pd_score))
print('Warning: Waymo evaluation only supports normalized scores')
graph = tf.Graph()
metrics = self.build_graph(graph)
with self.test_session(graph=graph) as sess:
sess.run(tf.compat.v1.initializers.local_variables())
self.run_eval_ops(
sess, graph, metrics, pd_frameid, pd_boxes3d, pd_type, pd_score, pd_overlap_nlz,
gt_frameid, gt_boxes3d, gt_type, gt_difficulty,
)
with tf.compat.v1.variable_scope('detection_metrics', reuse=True):
aps = self.eval_value_ops(sess, graph, metrics)
return aps
def main():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--pred_infos', type=str, default=None, help='pickle file')
parser.add_argument('--gt_infos', type=str, default=None, help='pickle file')
parser.add_argument('--class_names', type=str, nargs='+', default=['Vehicle', 'Pedestrian', 'Cyclist'], help='')
parser.add_argument('--sampled_interval', type=int, default=5, help='sampled interval for GT sequences')
args = parser.parse_args()
pred_infos = pickle.load(open(args.pred_infos, 'rb'))
gt_infos = pickle.load(open(args.gt_infos, 'rb'))
print('Start to evaluate the waymo format results...')
eval = OpenPCDetWaymoDetectionMetricsEstimator()
gt_infos_dst = []
for idx in range(0, len(gt_infos), args.sampled_interval):
cur_info = gt_infos[idx]['annos']
cur_info['frame_id'] = gt_infos[idx]['frame_id']
gt_infos_dst.append(cur_info)
waymo_AP = eval.waymo_evaluation(
pred_infos, gt_infos_dst, class_name=args.class_names, distance_thresh=1000, fake_gt_infos=True
)
print(waymo_AP)
if __name__ == '__main__':
main()
pcdet/datasets/waymo/waymo_utils.py 0 → 100644
View file @ a6bb3580
# OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
# Reference https://github.com/open-mmlab/OpenPCDet
# Written by Shaoshuai Shi, Chaoxu Guo
# All Rights Reserved 2019-2020.
import os
import pickle
import numpy as np
from ...utils import common_utils
import tensorflow as tf
from waymo_open_dataset.utils import frame_utils, transform_utils, range_image_utils
from waymo_open_dataset import dataset_pb2
try:
tf.enable_eager_execution()
except:
pass
WAYMO_CLASSES = ['unknown', 'Vehicle', 'Pedestrian', 'Sign', 'Cyclist']
def generate_labels(frame):
obj_name, difficulty, dimensions, locations, heading_angles = [], [], [], [], []
tracking_difficulty, speeds, accelerations, obj_ids = [], [], [], []
laser_labels = frame.laser_labels
for i in range(len(laser_labels)):
box = laser_labels[i].box
class_ind = laser_labels[i].type
loc = [box.center_x, box.center_y, box.center_z]
heading_angles.append(box.heading)
obj_name.append(WAYMO_CLASSES[class_ind])
difficulty.append(laser_labels[i].detection_difficulty_level)
tracking_difficulty.append(laser_labels[i].tracking_difficulty_level)
dimensions.append([box.length, box.width, box.height]) # lwh in unified coordinate of OpenPCDet
locations.append(loc)
obj_ids.append(laser_labels[i].id)
annotations = {}
annotations['name'] = np.array(obj_name)
annotations['difficulty'] = np.array(difficulty)
annotations['dimensions'] = np.array(dimensions)
annotations['location'] = np.array(locations)
annotations['heading_angles'] = np.array(heading_angles)
annotations['obj_ids'] = np.array(obj_ids)
annotations['tracking_difficulty'] = np.array(tracking_difficulty)
annotations = common_utils.drop_info_with_name(annotations, name='unknown')
if annotations['name'].__len__() > 0:
gt_boxes_lidar = np.concatenate([
annotations['location'], annotations['dimensions'], annotations['heading_angles'][..., np.newaxis]],
axis=1
)
else:
gt_boxes_lidar = np.zeros((0, 7))
annotations['gt_boxes_lidar'] = gt_boxes_lidar
return annotations
def convert_range_image_to_point_cloud(frame, range_images, camera_projections, range_image_top_pose, ri_index=0):
"""
Modified from the codes of Waymo Open Dataset.
Convert range images to point cloud.
Args:
frame: open dataset frame
range_images: A dict of {laser_name, [range_image_first_return, range_image_second_return]}.
camera_projections: A dict of {laser_name,
[camera_projection_from_first_return, camera_projection_from_second_return]}.
range_image_top_pose: range image pixel pose for top lidar.
ri_index: 0 for the first return, 1 for the second return.
Returns:
points: {[N, 3]} list of 3d lidar points of length 5 (number of lidars).
cp_points: {[N, 6]} list of camera projections of length 5 (number of lidars).
"""
calibrations = sorted(frame.context.laser_calibrations, key=lambda c: c.name)
points = []
cp_points = []
points_NLZ = []
points_intensity = []
points_elongation = []
frame_pose = tf.convert_to_tensor(np.reshape(np.array(frame.pose.transform), [4, 4]))
# [H, W, 6]
range_image_top_pose_tensor = tf.reshape(
tf.convert_to_tensor(range_image_top_pose.data), range_image_top_pose.shape.dims
)
# [H, W, 3, 3]
range_image_top_pose_tensor_rotation = transform_utils.get_rotation_matrix(
range_image_top_pose_tensor[..., 0], range_image_top_pose_tensor[..., 1],
range_image_top_pose_tensor[..., 2])
range_image_top_pose_tensor_translation = range_image_top_pose_tensor[..., 3:]
range_image_top_pose_tensor = transform_utils.get_transform(
range_image_top_pose_tensor_rotation,
range_image_top_pose_tensor_translation)
for c in calibrations:
range_image = range_images[c.name][ri_index]
if len(c.beam_inclinations) == 0: # pylint: disable=g-explicit-length-test
beam_inclinations = range_image_utils.compute_inclination(
tf.constant([c.beam_inclination_min, c.beam_inclination_max]),
height=range_image.shape.dims[0])
else:
beam_inclinations = tf.constant(c.beam_inclinations)
beam_inclinations = tf.reverse(beam_inclinations, axis=[-1])
extrinsic = np.reshape(np.array(c.extrinsic.transform), [4, 4])
range_image_tensor = tf.reshape(
tf.convert_to_tensor(range_image.data), range_image.shape.dims)
pixel_pose_local = None
frame_pose_local = None
if c.name == dataset_pb2.LaserName.TOP:
pixel_pose_local = range_image_top_pose_tensor
pixel_pose_local = tf.expand_dims(pixel_pose_local, axis=0)
frame_pose_local = tf.expand_dims(frame_pose, axis=0)
range_image_mask = range_image_tensor[..., 0] > 0
range_image_NLZ = range_image_tensor[..., 3]
range_image_intensity = range_image_tensor[..., 1]
range_image_elongation = range_image_tensor[..., 2]
range_image_cartesian = range_image_utils.extract_point_cloud_from_range_image(
tf.expand_dims(range_image_tensor[..., 0], axis=0),
tf.expand_dims(extrinsic, axis=0),
tf.expand_dims(tf.convert_to_tensor(beam_inclinations), axis=0),
pixel_pose=pixel_pose_local,
frame_pose=frame_pose_local)
range_image_cartesian = tf.squeeze(range_image_cartesian, axis=0)
points_tensor = tf.gather_nd(range_image_cartesian,
tf.where(range_image_mask))
points_NLZ_tensor = tf.gather_nd(range_image_NLZ, tf.compat.v1.where(range_image_mask))
points_intensity_tensor = tf.gather_nd(range_image_intensity, tf.compat.v1.where(range_image_mask))
points_elongation_tensor = tf.gather_nd(range_image_elongation, tf.compat.v1.where(range_image_mask))
cp = camera_projections[c.name][0]
cp_tensor = tf.reshape(tf.convert_to_tensor(cp.data), cp.shape.dims)
cp_points_tensor = tf.gather_nd(cp_tensor, tf.where(range_image_mask))
points.append(points_tensor.numpy())
cp_points.append(cp_points_tensor.numpy())
points_NLZ.append(points_NLZ_tensor.numpy())
points_intensity.append(points_intensity_tensor.numpy())
points_elongation.append(points_elongation_tensor.numpy())
return points, cp_points, points_NLZ, points_intensity, points_elongation
def save_lidar_points(frame, cur_save_path):
range_images, camera_projections, range_image_top_pose = \
frame_utils.parse_range_image_and_camera_projection(frame)
points, cp_points, points_in_NLZ_flag, points_intensity, points_elongation = \
convert_range_image_to_point_cloud(frame, range_images, camera_projections, range_image_top_pose)
# 3d points in vehicle frame.
points_all = np.concatenate(points, axis=0)
points_in_NLZ_flag = np.concatenate(points_in_NLZ_flag, axis=0).reshape(-1, 1)
points_intensity = np.concatenate(points_intensity, axis=0).reshape(-1, 1)
points_elongation = np.concatenate(points_elongation, axis=0).reshape(-1, 1)
num_points_of_each_lidar = [point.shape[0] for point in points]
save_points = np.concatenate([
points_all, points_intensity, points_elongation, points_in_NLZ_flag
], axis=-1).astype(np.float32)
np.save(cur_save_path, save_points)
# print('saving to ', cur_save_path)
return num_points_of_each_lidar
def process_single_sequence(sequence_file, save_path, sampled_interval, has_label=True):
sequence_name = os.path.splitext(os.path.basename(sequence_file))[0]
# print('Load record (sampled_interval=%d): %s' % (sampled_interval, sequence_name))
if not sequence_file.exists():
print('NotFoundError: %s' % sequence_file)
return []
dataset = tf.data.TFRecordDataset(str(sequence_file), compression_type='')
cur_save_dir = save_path / sequence_name
cur_save_dir.mkdir(parents=True, exist_ok=True)
pkl_file = cur_save_dir / ('%s.pkl' % sequence_name)
sequence_infos = []
if pkl_file.exists():
sequence_infos = pickle.load(open(pkl_file, 'rb'))
print('Skip sequence since it has been processed before: %s' % pkl_file)
return sequence_infos
for cnt, data in enumerate(dataset):
if cnt % sampled_interval != 0:
continue
# print(sequence_name, cnt)
frame = dataset_pb2.Frame()
frame.ParseFromString(bytearray(data.numpy()))
info = {}
pc_info = {'num_features': 5, 'lidar_sequence': sequence_name, 'sample_idx': cnt}
info['point_cloud'] = pc_info
info['frame_id'] = sequence_name + ('_%03d' % cnt)
image_info = {}
for j in range(5):
width = frame.context.camera_calibrations[j].width
height = frame.context.camera_calibrations[j].height
image_info.update({'image_shape_%d' % j: (height, width)})
info['image'] = image_info
pose = np.array(frame.pose.transform, dtype=np.float32).reshape(4, 4)
info['pose'] = pose
if has_label:
annotations = generate_labels(frame)
info['annos'] = annotations
num_points_of_each_lidar = save_lidar_points(frame, cur_save_dir / ('%04d.npy' % cnt))
info['num_points_of_each_lidar'] = num_points_of_each_lidar
sequence_infos.append(info)
with open(pkl_file, 'wb') as f:
pickle.dump(sequence_infos, f)
print('Infos are saved to (sampled_interval=%d): %s' % (sampled_interval, pkl_file))
return sequence_infos
tools/cfgs/dataset_configs/waymo_dataset.yaml 0 → 100644
View file @ a6bb3580
DATASET: 'WaymoDataset'
DATA_PATH: '../data/waymo'
PROCESSED_DATA_TAG: 'waymo_processed_data'
POINT_CLOUD_RANGE: [-75.2, -75.2, -2, 75.2, 75.2, 4]
DATA_SPLIT: {
'train': train,
'test': val
}
SAMPLED_INTERVAL: {
'train': 5,
'test': 5
}
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
USE_ROAD_PLANE: False
DB_INFO_PATH:
- pcdet_waymo_dbinfos_train_sampled_10.pkl
PREPARE: {
filter_by_min_points: ['Vehicle:5', 'Pedestrian:5', 'Cyclist:5'],
filter_by_difficulty: [-1],
}
SAMPLE_GROUPS: ['Vehicle:15', 'Pedestrian:10', 'Cyclist:10']
NUM_POINT_FEATURES: 5
REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
LIMIT_WHOLE_SCENE: True
- NAME: random_world_flip
ALONG_AXIS_LIST: ['x', 'y']
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [0.95, 1.05]
POINT_FEATURE_ENCODING: {
encoding_type: absolute_coordinates_encoding,
used_feature_list: ['x', 'y', 'z', 'intensity', 'elongation'],
src_feature_list: ['x', 'y', 'z', 'intensity', 'elongation'],
}
DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': True
}
- NAME: transform_points_to_voxels
VOXEL_SIZE: [0.1, 0.1, 0.15]
MAX_POINTS_PER_VOXEL: 5
MAX_NUMBER_OF_VOXELS: {
'train': 80000,
'test': 90000
}
tools/cfgs/waymo_models/PartA2.yaml 0 → 100644
View file @ a6bb3580
CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/waymo_dataset.yaml
MODEL:
NAME: PartA2Net
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: UNetV2
MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 256
BACKBONE_2D:
NAME: BaseBEVBackbone
LAYER_NUMS: [5, 5]
LAYER_STRIDES: [1, 2]
NUM_FILTERS: [128, 256]
UPSAMPLE_STRIDES: [1, 2]
NUM_UPSAMPLE_FILTERS: [256, 256]
DENSE_HEAD:
NAME: AnchorHeadSingle
CLASS_AGNOSTIC: False
USE_DIRECTION_CLASSIFIER: True
DIR_OFFSET: 0.78539
DIR_LIMIT_OFFSET: 0.0
NUM_DIR_BINS: 2
ANCHOR_GENERATOR_CONFIG: [
{
'class_name': 'Vehicle',
'anchor_sizes': [[4.7, 2.1, 1.7]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.55,
'unmatched_threshold': 0.4
},
{
'class_name': 'Pedestrian',
'anchor_sizes': [[0.91, 0.86, 1.73]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.5,
'unmatched_threshold': 0.35
},
{
'class_name': 'Cyclist',
'anchor_sizes': [[1.78, 0.84, 1.78]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.5,
'unmatched_threshold': 0.35
}
]
TARGET_ASSIGNER_CONFIG:
NAME: AxisAlignedTargetAssigner
POS_FRACTION: -1.0
SAMPLE_SIZE: 512
NORM_BY_NUM_EXAMPLES: False
MATCH_HEIGHT: False
BOX_CODER: ResidualCoder
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'loc_weight': 2.0,
'dir_weight': 0.2,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
POINT_HEAD:
NAME: PointIntraPartOffsetHead
CLS_FC: []
PART_FC: []
CLASS_AGNOSTIC: True
TARGET_CONFIG:
GT_EXTRA_WIDTH: [0.2, 0.2, 0.2]
LOSS_CONFIG:
LOSS_REG: smooth-l1
LOSS_WEIGHTS: {
'point_cls_weight': 1.0,
'point_part_weight': 1.0
}
ROI_HEAD:
NAME: PartA2FCHead
CLASS_AGNOSTIC: True
SHARED_FC: [256, 256, 256]
CLS_FC: [256, 256]
REG_FC: [256, 256]
DP_RATIO: 0.3
SEG_MASK_SCORE_THRESH: 0.3
NMS_CONFIG:
TRAIN:
NMS_TYPE: nms_gpu
MULTI_CLASSES_NMS: False
NMS_PRE_MAXSIZE: 9000
NMS_POST_MAXSIZE: 512
NMS_THRESH: 0.8
TEST:
NMS_TYPE: nms_gpu
MULTI_CLASSES_NMS: False
NMS_PRE_MAXSIZE: 1024
NMS_POST_MAXSIZE: 100
NMS_THRESH: 0.7
ROI_AWARE_POOL:
POOL_SIZE: 10
NUM_FEATURES: 128
MAX_POINTS_PER_VOXEL: 128
TARGET_CONFIG:
BOX_CODER: ResidualCoder
ROI_PER_IMAGE: 128
FG_RATIO: 0.5
SAMPLE_ROI_BY_EACH_CLASS: True
CLS_SCORE_TYPE: roi_iou
CLS_FG_THRESH: 0.75
CLS_BG_THRESH: 0.25
CLS_BG_THRESH_LO: 0.1
HARD_BG_RATIO: 0.8
REG_FG_THRESH: 0.65
LOSS_CONFIG:
CLS_LOSS: BinaryCrossEntropy
REG_LOSS: smooth-l1
CORNER_LOSS_REGULARIZATION: True
LOSS_WEIGHTS: {
'rcnn_cls_weight': 1.0,
'rcnn_reg_weight': 1.0,
'rcnn_corner_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: waymo
NMS_CONFIG:
MULTI_CLASSES_NMS: False
NMS_TYPE: nms_gpu
NMS_THRESH: 0.1
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 3
NUM_EPOCHS: 30
OPTIMIZER: adam_onecycle
LR: 0.01
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
MOMS: [0.95, 0.85]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [35, 45]
LR_DECAY: 0.1
LR_CLIP: 0.0000001
LR_WARMUP: False
WARMUP_EPOCH: 1
GRAD_NORM_CLIP: 10
\ No newline at end of file
tools/cfgs/waymo_models/pv_rcnn.yaml 0 → 100644
View file @ a6bb3580
CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/waymo_dataset.yaml
MODEL:
NAME: PVRCNN
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: VoxelBackBone8x
MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 256
BACKBONE_2D:
NAME: BaseBEVBackbone
LAYER_NUMS: [5, 5]
LAYER_STRIDES: [1, 2]
NUM_FILTERS: [128, 256]
UPSAMPLE_STRIDES: [1, 2]
NUM_UPSAMPLE_FILTERS: [256, 256]
DENSE_HEAD:
NAME: AnchorHeadSingle
CLASS_AGNOSTIC: False
USE_DIRECTION_CLASSIFIER: True
DIR_OFFSET: 0.78539
DIR_LIMIT_OFFSET: 0.0
NUM_DIR_BINS: 2
ANCHOR_GENERATOR_CONFIG: [
{
'class_name': 'Vehicle',
'anchor_sizes': [[4.7, 2.1, 1.7]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.55,
'unmatched_threshold': 0.4
},
{
'class_name': 'Pedestrian',
'anchor_sizes': [[0.91, 0.86, 1.73]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.5,
'unmatched_threshold': 0.35
},
{
'class_name': 'Cyclist',
'anchor_sizes': [[1.78, 0.84, 1.78]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.5,
'unmatched_threshold': 0.35
}
]
TARGET_ASSIGNER_CONFIG:
NAME: AxisAlignedTargetAssigner
POS_FRACTION: -1.0
SAMPLE_SIZE: 512
NORM_BY_NUM_EXAMPLES: False
MATCH_HEIGHT: False
BOX_CODER: ResidualCoder
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'loc_weight': 2.0,
'dir_weight': 0.2,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
PFE:
NAME: VoxelSetAbstraction
POINT_SOURCE: raw_points
NUM_KEYPOINTS: 4096
NUM_OUTPUT_FEATURES: 128
SAMPLE_METHOD: FPS
FEATURES_SOURCE: ['bev', 'x_conv3', 'x_conv4', 'raw_points']
SA_LAYER:
raw_points:
MLPS: [[16, 16], [16, 16]]
POOL_RADIUS: [0.4, 0.8]
NSAMPLE: [16, 16]
x_conv1:
DOWNSAMPLE_FACTOR: 1
MLPS: [[16, 16], [16, 16]]
POOL_RADIUS: [0.4, 0.8]
NSAMPLE: [16, 16]
x_conv2:
DOWNSAMPLE_FACTOR: 2
MLPS: [[32, 32], [32, 32]]
POOL_RADIUS: [0.8, 1.2]
NSAMPLE: [16, 32]
x_conv3:
DOWNSAMPLE_FACTOR: 4
MLPS: [[64, 64], [64, 64]]
POOL_RADIUS: [1.2, 2.4]
NSAMPLE: [16, 32]
x_conv4:
DOWNSAMPLE_FACTOR: 8
MLPS: [[64, 64], [64, 64]]
POOL_RADIUS: [2.4, 4.8]
NSAMPLE: [16, 32]
POINT_HEAD:
NAME: PointHeadSimple
CLS_FC: [256, 256]
CLASS_AGNOSTIC: True
USE_POINT_FEATURES_BEFORE_FUSION: True
TARGET_CONFIG:
GT_EXTRA_WIDTH: [0.2, 0.2, 0.2]
LOSS_CONFIG:
LOSS_REG: smooth-l1
LOSS_WEIGHTS: {
'point_cls_weight': 1.0,
}
ROI_HEAD:
NAME: PVRCNNHead
CLASS_AGNOSTIC: True
SHARED_FC: [256, 256]
CLS_FC: [256, 256]
REG_FC: [256, 256]
DP_RATIO: 0.3
NMS_CONFIG:
TRAIN:
NMS_TYPE: nms_gpu
MULTI_CLASSES_NMS: False
NMS_PRE_MAXSIZE: 9000
NMS_POST_MAXSIZE: 512
NMS_THRESH: 0.8
TEST:
NMS_TYPE: nms_gpu
MULTI_CLASSES_NMS: False
NMS_PRE_MAXSIZE: 1024
NMS_POST_MAXSIZE: 100
NMS_THRESH: 0.7
ROI_GRID_POOL:
GRID_SIZE: 6
MLPS: [[64, 64], [64, 64]]
POOL_RADIUS: [0.8, 1.6]
NSAMPLE: [16, 16]
POOL_METHOD: max_pool
TARGET_CONFIG:
BOX_CODER: ResidualCoder
ROI_PER_IMAGE: 128
FG_RATIO: 0.5
SAMPLE_ROI_BY_EACH_CLASS: True
CLS_SCORE_TYPE: roi_iou
CLS_FG_THRESH: 0.75
CLS_BG_THRESH: 0.25
CLS_BG_THRESH_LO: 0.1
HARD_BG_RATIO: 0.8
REG_FG_THRESH: 0.55
LOSS_CONFIG:
CLS_LOSS: BinaryCrossEntropy
REG_LOSS: smooth-l1
CORNER_LOSS_REGULARIZATION: True
LOSS_WEIGHTS: {
'rcnn_cls_weight': 1.0,
'rcnn_reg_weight': 1.0,
'rcnn_corner_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: waymo
NMS_CONFIG:
MULTI_CLASSES_NMS: False
NMS_TYPE: nms_gpu
NMS_THRESH: 0.1
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 2
NUM_EPOCHS: 30
OPTIMIZER: adam_onecycle
LR: 0.01
WEIGHT_DECAY: 0.001
MOMENTUM: 0.9
MOMS: [0.95, 0.85]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [35, 45]
LR_DECAY: 0.1
LR_CLIP: 0.0000001
LR_WARMUP: False
WARMUP_EPOCH: 1
GRAD_NORM_CLIP: 10
\ No newline at end of file
tools/cfgs/waymo_models/second.yaml 0 → 100644
View file @ a6bb3580
CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/waymo_dataset.yaml
MODEL:
NAME: SECONDNet
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: VoxelBackBone8x
MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 256
BACKBONE_2D:
NAME: BaseBEVBackbone
LAYER_NUMS: [5, 5]
LAYER_STRIDES: [1, 2]
NUM_FILTERS: [128, 256]
UPSAMPLE_STRIDES: [1, 2]
NUM_UPSAMPLE_FILTERS: [256, 256]
DENSE_HEAD:
NAME: AnchorHeadSingle
CLASS_AGNOSTIC: False
USE_DIRECTION_CLASSIFIER: True
DIR_OFFSET: 0.78539
DIR_LIMIT_OFFSET: 0.0
NUM_DIR_BINS: 2
ANCHOR_GENERATOR_CONFIG: [
{
'class_name': 'Vehicle',
'anchor_sizes': [[4.7, 2.1, 1.7]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.55,
'unmatched_threshold': 0.4
},
{
'class_name': 'Pedestrian',
'anchor_sizes': [[0.91, 0.86, 1.73]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.5,
'unmatched_threshold': 0.35
},
{
'class_name': 'Cyclist',
'anchor_sizes': [[1.78, 0.84, 1.78]],
'anchor_rotations': [0, 1.57],
'anchor_bottom_heights': [0],
'align_center': False,
'feature_map_stride': 8,
'matched_threshold': 0.5,
'unmatched_threshold': 0.35
}
]
TARGET_ASSIGNER_CONFIG:
NAME: AxisAlignedTargetAssigner
POS_FRACTION: -1.0
SAMPLE_SIZE: 512
NORM_BY_NUM_EXAMPLES: False
MATCH_HEIGHT: False
BOX_CODER: ResidualCoder
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'loc_weight': 2.0,
'dir_weight': 0.2,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: waymo
NMS_CONFIG:
MULTI_CLASSES_NMS: False
NMS_TYPE: nms_gpu
NMS_THRESH: 0.01
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 4
NUM_EPOCHS: 30
OPTIMIZER: adam_onecycle
LR: 0.003
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
MOMS: [0.95, 0.85]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [35, 45]
LR_DECAY: 0.1
LR_CLIP: 0.0000001
LR_WARMUP: False
WARMUP_EPOCH: 1
GRAD_NORM_CLIP: 10
\ No newline at end of file
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