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Commit 1d005956 authored by zhangwenwei's avatar zhangwenwei
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Update model zoo links and readme

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.dev_scripts/gather_models.py 0 → 100644
View file @ 1d005956
import argparse
import glob
import json
import os.path as osp
import shutil
import subprocess
import mmcv
import torch
# build schedule look-up table to automatically find the final model
SCHEDULES_LUT = {
'_1x_': 12,
'_2x_': 24,
'_20e_': 20,
'_3x_': 36,
'_4x_': 48,
'_24e_': 24,
'_6x_': 73
}
# TODO: add support for lyft dataset
RESULTS_LUT = {
'coco': ['bbox_mAP', 'segm_mAP'],
'nus': ['pts_bbox_NuScenes/NDS', 'NDS'],
'kitti-3d-3class': [
'KITTI/Overall_3D_moderate',
'Overall_3D_moderate',
],
'kitti-3d-car': ['KITTI/Car_3D_moderate_strict', 'Car_3D_moderate_strict'],
'lyft': ['score'],
'scannet': ['mAR_0.50'],
'sunrgbd': ['mAR_0.50']
}
def get_model_dataset(log_json_path):
for key in RESULTS_LUT:
if log_json_path.find(key) != -1:
return key
def process_checkpoint(in_file, out_file):
checkpoint = torch.load(in_file, map_location='cpu')
# remove optimizer for smaller file size
if 'optimizer' in checkpoint:
del checkpoint['optimizer']
# if it is necessary to remove some sensitive data in checkpoint['meta'],
# add the code here.
torch.save(checkpoint, out_file)
sha = subprocess.check_output(['sha256sum', out_file]).decode()
final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
subprocess.Popen(['mv', out_file, final_file])
return final_file
def get_final_epoch(config):
if config.find('grid_rcnn') != -1 and config.find('2x') != -1:
# grid_rcnn 2x trains 25 epochs
return 25
for schedule_name, epoch_num in SCHEDULES_LUT.items():
if config.find(schedule_name) != -1:
return epoch_num
def get_best_results(log_json_path):
dataset = get_model_dataset(log_json_path)
max_dict = dict()
max_memory = 0
with open(log_json_path, 'r') as f:
for line in f.readlines():
log_line = json.loads(line)
if 'mode' not in log_line.keys():
continue
# record memory and find best results & epochs
if log_line['mode'] == 'train' \
and max_memory <= log_line['memory']:
max_memory = log_line['memory']
elif log_line['mode'] == 'val':
result_dict = {
key: log_line[key]
for key in RESULTS_LUT[dataset] if key in log_line
}
if len(max_dict) == 0:
max_dict = result_dict
max_dict['epoch'] = log_line['epoch']
elif all(
[max_dict[key] <= result_dict[key]
for key in result_dict]):
max_dict.update(result_dict)
max_dict['epoch'] = log_line['epoch']
max_dict['memory'] = max_memory
return max_dict
def parse_args():
parser = argparse.ArgumentParser(description='Gather benchmarked models')
parser.add_argument(
'root',
type=str,
help='root path of benchmarked models to be gathered')
parser.add_argument(
'out', type=str, help='output path of gathered models to be stored')
args = parser.parse_args()
return args
def main():
args = parse_args()
models_root = args.root
models_out = args.out
mmcv.mkdir_or_exist(models_out)
# find all models in the root directory to be gathered
raw_configs = list(mmcv.scandir('./configs', '.py', recursive=True))
# filter configs that is not trained in the experiments dir
used_configs = []
for raw_config in raw_configs:
if osp.exists(osp.join(models_root, raw_config)):
used_configs.append(raw_config)
print(f'Find {len(used_configs)} models to be gathered')
# find final_ckpt and log file for trained each config
# and parse the best performance
model_infos = []
for used_config in used_configs:
exp_dir = osp.join(models_root, used_config)
# get logs
log_json_path = glob.glob(osp.join(exp_dir, '*.log.json'))[0]
log_txt_path = glob.glob(osp.join(exp_dir, '*.log'))[0]
model_performance = get_best_results(log_json_path)
final_epoch = model_performance['epoch']
final_model = 'epoch_{}.pth'.format(final_epoch)
model_path = osp.join(exp_dir, final_model)
# skip if the model is still training
if not osp.exists(model_path):
print(f'Expected {model_path} does not exist!')
continue
if model_performance is None:
print(f'Obtained no performance for model {used_config}')
continue
model_time = osp.split(log_txt_path)[-1].split('.')[0]
model_infos.append(
dict(
config=used_config,
results=model_performance,
epochs=final_epoch,
model_time=model_time,
log_json_path=osp.split(log_json_path)[-1]))
# publish model for each checkpoint
publish_model_infos = []
for model in model_infos:
model_publish_dir = osp.join(models_out, model['config'].rstrip('.py'))
mmcv.mkdir_or_exist(model_publish_dir)
model_name = model_publish_dir + '_' + model['model_time']
publish_model_path = osp.join(model_publish_dir, model_name)
trained_model_path = osp.join(models_root, model['config'],
'epoch_{}.pth'.format(model['epochs']))
# convert model
final_model_path = process_checkpoint(trained_model_path,
publish_model_path)
# copy log
shutil.copy(
osp.join(models_root, model['config'], model['log_json_path']),
osp.join(model_publish_dir, f'{model_name}.log.json'))
shutil.copy(
osp.join(models_root, model['config'],
model['log_json_path'].rstrip('.json')),
osp.join(model_publish_dir, f'{model_name}.log'))
# copy config to guarantee reproducibility
config_path = model['config']
config_path = osp.join(
'configs',
config_path) if 'configs' not in config_path else config_path
target_cconfig_path = osp.split(config_path)[-1]
shutil.copy(config_path,
osp.join(model_publish_dir, target_cconfig_path))
model['model_path'] = final_model_path
publish_model_infos.append(model)
models = dict(models=publish_model_infos)
print(f'Totally gathered {len(publish_model_infos)} models')
mmcv.dump(models, osp.join(models_out, 'model_info.json'))
if __name__ == '__main__':
main()
README.md
View file @ 1d005956
...@@ -49,17 +49,17 @@ Results and models are available in the [model zoo](docs/model_zoo.md). ...@@ -49,17 +49,17 @@ Results and models are available in the [model zoo](docs/model_zoo.md).
| | ResNet | ResNeXt | SENet |PointNet++ | HRNet | RegNetX | Res2Net | | | ResNet | ResNeXt | SENet |PointNet++ | HRNet | RegNetX | Res2Net |
|--------------------|:--------:|:--------:|:--------:|:---------:|:-----:|:--------:|:-----:| |--------------------|:--------:|:--------:|:--------:|:---------:|:-----:|:--------:|:-----:|
| SECOND | ☐ | ☐ | ☐ | ✗ | ✓ | ✓ | ☐ | | SECOND | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| PointPillars | ☐ | ☐ | ☐ | ✗ | ✓ | ✓ | ☐ | | PointPillars | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ✓ | ✓ | ☐ | | FreeAnchor | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | | VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Part-A2 | ☐ | ☐ | ☐ | ✗ | ✓ | ✓ | ☐ | | Part-A2 | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| MVXNet | ☐ | ☐ | ☐ | ✗ | ✓ | ✓ | ☐ | | MVXNet | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
Other features Other features
- [x] [Dynamic Voxelization](configs/carafe/README.md) - [x] [Dynamic Voxelization](configs/carafe/README.md)
**Notice**: All the models or modules supported in [MMDetection's model zoo](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase. All the about **300 models, 40+ papers**, and modules supported in [MMDetection's model zoo](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
## Installation ## Installation
......
configs/dynamic_voxelization/README.md
View file @ 1d005956
...@@ -21,6 +21,6 @@ We implement Dynamic Voxelization proposed in and provide its results and model ...@@ -21,6 +21,6 @@ We implement Dynamic Voxelization proposed in and provide its results and model
| Model |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download | | Model |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
| :---------: | :-----: |:-----: | :------: | :------------: | :----: | :------: | | :---------: | :-----: |:-----: | :------: | :------------: | :----: | :------: |
|[SECOND](./dv_second_secfpn_6x8_80e_kitti-3d-car.py)|Car |cyclic 80e|5.5||78.83|| |[SECOND](./dv_second_secfpn_6x8_80e_kitti-3d-car.py)|Car |cyclic 80e|5.5||78.83|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/)|
|[SECOND](./dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py)| 3 Class|cosine 80e|5.5||65.10|| |[SECOND](./dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py)| 3 Class|cosine 80e|5.5||65.10|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/)|
|[PointPillars](./dv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py)| Car|cyclic 80e|4.7||77.76|| |[PointPillars](./dv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py)| Car|cyclic 80e|4.7||77.76|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/)|
configs/free_anchor/README.md
View file @ 1d005956
...@@ -80,9 +80,13 @@ train_cfg = dict( ...@@ -80,9 +80,13 @@ train_cfg = dict(
| Backbone |FreeAnchor|Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download | | Backbone |FreeAnchor|Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download |
| :---------: |:-----: |:-----: | :------: | :------------: | :----: |:----: | :------: | | :---------: |:-----: |:-----: | :------: | :------------: | :----: |:----: | :------: |
|[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|✗|2x|17.1||40.0|53.3|| |[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|✗|2x|17.1||40.0|53.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405.log.json)|
|[FPN](./hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|||43.7|55.1|| |[FPN](./hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|16.2||43.7|55.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20200628_210537-09d359fc.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20200628_210537.log.json)|
|[RegNetX-400MF-FPN](../regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py)|✗|2x|17.3||44.8|56.4|| |[RegNetX-400MF-FPN](../regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py)|✗|2x|17.3||44.8|56.4|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239-c694dce7.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239.log.json)|
|[RegNetX-400MF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|||||| |[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|17.7||47.9|58.6|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20200629_050311-a334765d.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20200629_050311.log.json)|
|[RegNetX-1.6GF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|||||| |[RegNetX-1.6GF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|24.3||51.2|60.8|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20200629_105446-6ffa59cb.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20200629_105446.log.json)|
|[RegNetX-3.2GF-FPN](./hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|||||| |[RegNetX-1.6GF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py)*|✓|3x|24.3|53.0|62.2|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20200701_201531-036f7de3.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20200701_201531.log.json)|
|[RegNetX-3.2GF-FPN](./hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py)|✓|2x|29.5||52.2|62.0|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20200629_055854-658125b0.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d_20200629_055854.log.json)|
|[RegNetX-3.2GF-FPN](./hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py)*|✓|3x|29.5||55.09|63.5|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20200629_181452-297fdc66.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d_20200629_181452.log.json)|
**Note**: Models noted by `*` means it is trained using stronger augmentation with vertical flip under bird-eye-view, global translation, and larger range of global rotation.
configs/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py 0 → 100644
View file @ 1d005956
_base_ = './hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py'
model = dict(
pretrained=dict(pts='open-mmlab://regnetx_1.6gf'),
pts_backbone=dict(
_delete_=True,
type='NoStemRegNet',
arch='regnetx_1.6gf',
out_indices=(1, 2, 3),
frozen_stages=-1,
strides=(1, 2, 2, 2),
base_channels=64,
stem_channels=64,
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
norm_eval=False,
style='pytorch'),
pts_neck=dict(in_channels=[168, 408, 912]))
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-50, -50, -5, 50, 50, 3]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
# file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
file_client_args = dict(
backend='petrel',
path_mapping=dict({
'./data/nuscenes/': 's3://nuscenes/nuscenes/',
'data/nuscenes/': 's3://nuscenes/nuscenes/'
}))
train_pipeline = [
dict(
type='LoadPointsFromFile',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.7854, 0.7854],
scale_ratio_range=[0.95, 1.05],
translation_std=[0.2, 0.2, 0.2]),
dict(
type='RandomFlip3D',
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
data = dict(train=dict(pipeline=train_pipeline))
lr_config = dict(step=[28, 34])
evaluation = dict(interval=36)
total_epochs = 36
configs/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py 0 → 100644
View file @ 1d005956
_base_ = './hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py'
model = dict(
pretrained=dict(pts='open-mmlab://regnetx_3.2gf'),
pts_backbone=dict(
_delete_=True,
type='NoStemRegNet',
arch='regnetx_3.2gf',
out_indices=(1, 2, 3),
frozen_stages=-1,
strides=(1, 2, 2, 2),
base_channels=64,
stem_channels=64,
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
norm_eval=False,
style='pytorch'),
pts_neck=dict(in_channels=[192, 432, 1008]))
configs/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py 0 → 100644
View file @ 1d005956
_base_ = './hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py'
model = dict(
pretrained=dict(pts='open-mmlab://regnetx_3.2gf'),
pts_backbone=dict(
_delete_=True,
type='NoStemRegNet',
arch='regnetx_3.2gf',
out_indices=(1, 2, 3),
frozen_stages=-1,
strides=(1, 2, 2, 2),
base_channels=64,
stem_channels=64,
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
norm_eval=False,
style='pytorch'),
pts_neck=dict(in_channels=[192, 432, 1008]))
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-50, -50, -5, 50, 50, 3]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
# file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
file_client_args = dict(
backend='petrel',
path_mapping=dict({
'./data/nuscenes/': 's3://nuscenes/nuscenes/',
'data/nuscenes/': 's3://nuscenes/nuscenes/'
}))
train_pipeline = [
dict(
type='LoadPointsFromFile',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.7854, 0.7854],
scale_ratio_range=[0.9, 1.1],
translation_std=[0.2, 0.2, 0.2]),
dict(
type='RandomFlip3D',
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
data = dict(train=dict(pipeline=train_pipeline))
lr_config = dict(step=[28, 34])
evaluation = dict(interval=36)
total_epochs = 36
configs/free_anchor/hv_pointpillars_regnet-400mf_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py 0 → 100644
View file @ 1d005956
_base_ = './hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py'
model = dict(
pretrained=dict(pts='open-mmlab://regnetx_400mf'),
pts_backbone=dict(
_delete_=True,
type='NoStemRegNet',
arch='regnetx_400mf',
out_indices=(1, 2, 3),
frozen_stages=-1,
strides=(1, 2, 2, 2),
base_channels=64,
stem_channels=64,
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
norm_eval=False,
style='pytorch'),
pts_neck=dict(in_channels=[64, 160, 384]))
configs/mvxnet/README.md
View file @ 1d005956
...@@ -21,4 +21,4 @@ We implement MVX-Net and provide its results and models on KITTI dataset. ...@@ -21,4 +21,4 @@ We implement MVX-Net and provide its results and models on KITTI dataset.
| Backbone |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download | | Backbone |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
| [SECFPN](./dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py)|3 Class|cosine 80e|6.7||63.0|| | [SECFPN](./dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py)|3 Class|cosine 80e|6.7||63.0|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class_20200621_003904-10140f2d.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class_20200621_003904.log.json)|
configs/parta2/README.md
View file @ 1d005956
...@@ -20,5 +20,5 @@ We implement Part-A^2 and provide its results and checkpoints on KITTI dataset. ...@@ -20,5 +20,5 @@ We implement Part-A^2 and provide its results and checkpoints on KITTI dataset.
| Backbone |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download | | Backbone |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
| :---------: | :-----: |:-----: | :------: | :------------: | :----: |:----: | :------: | | :---------: | :-----: |:-----: | :------: | :------------: | :----: |:----: | :------: |
| [SECFPN](./hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py) |3 Class|cyclic 80e|4.1||67.9|| | [SECFPN](./hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py) |3 Class|cyclic 80e|4.1||67.9|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20200620_230724-a2672098.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20200620_230724.log.json)|
| [SECFPN](./hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car.py) |Car |cyclic 80e|4.0||79.16|| | [SECFPN](./hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car.py) |Car |cyclic 80e|4.0||79.16|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20200620_230755-f2a38b9a.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20200620_230755.log.json)|
configs/pointpillars/README.md
View file @ 1d005956
...@@ -21,12 +21,12 @@ We implement PointPillars and provide the results and checkpoints on KITTI and n ...@@ -21,12 +21,12 @@ We implement PointPillars and provide the results and checkpoints on KITTI and n
| Backbone|Class | Lr schd | Mem (GB) | Inf time (fps) | AP |Download | | Backbone|Class | Lr schd | Mem (GB) | Inf time (fps) | AP |Download |
| :---------: | :-----: |:-----: | :------: | :------------: | :----: | :------: | | :---------: | :-----: |:-----: | :------: | :------------: | :----: | :------: |
| [SECFPN](./hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py)|Car|cyclic 160e|5.4||77.1|| | [SECFPN](./hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py)|Car|cyclic 160e|5.4||77.1|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230614-77663cd6.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230614.log.json)|
| [SECFPN](./hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py)|3 Class|cyclic 160e|5.5||59.5| | [SECFPN](./hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py)|3 Class|cyclic 160e|5.5||59.5|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class_20200620_230421-aa0f3adb.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class_20200620_230421.log.json)|
### nuScenes ### nuScenes
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
|[SECFPN](./hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||35.17|49.7|| |[SECFPN](./hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||35.17|49.7|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725-0817d270.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725.log.json)|
|[FPN](./hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||40.0|53.3|| |[FPN](./hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||40.0|53.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405.log.json)|
configs/regnet/README.md
View file @ 1d005956
...@@ -51,7 +51,8 @@ For other pre-trained models or self-implemented regnet models, the users are re ...@@ -51,7 +51,8 @@ For other pre-trained models or self-implemented regnet models, the users are re
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
|[SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||35.17|49.7|| |[SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||35.17|49.7|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725-0817d270.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725.log.json)|
|[RegNetX-400MF-SECFPN](./hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d.py)| 2x |16.4||41.2|55.2|| |[RegNetX-400MF-SECFPN](./hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d.py)| 2x |16.4||41.2|55.2|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230334-53044f32.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230334.log.json)|
|[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.1||40.0|53.3|| |[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.1||40.0|53.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405.log.json)|
|[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.3||44.8|56.4|| |[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.3||44.8|56.4|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239-c694dce7.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239.log.json)|
|[RegNetX-1.6gF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|24.0||48.2|59.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d_20200629_050311-dcd4e090.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d_20200629_050311.log.json)|
configs/second/README.md
View file @ 1d005956
...@@ -18,5 +18,5 @@ We implement SECOND and provide the results and checkpoints on KITTI dataset. ...@@ -18,5 +18,5 @@ We implement SECOND and provide the results and checkpoints on KITTI dataset.
### KITTI ### KITTI
| Backbone |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP |Download | | Backbone |Class| Lr schd | Mem (GB) | Inf time (fps) | mAP |Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
| [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-car.py)| Car |cyclic 80e|5.4||79.07| | [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-car.py)| Car |cyclic 80e|5.4||79.07|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238.log.json)|
| [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-3class.py)| 3 Class |cyclic 80e|5.4||64.41| | [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-3class.py)| 3 Class |cyclic 80e|5.4||64.41|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20200620_230238-9208083a.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20200620_230238-9208083a.pth)|
configs/votenet/README.md
View file @ 1d005956
...@@ -16,9 +16,9 @@ We implement VoteNet and provide the result and checkpoints on ScanNet and SUNRG ...@@ -16,9 +16,9 @@ We implement VoteNet and provide the result and checkpoints on ScanNet and SUNRG
### ScanNet ### ScanNet
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
| [PointNet++](./votenet_8x8_scannet-3d-18class.py) | 3x |4.1||62.90|39.91|| | [PointNet++](./votenet_8x8_scannet-3d-18class.py) | 3x |4.1||62.90|39.91|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20200620_230238-4483c0c0.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20200620_230238.log.json)|
### SUNRGBD ### SUNRGBD
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download | | Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: | | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
| [PointNet++](./) | 3x |8.1||59.07|35.77|| | [PointNet++](./votenet_16x8_sunrgbd-3d-10class.py) | 3x |8.1||59.07|35.77|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_8x8_scannet-3d-18class/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_8x8_scannet-3d-18class/votenet_8x8_scannet-3d-18class_20200620_230238.log.json)|
mmdet3d/utils/__init__.py
View file @ 1d005956
from mmcv.utils import Registry, build_from_cfg, print_log from mmcv.utils import Registry, build_from_cfg, print_log
from mmdet.utils import get_model_complexity_info, get_root_logger from mmdet import get_root_logger
from .collect_env import collect_env from .collect_env import collect_env
__all__ = [ __all__ = [
'Registry', 'build_from_cfg', 'get_model_complexity_info', 'Registry', 'build_from_cfg', 'get_root_logger', 'collect_env', 'print_log'
'get_root_logger', 'collect_env', 'print_log'
] ]
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