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Commit 7aa442d5 authored by raojy's avatar raojy
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raw_mmdetection

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mmdetection3d/configs/_base_/datasets/kitti-mono3d.py 0 → 100644
View file @ 7aa442d5
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
input_modality = dict(use_lidar=False, use_camera=True)
metainfo = dict(classes=class_names)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/kitti/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
dict(type='Resize', scale=(1242, 375), keep_ratio=True),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='Pack3DDetInputs',
keys=[
'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
'gt_labels_3d', 'centers_2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(type='Resize', scale=(1242, 375), keep_ratio=True),
dict(type='Pack3DDetInputs', keys=['img'])
]
eval_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(type='Pack3DDetInputs', keys=['img'])
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='kitti_infos_train.pkl',
data_prefix=dict(img='training/image_2'),
pipeline=train_pipeline,
modality=input_modality,
load_type='fov_image_based',
test_mode=False,
metainfo=metainfo,
# we use box_type_3d='Camera' in monocular 3d
# detection task
box_type_3d='Camera',
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(img='training/image_2'),
ann_file='kitti_infos_val.pkl',
pipeline=test_pipeline,
modality=input_modality,
load_type='fov_image_based',
metainfo=metainfo,
test_mode=True,
box_type_3d='Camera',
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='KittiMetric',
ann_file=data_root + 'kitti_infos_val.pkl',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/lyft-3d-range100.py 0 → 100644
View file @ 7aa442d5
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-100, -100, -5, 100, 100, 3]
# For Lyft we usually do 9-class detection
class_names = [
'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
'bicycle', 'pedestrian', 'animal'
]
dataset_type = 'LyftDataset'
data_root = 'data/lyft/'
data_prefix = dict(pts='v1.01-train/lidar', img='', sweeps='v1.01-train/lidar')
# Input modality for Lyft dataset, this is consistent with the submission
# format which requires the information in input_modality.
input_modality = dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/lyft/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
backend_args=backend_args),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='lyft_infos_train.pkl',
pipeline=train_pipeline,
metainfo=dict(classes=class_names),
modality=input_modality,
data_prefix=data_prefix,
test_mode=False,
box_type_3d='LiDAR',
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='lyft_infos_val.pkl',
pipeline=test_pipeline,
metainfo=dict(classes=class_names),
modality=input_modality,
test_mode=True,
data_prefix=data_prefix,
box_type_3d='LiDAR',
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='LyftMetric',
data_root=data_root,
ann_file='lyft_infos_val.pkl',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/lyft-3d.py 0 → 100644
View file @ 7aa442d5
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-80, -80, -5, 80, 80, 3]
# For Lyft we usually do 9-class detection
class_names = [
'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
'bicycle', 'pedestrian', 'animal'
]
dataset_type = 'LyftDataset'
data_root = 'data/lyft/'
# Input modality for Lyft dataset, this is consistent with the submission
# format which requires the information in input_modality.
input_modality = dict(use_lidar=True, use_camera=False)
data_prefix = dict(pts='v1.01-train/lidar', img='', sweeps='v1.01-train/lidar')
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/lyft/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range)
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
backend_args=backend_args),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='lyft_infos_train.pkl',
pipeline=train_pipeline,
metainfo=dict(classes=class_names),
modality=input_modality,
data_prefix=data_prefix,
test_mode=False,
box_type_3d='LiDAR',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='lyft_infos_val.pkl',
pipeline=test_pipeline,
metainfo=dict(classes=class_names),
modality=input_modality,
data_prefix=data_prefix,
test_mode=True,
box_type_3d='LiDAR',
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='lyft_infos_val.pkl',
pipeline=test_pipeline,
metainfo=dict(classes=class_names),
modality=input_modality,
test_mode=True,
data_prefix=data_prefix,
box_type_3d='LiDAR',
backend_args=backend_args))
val_evaluator = dict(
type='LyftMetric',
data_root=data_root,
ann_file='lyft_infos_val.pkl',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/nuim-instance.py 0 → 100644
View file @ 7aa442d5
dataset_type = 'CocoDataset'
data_root = 'data/nuimages/'
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/nuimages/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1280, 720), (1920, 1080)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PackDetInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(
type='MultiScaleFlipAug',
img_scale=(1600, 900),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
]),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor')),
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/nuimages_v1.0-train.json',
img_prefix=data_root,
classes=class_names,
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/nuimages_v1.0-val.json',
img_prefix=data_root,
classes=class_names,
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/nuimages_v1.0-val.json',
img_prefix=data_root,
classes=class_names,
pipeline=test_pipeline))
evaluation = dict(metric=['bbox', 'segm'])
mmdetection3d/configs/_base_/datasets/nus-3d.py 0 → 100644
View file @ 7aa442d5
# 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]
# Using calibration info convert the Lidar-coordinate point cloud range to the
# ego-coordinate point cloud range could bring a little promotion in nuScenes.
# point_cloud_range = [-50, -50.8, -5, 50, 49.2, 3]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
metainfo = dict(classes=class_names)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
# Input modality for nuScenes dataset, this is consistent with the submission
# format which requires the information in input_modality.
input_modality = dict(use_lidar=True, use_camera=False)
data_prefix = dict(pts='samples/LIDAR_TOP', img='', sweeps='sweeps/LIDAR_TOP')
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/nuscenes/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=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='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
test_mode=True,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range)
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
test_mode=True,
backend_args=backend_args),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=4,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='nuscenes_infos_train.pkl',
pipeline=train_pipeline,
metainfo=metainfo,
modality=input_modality,
test_mode=False,
data_prefix=data_prefix,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='nuscenes_infos_val.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
modality=input_modality,
data_prefix=data_prefix,
test_mode=True,
box_type_3d='LiDAR',
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='nuscenes_infos_val.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
modality=input_modality,
test_mode=True,
data_prefix=data_prefix,
box_type_3d='LiDAR',
backend_args=backend_args))
val_evaluator = dict(
type='NuScenesMetric',
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_val.pkl',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/nus-mono3d.py 0 → 100644
View file @ 7aa442d5
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
metainfo = dict(classes=class_names)
# Input modality for nuScenes dataset, this is consistent with the submission
# format which requires the information in input_modality.
input_modality = dict(use_lidar=False, use_camera=True)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/nuscenes/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=True,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
dict(type='Resize', scale=(1600, 900), keep_ratio=True),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='Pack3DDetInputs',
keys=[
'img', 'gt_bboxes', 'gt_bboxes_labels', 'attr_labels',
'gt_bboxes_3d', 'gt_labels_3d', 'centers_2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(type='mmdet.Resize', scale=(1600, 900), keep_ratio=True),
dict(type='Pack3DDetInputs', keys=['img'])
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
pts='',
CAM_FRONT='samples/CAM_FRONT',
CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT',
CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT',
CAM_BACK='samples/CAM_BACK',
CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
CAM_BACK_LEFT='samples/CAM_BACK_LEFT'),
ann_file='nuscenes_infos_train.pkl',
load_type='mv_image_based',
pipeline=train_pipeline,
metainfo=metainfo,
modality=input_modality,
test_mode=False,
# we use box_type_3d='Camera' in monocular 3d
# detection task
box_type_3d='Camera',
use_valid_flag=True,
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
pts='',
CAM_FRONT='samples/CAM_FRONT',
CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT',
CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT',
CAM_BACK='samples/CAM_BACK',
CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
CAM_BACK_LEFT='samples/CAM_BACK_LEFT'),
ann_file='nuscenes_infos_val.pkl',
load_type='mv_image_based',
pipeline=test_pipeline,
modality=input_modality,
metainfo=metainfo,
test_mode=True,
box_type_3d='Camera',
use_valid_flag=True,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='NuScenesMetric',
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_val.pkl',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/s3dis-3d.py 0 → 100644
View file @ 7aa442d5
# dataset settings
dataset_type = 'S3DISDataset'
data_root = 'data/s3dis/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/s3dis/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
metainfo = dict(classes=('table', 'chair', 'sofa', 'bookcase', 'board'))
train_area = [1, 2, 3, 4, 6]
test_area = 5
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='PointSample', num_points=100000),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[0.9, 1.1],
translation_std=[.1, .1, .1],
shift_height=False),
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointSample', num_points=100000),
dict(type='NormalizePointsColor', color_mean=None),
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=8,
num_workers=4,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=13,
dataset=dict(
type='ConcatDataset',
datasets=[
dict(
type=dataset_type,
data_root=data_root,
ann_file=f's3dis_infos_Area_{i}.pkl',
pipeline=train_pipeline,
filter_empty_gt=True,
metainfo=metainfo,
box_type_3d='Depth',
backend_args=backend_args) for i in train_area
])))
val_dataloader = dict(
batch_size=1,
num_workers=1,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=f's3dis_infos_Area_{test_area}.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=f's3dis_infos_Area_{test_area}.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth',
backend_args=backend_args))
val_evaluator = dict(type='IndoorMetric')
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/s3dis-seg.py 0 → 100644
View file @ 7aa442d5
# For S3DIS seg we usually do 13-class segmentation
class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter')
metainfo = dict(classes=class_names)
dataset_type = 'S3DISSegDataset'
data_root = 'data/s3dis/'
input_modality = dict(use_lidar=True, use_camera=False)
data_prefix = dict(
pts='points',
pts_instance_mask='instance_mask',
pts_semantic_mask='semantic_mask')
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/s3dis/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
num_points = 4096
train_area = [1, 2, 3, 4, 6]
test_area = 5
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True,
backend_args=backend_args),
dict(type='PointSegClassMapping'),
dict(
type='IndoorPatchPointSample',
num_points=num_points,
block_size=1.0,
ignore_index=len(class_names),
use_normalized_coord=True,
enlarge_size=0.2,
min_unique_num=None),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True,
backend_args=backend_args),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='Pack3DDetInputs', keys=['points'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
# we need to load gt seg_mask!
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='Pack3DDetInputs', keys=['points'])
]
tta_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True,
backend_args=backend_args),
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='TestTimeAug',
transforms=[[
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.,
flip_ratio_bev_vertical=0.)
], [dict(type='Pack3DDetInputs', keys=['points'])]])
]
# train on area 1, 2, 3, 4, 6
# test on area 5
train_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
metainfo=metainfo,
data_prefix=data_prefix,
pipeline=train_pipeline,
modality=input_modality,
ignore_index=len(class_names),
scene_idxs=[
f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
],
test_mode=False,
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_files=f's3dis_infos_Area_{test_area}.pkl',
metainfo=metainfo,
data_prefix=data_prefix,
pipeline=test_pipeline,
modality=input_modality,
ignore_index=len(class_names),
scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy',
test_mode=True,
backend_args=backend_args))
val_dataloader = test_dataloader
val_evaluator = dict(type='SegMetric')
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
tta_model = dict(type='Seg3DTTAModel')
mmdetection3d/configs/_base_/datasets/scannet-3d.py 0 → 100644
View file @ 7aa442d5
# dataset settings
dataset_type = 'ScanNetDataset'
data_root = 'data/scannet/'
metainfo = dict(
classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain',
'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin'))
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/scannet/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True,
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSegClassMapping'),
dict(type='PointSample', num_points=40000),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[1.0, 1.0],
shift_height=True),
dict(
type='Pack3DDetInputs',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
'pts_instance_mask'
])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointSample', num_points=40000),
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=8,
num_workers=4,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=5,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='scannet_infos_train.pkl',
pipeline=train_pipeline,
filter_empty_gt=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Depth',
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='scannet_infos_val.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='scannet_infos_val.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth',
backend_args=backend_args))
val_evaluator = dict(type='IndoorMetric')
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/scannet-seg.py 0 → 100644
View file @ 7aa442d5
# For ScanNet seg we usually do 20-class segmentation
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink',
'bathtub', 'otherfurniture')
metainfo = dict(classes=class_names)
dataset_type = 'ScanNetSegDataset'
data_root = 'data/scannet/'
input_modality = dict(use_lidar=True, use_camera=False)
data_prefix = dict(
pts='points',
pts_instance_mask='instance_mask',
pts_semantic_mask='semantic_mask')
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/scannet/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
num_points = 8192
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True,
backend_args=backend_args),
dict(type='PointSegClassMapping'),
dict(
type='IndoorPatchPointSample',
num_points=num_points,
block_size=1.5,
ignore_index=len(class_names),
use_normalized_coord=False,
enlarge_size=0.2,
min_unique_num=None),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True,
backend_args=backend_args),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='Pack3DDetInputs', keys=['points'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
# we need to load gt seg_mask!
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='Pack3DDetInputs', keys=['points'])
]
tta_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True,
backend_args=backend_args),
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='TestTimeAug',
transforms=[[
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.,
flip_ratio_bev_vertical=0.)
], [dict(type='Pack3DDetInputs', keys=['points'])]])
]
train_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='scannet_infos_train.pkl',
metainfo=metainfo,
data_prefix=data_prefix,
pipeline=train_pipeline,
modality=input_modality,
ignore_index=len(class_names),
scene_idxs=data_root + 'seg_info/train_resampled_scene_idxs.npy',
test_mode=False,
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='scannet_infos_val.pkl',
metainfo=metainfo,
data_prefix=data_prefix,
pipeline=test_pipeline,
modality=input_modality,
ignore_index=len(class_names),
test_mode=True,
backend_args=backend_args))
val_dataloader = test_dataloader
val_evaluator = dict(type='SegMetric')
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
tta_model = dict(type='Seg3DTTAModel')
mmdetection3d/configs/_base_/datasets/semantickitti.py 0 → 100644
View file @ 7aa442d5
# For SemanticKitti we usually do 19-class segmentation.
# For labels_map we follow the uniform format of MMDetection & MMSegmentation
# i.e. we consider the unlabeled class as the last one, which is different
# from the original implementation of some methods e.g. Cylinder3D.
dataset_type = 'SemanticKittiDataset'
data_root = 'data/semantickitti/'
class_names = [
'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist',
'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building',
'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign'
]
labels_map = {
0: 19, # "unlabeled"
1: 19, # "outlier" mapped to "unlabeled" --------------mapped
10: 0, # "car"
11: 1, # "bicycle"
13: 4, # "bus" mapped to "other-vehicle" --------------mapped
15: 2, # "motorcycle"
16: 4, # "on-rails" mapped to "other-vehicle" ---------mapped
18: 3, # "truck"
20: 4, # "other-vehicle"
30: 5, # "person"
31: 6, # "bicyclist"
32: 7, # "motorcyclist"
40: 8, # "road"
44: 9, # "parking"
48: 10, # "sidewalk"
49: 11, # "other-ground"
50: 12, # "building"
51: 13, # "fence"
52: 19, # "other-structure" mapped to "unlabeled" ------mapped
60: 8, # "lane-marking" to "road" ---------------------mapped
70: 14, # "vegetation"
71: 15, # "trunk"
72: 16, # "terrain"
80: 17, # "pole"
81: 18, # "traffic-sign"
99: 19, # "other-object" to "unlabeled" ----------------mapped
252: 0, # "moving-car" to "car" ------------------------mapped
253: 6, # "moving-bicyclist" to "bicyclist" ------------mapped
254: 5, # "moving-person" to "person" ------------------mapped
255: 7, # "moving-motorcyclist" to "motorcyclist" ------mapped
256: 4, # "moving-on-rails" mapped to "other-vehic------mapped
257: 4, # "moving-bus" mapped to "other-vehicle" -------mapped
258: 3, # "moving-truck" to "truck" --------------------mapped
259: 4 # "moving-other"-vehicle to "other-vehicle"-----mapped
}
metainfo = dict(
classes=class_names, seg_label_mapping=labels_map, max_label=259)
input_modality = dict(use_lidar=True, use_camera=False)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/semantickitti/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_seg_3d=True,
seg_3d_dtype='np.int32',
seg_offset=2**16,
dataset_type='semantickitti',
backend_args=backend_args),
dict(type='PointSegClassMapping'),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05],
translation_std=[0.1, 0.1, 0.1],
),
dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_seg_3d=True,
seg_3d_dtype='np.int32',
seg_offset=2**16,
dataset_type='semantickitti',
backend_args=backend_args),
dict(type='PointSegClassMapping'),
dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
dict(type='Pack3DDetInputs', keys=['points'])
]
tta_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_seg_3d=True,
seg_3d_dtype='np.int32',
seg_offset=2**16,
dataset_type='semantickitti',
backend_args=backend_args),
dict(type='PointSegClassMapping'),
dict(
type='TestTimeAug',
transforms=[[
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.,
flip_ratio_bev_vertical=0.),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.,
flip_ratio_bev_vertical=1.),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=1.,
flip_ratio_bev_vertical=0.),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=1.,
flip_ratio_bev_vertical=1.)
],
[
dict(
type='GlobalRotScaleTrans',
rot_range=[pcd_rotate_range, pcd_rotate_range],
scale_ratio_range=[
pcd_scale_factor, pcd_scale_factor
],
translation_std=[0, 0, 0])
for pcd_rotate_range in [-0.78539816, 0.0, 0.78539816]
for pcd_scale_factor in [0.95, 1.0, 1.05]
], [dict(type='Pack3DDetInputs', keys=['points'])]])
]
train_dataloader = dict(
batch_size=2,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='semantickitti_infos_train.pkl',
pipeline=train_pipeline,
metainfo=metainfo,
modality=input_modality,
ignore_index=19,
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='semantickitti_infos_val.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
modality=input_modality,
ignore_index=19,
test_mode=True,
backend_args=backend_args))
val_dataloader = test_dataloader
val_evaluator = dict(type='SegMetric')
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
tta_model = dict(type='Seg3DTTAModel')
mmdetection3d/configs/_base_/datasets/sunrgbd-3d.py 0 → 100644
View file @ 7aa442d5
dataset_type = 'SUNRGBDDataset'
data_root = 'data/sunrgbd/'
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
'night_stand', 'bookshelf', 'bathtub')
metainfo = dict(classes=class_names)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/sunrgbd/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(type='LoadAnnotations3D'),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.523599, 0.523599],
scale_ratio_range=[0.85, 1.15],
shift_height=True),
dict(type='PointSample', num_points=20000),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
),
dict(type='PointSample', num_points=20000)
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=16,
num_workers=4,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=5,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='sunrgbd_infos_train.pkl',
pipeline=train_pipeline,
filter_empty_gt=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Depth',
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='sunrgbd_infos_val.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='sunrgbd_infos_val.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth',
backend_args=backend_args))
val_evaluator = dict(type='IndoorMetric')
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/waymoD3-fov-mono3d-3class.py 0 → 100644
View file @ 7aa442d5
# dataset settings
# D3 in the config name means the whole dataset is divided into 3 folds
# We only use one fold for efficient experiments
dataset_type = 'WaymoDataset'
data_root = 'data/waymo/kitti_format/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
metainfo = dict(classes=class_names)
input_modality = dict(use_lidar=False, use_camera=True)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
# base shape (1248, 832), scale (0.95, 1.05)
dict(
type='RandomResize3D',
scale=(1248, 832),
ratio_range=(0.95, 1.05),
# ratio_range=(1., 1.),
interpolation='nearest',
keep_ratio=True,
),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='Pack3DDetInputs',
keys=[
'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
'gt_labels_3d', 'centers_2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='RandomResize3D',
scale=(1248, 832),
ratio_range=(1., 1.),
interpolation='nearest',
keep_ratio=True),
dict(
type='Pack3DDetInputs',
keys=['img'],
meta_keys=[
'box_type_3d', 'img_shape', 'cam2img', 'scale_factor',
'sample_idx', 'context_name', 'timestamp', 'lidar2cam'
]),
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='RandomResize3D',
scale=(1248, 832),
ratio_range=(1., 1.),
interpolation='nearest',
keep_ratio=True),
dict(
type='Pack3DDetInputs',
keys=['img'],
meta_keys=[
'box_type_3d', 'img_shape', 'cam2img', 'scale_factor',
'sample_idx', 'context_name', 'timestamp', 'lidar2cam'
]),
]
train_dataloader = dict(
batch_size=3,
num_workers=3,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='waymo_infos_train.pkl',
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
metainfo=metainfo,
cam_sync_instances=True,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='fov_image_based',
# load one frame every three frames
load_interval=3,
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
cam_sync_instances=True,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='fov_image_based',
load_eval_anns=False,
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
cam_sync_instances=True,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='fov_image_based',
backend_args=backend_args))
val_evaluator = dict(
type='WaymoMetric',
waymo_bin_file='./data/waymo/waymo_format/fov_gt.bin',
metric='LET_mAP',
load_type='fov_image_based',
result_prefix='./pgd_fov_pred')
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/waymoD3-mv-mono3d-3class.py 0 → 100644
View file @ 7aa442d5
# dataset settings
# D3 in the config name means the whole dataset is divided into 3 folds
# We only use one fold for efficient experiments
dataset_type = 'WaymoDataset'
data_root = 'data/waymo/kitti_format/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
metainfo = dict(classes=class_names)
input_modality = dict(use_lidar=False, use_camera=True)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
# base shape (1248, 832), scale (0.95, 1.05)
dict(
type='RandomResize3D',
scale=(1248, 832),
# ratio_range=(1., 1.),
ratio_range=(0.95, 1.05),
interpolation='nearest',
keep_ratio=True,
),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='Pack3DDetInputs',
keys=[
'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
'gt_labels_3d', 'centers_2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='Resize3D',
scale_factor=0.65,
interpolation='nearest',
keep_ratio=True),
dict(
type='Pack3DDetInputs',
keys=['img'],
meta_keys=[
'box_type_3d', 'img_shape', 'cam2img', 'scale_factor',
'sample_idx', 'context_name', 'timestamp', 'lidar2cam'
]),
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='Resize3D',
scale_factor=0.65,
interpolation='nearest',
keep_ratio=True),
dict(
type='Pack3DDetInputs',
keys=['img'],
meta_keys=[
'box_type_3d', 'img_shape', 'cam2img', 'scale_factor',
'sample_idx', 'context_name', 'timestamp', 'lidar2cam'
]),
]
train_dataloader = dict(
batch_size=3,
num_workers=3,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='waymo_infos_train.pkl',
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
metainfo=metainfo,
cam_sync_instances=True,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='mv_image_based',
# load one frame every three frames
load_interval=3,
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=0,
persistent_workers=False,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
cam_sync_instances=True,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='mv_image_based',
# load_eval_anns=False,
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=0,
persistent_workers=False,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
cam_sync_instances=True,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='mv_image_based',
load_eval_anns=False,
backend_args=backend_args))
val_evaluator = dict(
type='WaymoMetric',
waymo_bin_file='./data/waymo/waymo_format/cam_gt.bin',
metric='LET_mAP',
load_type='mv_image_based',
result_prefix='./pgd_mv_pred',
nms_cfg=dict(
use_rotate_nms=True,
nms_across_levels=False,
nms_pre=500,
nms_thr=0.05,
score_thr=0.001,
min_bbox_size=0,
max_per_frame=100))
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/waymoD5-3d-3class.py 0 → 100644
View file @ 7aa442d5
# dataset settings
# D5 in the config name means the whole dataset is divided into 5 folds
# We only use one fold for efficient experiments
dataset_type = 'WaymoDataset'
# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
data_root = 'data/waymo/kitti_format/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
class_names = ['Car', 'Pedestrian', 'Cyclist']
metainfo = dict(classes=class_names)
point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4]
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'waymo_dbinfos_train.pkl',
rate=1.0,
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
classes=class_names,
sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=[0, 1, 2, 3, 4],
backend_args=backend_args),
backend_args=backend_args)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=5,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
# dict(type='ObjectSample', db_sampler=db_sampler),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05]),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=5,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range)
]),
dict(
type='Pack3DDetInputs',
keys=['points'],
meta_keys=['box_type_3d', 'sample_idx', 'context_name', 'timestamp'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=5,
backend_args=backend_args),
dict(
type='Pack3DDetInputs',
keys=['points'],
meta_keys=['box_type_3d', 'sample_idx', 'context_name', 'timestamp'])
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='waymo_infos_train.pkl',
data_prefix=dict(
pts='training/velodyne', sweeps='training/velodyne'),
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR',
# load one frame every five frames
load_interval=5,
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR',
backend_args=backend_args))
val_evaluator = dict(
type='WaymoMetric', waymo_bin_file='./data/waymo/waymo_format/gt.bin')
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/waymoD5-3d-car.py 0 → 100644
View file @ 7aa442d5
# dataset settings
# D5 in the config name means the whole dataset is divided into 5 folds
# We only use one fold for efficient experiments
dataset_type = 'WaymoDataset'
data_root = 'data/waymo/kitti_format/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
class_names = ['Car']
metainfo = dict(classes=class_names)
point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4]
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'waymo_dbinfos_train.pkl',
rate=1.0,
prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
classes=class_names,
sample_groups=dict(Car=15),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=[0, 1, 2, 3, 4],
backend_args=backend_args),
backend_args=backend_args)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=5,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05]),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(
type='Pack3DDetInputs',
keys=['points'],
meta_keys=['box_type_3d', 'sample_idx', 'context_name', 'timestamp'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=5,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range)
]),
dict(
type='Pack3DDetInputs',
keys=['points'],
meta_keys=['box_type_3d', 'sample_idx', 'context_name', 'timestamp'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=5,
backend_args=backend_args),
dict(type='Pack3DDetInputs', keys=['points']),
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='waymo_infos_train.pkl',
data_prefix=dict(
pts='training/velodyne', sweeps='training/velodyne'),
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR',
# load one frame every five frames
load_interval=5,
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR',
backend_args=backend_args))
val_evaluator = dict(
type='WaymoMetric', waymo_bin_file='./data/waymo/waymo_format/gt.bin')
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/waymoD5-fov-mono3d-3class.py 0 → 100644
View file @ 7aa442d5
# dataset settings
# D3 in the config name means the whole dataset is divided into 3 folds
# We only use one fold for efficient experiments
dataset_type = 'WaymoDataset'
data_root = 'data/waymo/kitti_format/'
class_names = ['Car', 'Pedestrian', 'Cyclist']
input_modality = dict(use_lidar=False, use_camera=True)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
# base shape (1248, 832), scale (0.95, 1.05)
dict(
type='RandomResize3D',
scale=(1284, 832),
ratio_range=(0.95, 1.05),
keep_ratio=True,
),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='Pack3DDetInputs',
keys=[
'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
'gt_labels_3d', 'centers_2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='RandomResize3D',
scale=(1248, 832),
ratio_range=(1., 1.),
keep_ratio=True),
dict(type='Pack3DDetInputs', keys=['img']),
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='RandomResize3D',
scale=(1248, 832),
ratio_range=(1., 1.),
keep_ratio=True),
dict(type='Pack3DDetInputs', keys=['img']),
]
metainfo = dict(CLASSES=class_names)
train_dataloader = dict(
batch_size=3,
num_workers=3,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='waymo_infos_train.pkl',
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='fov_image_based',
# load one frame every three frames
load_interval=5,
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='fov_image_based',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='fov_image_based',
backend_args=backend_args))
val_evaluator = dict(
type='WaymoMetric',
ann_file='./data/waymo/kitti_format/waymo_infos_val.pkl',
waymo_bin_file='./data/waymo/waymo_format/fov_gt.bin',
data_root='./data/waymo/waymo_format',
metric='LET_mAP',
load_type='fov_image_based',
backend_args=backend_args)
test_evaluator = val_evaluator
mmdetection3d/configs/_base_/datasets/waymoD5-mv-mono3d-3class.py 0 → 100644
View file @ 7aa442d5
# dataset settings
# D3 in the config name means the whole dataset is divided into 3 folds
# We only use one fold for efficient experiments
dataset_type = 'WaymoDataset'
data_root = 'data/waymo/kitti_format/'
class_names = ['Car', 'Pedestrian', 'Cyclist']
input_modality = dict(use_lidar=False, use_camera=True)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
# base shape (1248, 832), scale (0.95, 1.05)
dict(
type='RandomResize3D',
scale=(1284, 832),
ratio_range=(0.95, 1.05),
keep_ratio=True,
),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='Pack3DDetInputs',
keys=[
'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
'gt_labels_3d', 'centers_2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='RandomResize3D',
scale=(1248, 832),
ratio_range=(1., 1.),
keep_ratio=True),
dict(type='Pack3DDetInputs', keys=['img']),
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='RandomResize3D',
scale=(1248, 832),
ratio_range=(1., 1.),
keep_ratio=True),
dict(type='Pack3DDetInputs', keys=['img']),
]
metainfo = dict(classes=class_names)
train_dataloader = dict(
batch_size=3,
num_workers=3,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='waymo_infos_train.pkl',
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='mv_image_based',
# load one frame every three frames
load_interval=5,
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='mv_image_based',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
ann_file='waymo_infos_val.pkl',
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='Camera',
load_type='mv_image_based',
backend_args=backend_args))
val_evaluator = dict(
type='WaymoMetric',
ann_file='./data/waymo/kitti_format/waymo_infos_val.pkl',
waymo_bin_file='./data/waymo/waymo_format/cam_gt.bin',
data_root='./data/waymo/waymo_format',
metric='LET_mAP',
load_type='mv_image_based',
backend_args=backend_args)
test_evaluator = val_evaluator
mmdetection3d/configs/_base_/datasets/waymoD5-mv3d-3class.py 0 → 100644
View file @ 7aa442d5
# dataset settings
# D5 in the config name means the whole dataset is divided into 5 folds
# We only use one fold for efficient experiments
dataset_type = 'WaymoDataset'
data_root = 'data/waymo/kitti_format/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
class_names = ['Pedestrian', 'Cyclist', 'Car']
input_modality = dict(use_lidar=False, use_camera=True)
point_cloud_range = [-35.0, -75.0, -2, 75.0, 75.0, 4]
train_transforms = [
dict(type='PhotoMetricDistortion3D'),
dict(
type='RandomResize3D',
scale=(1248, 832),
ratio_range=(0.95, 1.05),
keep_ratio=True),
dict(type='RandomCrop3D', crop_size=(1080, 720)),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5, flip_box3d=False),
]
train_pipeline = [
dict(
type='LoadMultiViewImageFromFiles',
to_float32=True,
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
dict(type='MultiViewWrapper', transforms=train_transforms),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(
type='Pack3DDetInputs', keys=[
'img',
'gt_bboxes_3d',
'gt_labels_3d',
]),
]
test_transforms = [
dict(
type='RandomResize3D',
scale=(1248, 832),
ratio_range=(1., 1.),
keep_ratio=True)
]
test_pipeline = [
dict(
type='LoadMultiViewImageFromFiles',
to_float32=True,
backend_args=backend_args),
dict(type='MultiViewWrapper', transforms=test_transforms),
dict(
type='Pack3DDetInputs',
keys=['img'],
meta_keys=[
'box_type_3d', 'img_shape', 'ori_cam2img', 'scale_factor',
'sample_idx', 'context_name', 'timestamp', 'lidar2cam',
'num_ref_frames', 'num_views'
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadMultiViewImageFromFiles',
to_float32=True,
backend_args=backend_args),
dict(type='MultiViewWrapper', transforms=test_transforms),
dict(
type='Pack3DDetInputs',
keys=['img'],
meta_keys=[
'box_type_3d', 'img_shape', 'ori_cam2img', 'scale_factor',
'sample_idx', 'context_name', 'timestamp', 'lidar2cam',
'num_ref_frames', 'num_views'
])
]
metainfo = dict(classes=class_names)
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='waymo_infos_train.pkl',
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
cam_sync_instances=True,
metainfo=metainfo,
box_type_3d='Lidar',
load_interval=5,
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='waymo_infos_val.pkl',
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
pipeline=eval_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
box_type_3d='Lidar',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='waymo_infos_val.pkl',
data_prefix=dict(
pts='training/velodyne',
CAM_FRONT='training/image_0',
CAM_FRONT_LEFT='training/image_1',
CAM_FRONT_RIGHT='training/image_2',
CAM_SIDE_LEFT='training/image_3',
CAM_SIDE_RIGHT='training/image_4'),
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
box_type_3d='Lidar',
backend_args=backend_args))
val_evaluator = dict(
type='WaymoMetric',
waymo_bin_file='./data/waymo/waymo_format/cam_gt.bin',
metric='LET_mAP')
test_evaluator = val_evaluator
mmdetection3d/configs/_base_/default_runtime.py 0 → 100644
View file @ 7aa442d5
default_scope = 'mmdet3d'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=-1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='Det3DVisualizationHook'))
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'),
)
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False
# TODO: support auto scaling lr
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