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Unverified Commit cf6f4732 authored by Xiang Xu's avatar Xiang Xu Committed by GitHub
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[Feature] File I/O migration and reconstruction (#2319) 

* refactor leio

* update mmengine and mmcv version

* update

* update docs

* update version
parent b2e5ad6b
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Showing with 204 additions and 139 deletions
configs/centerpoint/README.md
View file @ cf6f4732
......@@ -56,7 +56,7 @@ model = dict(
max_num=83)))
point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
file_client_args = dict(backend='disk')
backend_args = None
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
......@@ -67,12 +67,12 @@ test_pipeline = [
type='LoadPointsFromFile',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
file_client_args=file_client_args,
backend_args=backend_args,
pad_empty_sweeps=True,
remove_close=True),
dict(
......
configs/centerpoint/centerpoint_pillar02_second_secfpn_8xb4-cyclic-20e_nus-3d.py
View file @ cf6f4732
......@@ -27,7 +27,7 @@ model = dict(
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
file_client_args = dict(backend='disk')
backend_args = None
db_sampler = dict(
data_root=data_root,
......@@ -62,16 +62,24 @@ db_sampler = dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=[0, 1, 2, 3, 4]))
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=5, use_dim=5),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
pad_empty_sweeps=True,
remove_close=True),
remove_close=True,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(
......@@ -93,13 +101,19 @@ train_pipeline = [
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
pad_empty_sweeps=True,
remove_close=True),
remove_close=True,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
......@@ -135,7 +149,8 @@ train_dataloader = dict(
use_valid_flag=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='LiDAR')))
box_type_3d='LiDAR',
backend_args=backend_args)))
test_dataloader = dict(
dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
val_dataloader = dict(
......
configs/centerpoint/centerpoint_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py
View file @ cf6f4732
......@@ -31,7 +31,7 @@ model = dict(
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
file_client_args = dict(backend='disk')
backend_args = None
db_sampler = dict(
data_root=data_root,
......@@ -66,16 +66,24 @@ db_sampler = dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=[0, 1, 2, 3, 4]))
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=5, use_dim=5),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
pad_empty_sweeps=True,
remove_close=True),
remove_close=True,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(
......@@ -97,13 +105,19 @@ train_pipeline = [
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
pad_empty_sweeps=True,
remove_close=True),
remove_close=True,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
......
configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn-circlenms_8xb4-flip-tta-cyclic-20e_nus-3d.py
View file @ cf6f4732
......@@ -5,20 +5,26 @@ point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# 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 = [-54, -54.8, -5.0, 54, 53.2, 3.0]
file_client_args = dict(backend='disk')
backend_args = None
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
pad_empty_sweeps=True,
remove_close=True),
remove_close=True,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
......
configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn_8xb4-flip-tta-cyclic-20e_nus-3d.py
View file @ cf6f4732
......@@ -5,20 +5,26 @@ point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# 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 = [-54, -54.8, -5.0, 54, 53.2, 3.0]
file_client_args = dict(backend='disk')
backend_args = None
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
pad_empty_sweeps=True,
remove_close=True),
remove_close=True,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
......
configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn_8xb4-tta-cyclic-20e_nus-3d.py
View file @ cf6f4732
......@@ -7,20 +7,26 @@ point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# 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 = [-54, -54.8, -5.0, 54, 53.2, 3.0]
file_client_args = dict(backend='disk')
backend_args = None
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
pad_empty_sweeps=True,
remove_close=True),
remove_close=True,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
......
configs/centerpoint/centerpoint_voxel01_second_secfpn_8xb4-cyclic-20e_nus-3d.py
View file @ cf6f4732
......@@ -26,7 +26,7 @@ model = dict(
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
file_client_args = dict(backend='disk')
backend_args = None
db_sampler = dict(
data_root=data_root,
......@@ -61,16 +61,24 @@ db_sampler = dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=[0, 1, 2, 3, 4]))
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=5, use_dim=5),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
pad_empty_sweeps=True,
remove_close=True),
remove_close=True,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(
......@@ -92,13 +100,19 @@ train_pipeline = [
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
pad_empty_sweeps=True,
remove_close=True),
remove_close=True,
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
......@@ -136,7 +150,8 @@ train_dataloader = dict(
use_valid_flag=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='LiDAR')))
box_type_3d='LiDAR',
backend_args=backend_args)))
test_dataloader = dict(
dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
val_dataloader = dict(
......
configs/fcaf3d/fcaf3d_2xb8_scannet-3d-18class.py
View file @ cf6f4732
......@@ -3,6 +3,7 @@ _base_ = [
'../_base_/datasets/scannet-3d.py'
]
n_points = 100000
backend_args = None
train_pipeline = [
dict(
......@@ -11,7 +12,8 @@ train_pipeline = [
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(type='LoadAnnotations3D'),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSample', num_points=n_points),
......@@ -38,7 +40,8 @@ test_pipeline = [
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
......
configs/fcaf3d/fcaf3d_2xb8_sunrgbd-3d-10class.py
View file @ cf6f4732
......@@ -3,6 +3,7 @@ _base_ = [
'../_base_/datasets/sunrgbd-3d.py'
]
n_points = 100000
backend_args = None
model = dict(
bbox_head=dict(
......@@ -16,7 +17,8 @@ train_pipeline = [
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(type='LoadAnnotations3D'),
dict(type='PointSample', num_points=n_points),
dict(type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5),
......@@ -36,7 +38,8 @@ test_pipeline = [
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
use_dim=[0, 1, 2, 3, 4, 5],
backend_args=backend_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
......
configs/fcos3d/fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py
View file @ cf6f4732
......@@ -14,21 +14,10 @@ model = dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, False, True, True)))
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://openmmlab/datasets/detection3d/nuscenes/',
# 'data/nuscenes/':
# 's3://openmmlab/datasets/detection3d/nuscenes/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
......@@ -47,7 +36,7 @@ train_pipeline = [
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(type='mmdet.Resize', scale_factor=1.0),
dict(type='Pack3DDetInputs', keys=['img'])
]
......
configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py
View file @ cf6f4732
......@@ -25,19 +25,19 @@ 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/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
dict(type='LoadPointsFromMultiSweeps', sweeps_num=10),
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',
......
configs/free_anchor/pointpillars_hv_regnet-3.2gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py
View file @ cf6f4732
......@@ -25,27 +25,19 @@ 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/'
# }))
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
......
configs/groupfree3d/groupfree3d_head-L12-O256_4xb8_scannet-seg.py
View file @ cf6f4732
......@@ -79,19 +79,22 @@ class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'garbagebin')
metainfo = dict(classes=class_names)
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
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),
with_seg_3d=True,
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSegClassMapping'),
dict(type='PointSample', num_points=50000),
......@@ -116,7 +119,8 @@ test_pipeline = [
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
......@@ -155,7 +159,8 @@ train_dataloader = dict(
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')))
box_type_3d='Depth',
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
......@@ -167,7 +172,8 @@ val_dataloader = dict(
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth'))
box_type_3d='Depth',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
......@@ -179,7 +185,8 @@ test_dataloader = dict(
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth'))
box_type_3d='Depth',
backend_args=backend_args))
val_evaluator = dict(type='IndoorMetric')
test_evaluator = val_evaluator
......
configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py
View file @ cf6f4732
......@@ -78,19 +78,22 @@ class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'garbagebin')
metainfo = dict(classes=class_names)
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
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),
with_seg_3d=True,
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSegClassMapping'),
dict(type='PointSample', num_points=50000),
......@@ -115,7 +118,8 @@ test_pipeline = [
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
......@@ -154,7 +158,8 @@ train_dataloader = dict(
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')))
box_type_3d='Depth',
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
......@@ -166,7 +171,8 @@ val_dataloader = dict(
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth'))
box_type_3d='Depth',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
......@@ -178,7 +184,8 @@ test_dataloader = dict(
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth'))
box_type_3d='Depth',
backend_args=backend_args))
val_evaluator = dict(type='IndoorMetric')
test_evaluator = val_evaluator
......
configs/groupfree3d/groupfree3d_w2x-head-L12-O256_4xb8_scannet-seg.py
View file @ cf6f4732
......@@ -94,19 +94,22 @@ class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'garbagebin')
metainfo = dict(classes=class_names)
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
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),
with_seg_3d=True,
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSegClassMapping'),
dict(type='PointSample', num_points=50000),
......@@ -131,7 +134,8 @@ test_pipeline = [
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
......@@ -170,7 +174,8 @@ train_dataloader = dict(
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')))
box_type_3d='Depth',
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
......@@ -182,7 +187,8 @@ val_dataloader = dict(
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth'))
box_type_3d='Depth',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
......@@ -194,7 +200,8 @@ test_dataloader = dict(
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth'))
box_type_3d='Depth',
backend_args=backend_args))
val_evaluator = dict(type='IndoorMetric')
test_evaluator = val_evaluator
......
configs/groupfree3d/groupfree3d_w2x-head-L12-O512_4xb8_scannet-seg.py
View file @ cf6f4732
......@@ -95,19 +95,22 @@ class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'garbagebin')
metainfo = dict(classes=class_names)
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
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),
with_seg_3d=True,
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSegClassMapping'),
dict(type='PointSample', num_points=50000),
......@@ -132,7 +135,8 @@ test_pipeline = [
type='LoadPointsFromFile',
coord_type='DEPTH',
load_dim=6,
use_dim=[0, 1, 2]),
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
......@@ -171,7 +175,8 @@ train_dataloader = dict(
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')))
box_type_3d='Depth',
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
......@@ -183,7 +188,8 @@ val_dataloader = dict(
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth'))
box_type_3d='Depth',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
......@@ -195,7 +201,8 @@ test_dataloader = dict(
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
box_type_3d='Depth'))
box_type_3d='Depth',
backend_args=backend_args))
val_evaluator = dict(type='IndoorMetric')
test_evaluator = val_evaluator
......
configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py
View file @ cf6f4732
......@@ -3,8 +3,10 @@ _base_ = [
'../_base_/models/imvotenet.py'
]
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
......@@ -22,7 +24,7 @@ train_pipeline = [
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadImageFromFile', backend_args=backend_args),
# online evaluation
dict(
type='LoadAnnotations3D',
......
configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py
View file @ cf6f4732
......@@ -5,6 +5,7 @@ _base_ = [
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
'night_stand', 'bookshelf', 'bathtub')
backend_args = None
model = dict(
pts_backbone=dict(
......@@ -170,8 +171,9 @@ train_pipeline = [
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='LoadImageFromFile'),
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox=True,
......@@ -198,13 +200,14 @@ train_pipeline = [
]))
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
use_dim=[0, 1, 2],
backend_args=backend_args),
dict(type='Resize', scale=(1333, 600), keep_ratio=True),
dict(type='PointSample', num_points=20000),
dict(type='Pack3DDetInputs', keys=['img', 'points'])
......
configs/imvoxelnet/imvoxelnet_2xb4_sunrgbd-3d-10class.py
View file @ cf6f4732
......@@ -56,28 +56,17 @@ class_names = [
]
metainfo = dict(CLASSES=class_names)
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/sunrgbd/':
# 's3://openmmlab/datasets/detection3d/sunrgbd_processed/',
# 'data/sunrgbd/':
# 's3://openmmlab/datasets/detection3d/sunrgbd_processed/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadAnnotations3D'),
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='LoadAnnotations3D', backend_args=backend_args),
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='RandomResize', scale=[(512, 384), (768, 576)], keep_ratio=True),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='Pack3DDetInputs', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(640, 480), keep_ratio=True),
dict(type='Pack3DDetInputs', keys=['img'])
]
......@@ -98,7 +87,8 @@ train_dataloader = dict(
test_mode=False,
filter_empty_gt=True,
box_type_3d='Depth',
metainfo=metainfo)))
metainfo=metainfo,
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
......@@ -112,7 +102,8 @@ val_dataloader = dict(
pipeline=test_pipeline,
test_mode=True,
box_type_3d='Depth',
metainfo=metainfo))
metainfo=metainfo,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
......
configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py
View file @ cf6f4732
......@@ -84,22 +84,11 @@ input_modality = dict(use_lidar=False, use_camera=True)
point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
metainfo = dict(classes=class_names)
# 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/kitti/':
's3://openmmlab/datasets/detection3d/kitti/',
'data/kitti/':
's3://openmmlab/datasets/detection3d/kitti/'
}))
backend_args = None
train_pipeline = [
dict(type='LoadAnnotations3D'),
dict(type='LoadImageFromFileMono3D'),
dict(type='LoadAnnotations3D', backend_args=backend_args),
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='RandomResize', scale=[(1173, 352), (1387, 416)],
......@@ -108,7 +97,7 @@ train_pipeline = [
dict(type='Pack3DDetInputs', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
dict(type='Resize', scale=(1280, 384), keep_ratio=True),
dict(type='Pack3DDetInputs', keys=['img'])
]
......@@ -129,7 +118,8 @@ train_dataloader = dict(
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
metainfo=metainfo)))
metainfo=metainfo,
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
......@@ -144,13 +134,15 @@ val_dataloader = dict(
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo))
metainfo=metainfo,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='KittiMetric',
ann_file=data_root + 'kitti_infos_val.pkl',
metric='bbox')
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
# optimizer
......
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