Use file client

configs/fileclient/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py

+# model settings
+voxel_size = [0.25, 0.25, 8]
+point_cloud_range = [-50, -50, -5, 50, 50, 3]
+class_names = [
+    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
+]
+model = dict(
+    type='MVXFasterRCNNV2',
+    pts_voxel_layer=dict(
+        max_num_points=64,  # max_points_per_voxel
+        point_cloud_range=point_cloud_range,  # velodyne coordinates, x, y, z
+        voxel_size=voxel_size,
+        max_voxels=(30000, 40000),  # (training, testing) max_coxels
+    ),
+    pts_voxel_encoder=dict(
+        type='HardVFE',
+        num_input_features=4,
+        num_filters=[64, 64],
+        with_distance=False,
+        voxel_size=voxel_size,
+        with_cluster_center=True,
+        with_voxel_center=True,
+        point_cloud_range=point_cloud_range,
+        norm_cfg=dict(type='naiveSyncBN1d', eps=1e-3, momentum=0.01)),
+    pts_middle_encoder=dict(
+        type='PointPillarsScatter',
+        in_channels=64,
+        output_shape=[400, 400],  # checked from PointCloud3D
+    ),
+    pts_backbone=dict(
+        type='SECOND',
+        in_channels=64,
+        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
+        layer_nums=[3, 5, 5],
+        layer_strides=[2, 2, 2],
+        out_channels=[64, 128, 256],
+    ),
+    pts_neck=dict(
+        type='SECONDFPN',
+        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
+        in_channels=[64, 128, 256],
+        upsample_strides=[1, 2, 4],
+        out_channels=[128, 128, 128],
+    ),
+    pts_bbox_head=dict(
+        type='Anchor3DHead',
+        num_classes=10,
+        in_channels=384,
+        feat_channels=384,
+        use_direction_classifier=True,
+        anchor_generator=dict(
+            type='Anchor3DRangeGenerator',
+            ranges=[
+                [-49.6, -49.6, -1.80032795, 49.6, 49.6, -1.80032795],
+                [-49.6, -49.6, -1.74440365, 49.6, 49.6, -1.74440365],
+                [-49.6, -49.6, -1.68526504, 49.6, 49.6, -1.68526504],
+                [-49.6, -49.6, -1.67339111, 49.6, 49.6, -1.67339111],
+                [-49.6, -49.6, -1.61785072, 49.6, 49.6, -1.61785072],
+                [-49.6, -49.6, -1.80984986, 49.6, 49.6, -1.80984986],
+                [-49.6, -49.6, -1.763965, 49.6, 49.6, -1.763965],
+            ],
+            sizes=[
+                [1.95017717, 4.60718145, 1.72270761],  # car
+                [2.4560939, 6.73778078, 2.73004906],  # truck
+                [2.87427237, 12.01320693, 3.81509561],  # trailer
+                [0.60058911, 1.68452161, 1.27192197],  # bicycle
+                [0.66344886, 0.7256437, 1.75748069],  # pedestrian
+                [0.39694519, 0.40359262, 1.06232151],  # traffic_cone
+                [2.49008838, 0.48578221, 0.98297065],  # barrier
+            ],
+            custom_values=[0, 0],
+            rotations=[0, 1.57],
+            reshape_out=True),
+        assigner_per_size=False,
+        diff_rad_by_sin=True,
+        dir_offset=0.7854,  # pi/4
+        dir_limit_offset=0,
+        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=9),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
+        loss_dir=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)))
+# model training and testing settings
+train_cfg = dict(
+    pts=dict(
+        assigner=dict(  # for Car
+            type='MaxIoUAssigner',
+            iou_calculator=dict(type='BboxOverlapsNearest3D'),
+            pos_iou_thr=0.6,
+            neg_iou_thr=0.3,
+            min_pos_iou=0.3,
+            ignore_iof_thr=-1),
+        allowed_border=0,
+        code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2],
+        pos_weight=-1,
+        debug=False))
+test_cfg = dict(
+    pts=dict(
+        use_rotate_nms=True,
+        nms_across_levels=False,
+        nms_pre=1000,
+        nms_thr=0.2,
+        score_thr=0.05,
+        min_bbox_size=0,
+        max_num=500
+        # soft-nms is also supported for rcnn testing
+        # e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
+    ))
+
+# dataset settings
+dataset_type = 'NuScenesDataset'
+data_root = 'data/nuscenes/'
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+input_modality = dict(
+    use_lidar=True,
+    use_depth=False,
+    use_lidar_intensity=True,
+    use_camera=False,
+)
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'nuscenes_dbinfos_train.pkl',
+    rate=1.0,
+    object_rot_range=[0.0, 0.0],
+    prepare=dict(),
+    classes=class_names,
+    sample_groups=dict(
+        bus=4,
+        trailer=4,
+        truck=4,
+    ))
+
+file_client_args = dict(
+    backend='petrel',
+    path_mapping=dict({
+        './data/nuscenes/': 's3://nuscenes/nuscenes/',
+        'data/nuscenes/': 's3://nuscenes/nuscenes/'
+    }))
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(
+        type='GlobalRotScale',
+        rot_uniform_noise=[-0.3925, 0.3925],
+        scaling_uniform_noise=[0.95, 1.05],
+        trans_normal_noise=[0, 0, 0]),
+    dict(type='RandomFlip3D', flip_ratio=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='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']),
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='RandomFlip3D', flip_ratio=0),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points']),
+]
+
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_train.pkl',
+        pipeline=train_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=False),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_val.pkl',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_val.pkl',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True))
+# optimizer
+optimizer = dict(type='AdamW', lr=0.001, weight_decay=0.01)
+# max_norm=10 is better for SECOND
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[20, 23])
+momentum_config = None
+checkpoint_config = dict(interval=1)
+# yapf:disable
+evaluation = dict(interval=24)
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+# runtime settings
+total_epochs = 24
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]

configs/fileclient/hv_second_secfpn_6x8_80e_fileclient_kitti-3d-car.py

+# model settings
+voxel_size = [0.05, 0.05, 0.1]
+point_cloud_range = [0, -40, -3, 70.4, 40, 1]  # velodyne coordinates, x, y, z
+
+model = dict(
+    type='VoxelNet',
+    voxel_layer=dict(
+        max_num_points=5,  # max_points_per_voxel
+        point_cloud_range=point_cloud_range,
+        voxel_size=voxel_size,
+        max_voxels=(16000, 40000),  # (training, testing) max_coxels
+    ),
+    voxel_encoder=dict(
+        type='VoxelFeatureExtractorV3',
+        num_input_features=4,
+        num_filters=[4],
+        with_distance=False),
+    middle_encoder=dict(
+        type='SparseEncoder',
+        in_channels=4,
+        sparse_shape=[41, 1600, 1408],
+        order=('conv', 'norm', 'act')),
+    backbone=dict(
+        type='SECOND',
+        in_channels=256,
+        layer_nums=[5, 5],
+        layer_strides=[1, 2],
+        out_channels=[128, 256],
+    ),
+    neck=dict(
+        type='SECONDFPN',
+        in_channels=[128, 256],
+        upsample_strides=[1, 2],
+        out_channels=[256, 256],
+    ),
+    bbox_head=dict(
+        type='Anchor3DHead',
+        num_classes=1,
+        in_channels=512,
+        feat_channels=512,
+        use_direction_classifier=True,
+        anchor_generator=dict(
+            type='Anchor3DRangeGenerator',
+            ranges=[[0, -40.0, -1.78, 70.4, 40.0, -1.78]],
+            sizes=[[1.6, 3.9, 1.56]],
+            rotations=[0, 1.57],
+            reshape_out=True),
+        diff_rad_by_sin=True,
+        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
+        loss_dir=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2),
+    ),
+)
+# model training and testing settings
+train_cfg = dict(
+    assigner=dict(
+        type='MaxIoUAssigner',
+        iou_calculator=dict(type='BboxOverlapsNearest3D'),
+        pos_iou_thr=0.6,
+        neg_iou_thr=0.45,
+        min_pos_iou=0.45,
+        ignore_iof_thr=-1),
+    allowed_border=0,
+    pos_weight=-1,
+    debug=False)
+test_cfg = dict(
+    use_rotate_nms=True,
+    nms_across_levels=False,
+    nms_thr=0.01,
+    score_thr=0.3,
+    min_bbox_size=0,
+    nms_pre=100,
+    max_num=50)
+
+# dataset settings
+dataset_type = 'KittiDataset'
+data_root = 'data/kitti/'
+class_names = ['Car']
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+input_modality = dict(
+    use_lidar=False,
+    use_lidar_reduced=True,
+    use_depth=False,
+    use_lidar_intensity=True,
+    use_camera=False,
+)
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'kitti_dbinfos_train.pkl',
+    rate=1.0,
+    object_rot_range=[0.0, 0.0],
+    prepare=dict(
+        filter_by_difficulty=[-1],
+        filter_by_min_points=dict(Car=5),
+    ),
+    classes=class_names,
+    sample_groups=dict(Car=15),
+)
+file_client_args = dict(
+    backend='petrel', path_mapping=dict(data='s3://kitti_data/'))
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        load_dim=4,
+        use_dim=4,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=True,
+        with_label_3d=True,
+        file_client_args=file_client_args),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='ObjectNoise',
+        num_try=100,
+        loc_noise_std=[1.0, 1.0, 0.5],
+        global_rot_range=[0.0, 0.0],
+        rot_uniform_noise=[-0.78539816, 0.78539816]),
+    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(
+        type='GlobalRotScale',
+        rot_uniform_noise=[-0.78539816, 0.78539816],
+        scaling_uniform_noise=[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='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']),
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        load_dim=4,
+        use_dim=4,
+        file_client_args=file_client_args),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points']),
+]
+
+data = dict(
+    samples_per_gpu=6,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_train.pkl',
+        split='training',
+        pts_prefix='velodyne_reduced',
+        pipeline=train_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=False),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_val.pkl',
+        split='training',
+        pts_prefix='velodyne_reduced',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_val.pkl',
+        split='training',
+        pts_prefix='velodyne_reduced',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True))
+# optimizer
+lr = 0.0018  # max learning rate
+optimizer = dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01)
+optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
+lr_config = dict(
+    policy='cyclic',
+    target_ratio=(10, 1e-4),
+    cyclic_times=1,
+    step_ratio_up=0.4,
+)
+momentum_config = dict(
+    policy='cyclic',
+    target_ratio=(0.85 / 0.95, 1),
+    cyclic_times=1,
+    step_ratio_up=0.4,
+)
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+# runtime settings
+total_epochs = 80
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/sec_secfpn_80e'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]

mmdet3d/datasets/__init__.py

@@ -8,8 +8,8 @@ from .loader import DistributedGroupSampler, GroupSampler, build_dataloader
 from .nuscenes_dataset import NuScenesDataset
 from .pipelines import (GlobalRotScale, IndoorFlipData, IndoorGlobalRotScale,
                         IndoorPointSample, IndoorPointsColorJitter,
-                        IndoorPointsColorNormalize, LoadAnnotations3D,
-                        LoadPointsFromFile, ObjectNoise, ObjectRangeFilter,
+                        LoadAnnotations3D, LoadPointsFromFile,
+                        NormalizePointsColor, ObjectNoise, ObjectRangeFilter,
                         ObjectSample, PointShuffle, PointsRangeFilter,
                         RandomFlip3D)
 from .scannet_dataset import ScanNetDataset
@@ -21,7 +21,7 @@ __all__ = [
     'CocoDataset', 'Kitti2DDataset', 'NuScenesDataset', 'ObjectSample',
     'RandomFlip3D', 'ObjectNoise', 'GlobalRotScale', 'PointShuffle',
     'ObjectRangeFilter', 'PointsRangeFilter', 'Collect3D',
-    'LoadPointsFromFile', 'IndoorPointsColorNormalize', 'IndoorPointSample',
+    'LoadPointsFromFile', 'NormalizePointsColor', 'IndoorPointSample',
     'LoadAnnotations3D', 'IndoorPointsColorJitter', 'IndoorGlobalRotScale',
     'IndoorFlipData', 'SUNRGBDDataset', 'ScanNetDataset', 'Custom3DDataset'
 ]

mmdet3d/datasets/pipelines/__init__.py

@@ -3,8 +3,8 @@ from .dbsampler import DataBaseSampler, MMDataBaseSampler
 from .formating import DefaultFormatBundle, DefaultFormatBundle3D
 from .indoor_augment import (IndoorFlipData, IndoorGlobalRotScale,
                              IndoorPointsColorJitter)
-from .indoor_loading import (IndoorPointsColorNormalize, LoadAnnotations3D,
-                             LoadPointsFromFile)
+from .indoor_loading import (LoadAnnotations3D, LoadPointsFromFile,
+                             NormalizePointsColor)
 from .indoor_sample import IndoorPointSample
 from .loading import LoadMultiViewImageFromFiles
 from .train_aug import (GlobalRotScale, ObjectNoise, ObjectRangeFilter,
@@ -17,6 +17,6 @@ __all__ = [
     'Compose', 'LoadMultiViewImageFromFiles', 'LoadPointsFromFile',
     'DefaultFormatBundle', 'DefaultFormatBundle3D', 'DataBaseSampler',
     'IndoorGlobalRotScale', 'IndoorPointsColorJitter', 'IndoorFlipData',
-    'MMDataBaseSampler', 'IndoorPointsColorNormalize', 'LoadAnnotations3D',
+    'MMDataBaseSampler', 'NormalizePointsColor', 'LoadAnnotations3D',
     'IndoorPointSample'
 ]

mmdet3d/datasets/pipelines/indoor_loading.py

@@ -6,8 +6,8 @@ from mmdet.datasets.pipelines import LoadAnnotations
 
 
 @PIPELINES.register_module()
-class IndoorPointsColorNormalize(object):
-    """Indoor points color normalize
+class NormalizePointsColor(object):
+    """Normalize color of points
 
     Normalize color of the points.
 
@@ -45,9 +45,16 @@ class LoadPointsFromFile(object):
         use_dim (list[int]): Which dimensions of the points to be used.
             Default: [0, 1, 2]. For KITTI dataset, set use_dim=4
             or use_dim=[0, 1, 2, 3] to use the intensity dimension
+        file_client_args (dict): Config dict of file clients, refer to
+            https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
+            for more details.
     """
 
-    def __init__(self, load_dim=6, use_dim=[0, 1, 2], shift_height=False):
+    def __init__(self,
+                 load_dim=6,
+                 use_dim=[0, 1, 2],
+                 shift_height=False,
+                 file_client_args=dict(backend='disk')):
         self.shift_height = shift_height
         if isinstance(use_dim, int):
             use_dim = list(range(use_dim))
@@ -56,8 +63,16 @@ class LoadPointsFromFile(object):
 
         self.load_dim = load_dim
         self.use_dim = use_dim
+        self.file_client_args = file_client_args.copy()
+        self.file_client = None
 
     def _load_points(self, pts_filename):
+        if self.file_client is None:
+            self.file_client = mmcv.FileClient(**self.file_client_args)
+        try:
+            pts_bytes = self.file_client.get(pts_filename)
+            points = np.frombuffer(pts_bytes, dtype=np.float32)
+        except ConnectionError:
             mmcv.check_file_exist(pts_filename)
             if pts_filename.endswith('.npy'):
                 points = np.load(pts_filename)
@@ -113,6 +128,9 @@ class LoadAnnotations3D(LoadAnnotations):
             Defaults to False.
         poly2mask (bool, optional): Whether to convert polygon annotations
             to bitmasks. Defaults to True.
+        file_client_args (dict): Config dict of file clients, refer to
+            https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
+            for more details.
     """
 
     def __init__(self,
@@ -124,8 +142,15 @@ class LoadAnnotations3D(LoadAnnotations):
                  with_label=False,
                  with_mask=False,
                  with_seg=False,
-                 poly2mask=True):
-        super().__init__(with_bbox, with_label, with_mask, with_seg, poly2mask)
+                 poly2mask=True,
+                 file_client_args=dict(backend='disk')):
+        super().__init__(
+            with_bbox,
+            with_label,
+            with_mask,
+            with_seg,
+            poly2mask,
+            file_client_args=file_client_args)
         self.with_bbox_3d = with_bbox_3d
         self.with_label_3d = with_label_3d
         self.with_mask_3d = with_mask_3d
@@ -142,16 +167,34 @@ class LoadAnnotations3D(LoadAnnotations):
 
     def _load_masks_3d(self, results):
         pts_instance_mask_path = results['ann_info']['pts_instance_mask_path']
+
+        if self.file_client is None:
+            self.file_client = mmcv.FileClient(**self.file_client_args)
+        try:
+            mask_bytes = self.file_client.get(pts_instance_mask_path)
+            pts_instance_mask = np.frombuffer(mask_bytes, dtype=np.int)
+        except ConnectionError:
             mmcv.check_file_exist(pts_instance_mask_path)
-        pts_instance_mask = np.fromfile(pts_instance_mask_path, dtype=np.long)
+            pts_instance_mask = np.fromfile(
+                pts_instance_mask_path, dtype=np.long)
+
         results['pts_instance_mask'] = pts_instance_mask
         results['pts_mask_fields'].append(results['pts_instance_mask'])
         return results
 
     def _load_semantic_seg_3d(self, results):
         pts_semantic_mask_path = results['ann_info']['pts_semantic_mask_path']
+
+        if self.file_client is None:
+            self.file_client = mmcv.FileClient(**self.file_client_args)
+        try:
+            mask_bytes = self.file_client.get(pts_semantic_mask_path)
+            pts_semantic_mask = np.frombuffer(mask_bytes, dtype=np.int)
+        except ConnectionError:
             mmcv.check_file_exist(pts_semantic_mask_path)
-        pts_semantic_mask = np.fromfile(pts_semantic_mask_path, dtype=np.long)
+            pts_semantic_mask = np.fromfile(
+                pts_semantic_mask_path, dtype=np.long)
+
         results['pts_semantic_mask'] = pts_semantic_mask
         results['pts_seg_fields'].append(results['pts_semantic_mask'])
         return results

mmdet3d/datasets/pipelines/loading.py

@@ -42,9 +42,40 @@ class LoadMultiViewImageFromFiles(object):
 
 @PIPELINES.register_module()
 class LoadPointsFromMultiSweeps(object):
+    """Load points from multiple sweeps
 
-    def __init__(self, sweeps_num=10):
+    This is usually used for nuScenes dataset to utilize previous sweeps.
+
+    Args:
+        sweeps_num (int): number of sweeps
+        load_dim (int): dimension number of the loaded points
+        file_client_args (dict): Config dict of file clients, refer to
+            https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
+            for more details.
+    """
+
+    def __init__(self,
+                 sweeps_num=10,
+                 load_dim=5,
+                 file_client_args=dict(backend='disk')):
+        self.load_dim = load_dim
         self.sweeps_num = sweeps_num
+        self.file_client_args = file_client_args.copy()
+        self.file_client = None
+
+    def _load_points(self, pts_filename):
+        if self.file_client is None:
+            self.file_client = mmcv.FileClient(**self.file_client_args)
+        try:
+            pts_bytes = self.file_client.get(pts_filename)
+            points = np.frombuffer(pts_bytes, dtype=np.float32)
+        except ConnectionError:
+            mmcv.check_file_exist(pts_filename)
+            if pts_filename.endswith('.npy'):
+                points = np.load(pts_filename)
+            else:
+                points = np.fromfile(pts_filename, dtype=np.float32)
+        return points
 
     def __call__(self, results):
         points = results['points']
@@ -56,9 +87,8 @@ class LoadPointsFromMultiSweeps(object):
         for idx, sweep in enumerate(results['sweeps']):
             if idx >= self.sweeps_num:
                 break
-            points_sweep = np.fromfile(
-                sweep['data_path'], dtype=np.float32,
-                count=-1).reshape([-1, 5])
+            points_sweep = self._load_points(sweep['data_path'])
+            points_sweep = np.copy(points_sweep).reshape(-1, self.load_dim)
             sweep_ts = sweep['timestamp'] / 1e6
             points_sweep[:, 3] /= 255
             points_sweep[:, :3] = points_sweep[:, :3] @ sweep[