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_base_ = [ |
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'../_base_/schedules/mmdet-schedule-1x.py', '../_base_/default_runtime.py' |
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] |
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|
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model = dict( |
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type='ImVoxelNet', |
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data_preprocessor=dict( |
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type='Det3DDataPreprocessor', |
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mean=[123.675, 116.28, 103.53], |
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std=[58.395, 57.12, 57.375], |
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bgr_to_rgb=True, |
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pad_size_divisor=32), |
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backbone=dict( |
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type='mmdet.ResNet', |
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depth=50, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type='BN', requires_grad=False), |
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norm_eval=True, |
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), |
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style='pytorch'), |
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neck=dict( |
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type='mmdet.FPN', |
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in_channels=[256, 512, 1024, 2048], |
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out_channels=64, |
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num_outs=4), |
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neck_3d=dict(type='OutdoorImVoxelNeck', in_channels=64, out_channels=256), |
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bbox_head=dict( |
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type='Anchor3DHead', |
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num_classes=1, |
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in_channels=256, |
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feat_channels=256, |
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use_direction_classifier=True, |
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anchor_generator=dict( |
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type='AlignedAnchor3DRangeGenerator', |
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ranges=[[-0.16, -39.68, -1.78, 68.96, 39.68, -1.78]], |
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sizes=[[3.9, 1.6, 1.56]], |
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rotations=[0, 1.57], |
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reshape_out=True), |
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diff_rad_by_sin=True, |
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bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'), |
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loss_cls=dict( |
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type='mmdet.FocalLoss', |
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use_sigmoid=True, |
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gamma=2.0, |
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alpha=0.25, |
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loss_weight=1.0), |
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loss_bbox=dict( |
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type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0), |
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loss_dir=dict( |
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type='mmdet.CrossEntropyLoss', use_sigmoid=False, |
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loss_weight=0.2)), |
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n_voxels=[216, 248, 12], |
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coord_type='LIDAR', |
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prior_generator=dict( |
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type='AlignedAnchor3DRangeGenerator', |
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ranges=[[-0.16, -39.68, -3.08, 68.96, 39.68, 0.76]], |
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rotations=[.0]), |
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train_cfg=dict( |
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assigner=dict( |
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type='Max3DIoUAssigner', |
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iou_calculator=dict(type='mmdet3d.BboxOverlapsNearest3D'), |
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pos_iou_thr=0.6, |
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neg_iou_thr=0.45, |
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min_pos_iou=0.45, |
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ignore_iof_thr=-1), |
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allowed_border=0, |
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pos_weight=-1, |
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debug=False), |
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test_cfg=dict( |
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use_rotate_nms=True, |
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nms_across_levels=False, |
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nms_thr=0.01, |
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score_thr=0.1, |
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min_bbox_size=0, |
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nms_pre=100, |
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max_num=50)) |
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|
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dataset_type = 'KittiDataset' |
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data_root = 'data/kitti/' |
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class_names = ['Car'] |
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input_modality = dict(use_lidar=False, use_camera=True) |
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point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1] |
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metainfo = dict(classes=class_names) |
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|
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backend_args = None |
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|
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train_pipeline = [ |
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dict(type='LoadAnnotations3D', backend_args=backend_args), |
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dict(type='LoadImageFromFileMono3D', backend_args=backend_args), |
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dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5), |
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dict( |
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type='RandomResize', scale=[(1173, 352), (1387, 416)], |
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keep_ratio=True), |
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dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range), |
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dict(type='Pack3DDetInputs', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d']) |
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] |
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test_pipeline = [ |
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dict(type='LoadImageFromFileMono3D', backend_args=backend_args), |
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dict(type='Resize', scale=(1280, 384), keep_ratio=True), |
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dict(type='Pack3DDetInputs', keys=['img']) |
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] |
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|
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train_dataloader = dict( |
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batch_size=4, |
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num_workers=4, |
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persistent_workers=True, |
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sampler=dict(type='DefaultSampler', shuffle=True), |
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dataset=dict( |
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type='RepeatDataset', |
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times=3, |
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dataset=dict( |
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type=dataset_type, |
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data_root=data_root, |
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ann_file='kitti_infos_train.pkl', |
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data_prefix=dict(img='training/image_2'), |
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pipeline=train_pipeline, |
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modality=input_modality, |
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test_mode=False, |
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metainfo=metainfo, |
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box_type_3d='LiDAR', |
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backend_args=backend_args))) |
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val_dataloader = dict( |
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batch_size=1, |
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num_workers=1, |
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persistent_workers=True, |
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drop_last=False, |
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sampler=dict(type='DefaultSampler', shuffle=False), |
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dataset=dict( |
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type=dataset_type, |
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data_root=data_root, |
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ann_file='kitti_infos_val.pkl', |
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data_prefix=dict(img='training/image_2'), |
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pipeline=test_pipeline, |
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modality=input_modality, |
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test_mode=True, |
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metainfo=metainfo, |
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box_type_3d='LiDAR', |
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backend_args=backend_args)) |
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test_dataloader = val_dataloader |
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|
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val_evaluator = dict( |
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type='KittiMetric', |
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ann_file=data_root + 'kitti_infos_val.pkl', |
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metric='bbox', |
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backend_args=backend_args) |
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test_evaluator = val_evaluator |
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|
|
|
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optim_wrapper = dict( |
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type='OptimWrapper', |
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optimizer=dict( |
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_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001), |
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paramwise_cfg=dict( |
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custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}), |
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clip_grad=dict(max_norm=35., norm_type=2)) |
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param_scheduler = [ |
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dict( |
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type='MultiStepLR', |
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begin=0, |
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end=12, |
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by_epoch=True, |
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milestones=[8, 11], |
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gamma=0.1) |
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] |
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|
|
|
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default_hooks = dict(checkpoint=dict(type='CheckpointHook', max_keep_ckpts=1)) |
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|
|
|
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find_unused_parameters = True |
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|
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vis_backends = [dict(type='LocalVisBackend')] |
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visualizer = dict( |
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type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer') |
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|
