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Add imvoxelnet model for vic3d late fusion
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configs/vic3d/late-fusion-image/imvoxelnet/trainval_config_i.py
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| dataset_type = 'KittiDataset' | ||
| data_root = '../../../../data/DAIR-V2X/cooperative-vehicle-infrastructure/infrastructure-side/' | ||
| class_names = ['Car'] | ||
| input_modality = dict(use_lidar=False, use_camera=True) | ||
| point_cloud_range = [0, -39.68, -3, 92.16, 39.68, 1] | ||
| voxel_size = [0.32, 0.32, 0.33] | ||
| length = int((point_cloud_range[3] - point_cloud_range[0]) / voxel_size[0]) | ||
| width = int((point_cloud_range[4] - point_cloud_range[1]) / voxel_size[1]) | ||
| height = int((point_cloud_range[5] - point_cloud_range[2]) / voxel_size[2]) | ||
| output_shape = [width, length, height] | ||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
| img_scale = (960, 540) | ||
| img_resize_scale = [(912, 513), (1008, 567)] | ||
|
|
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| work_dir = './work_dirs/vic3d_latefusion_inf_imvoxelnet' | ||
|
|
||
| model = dict( | ||
| type='ImVoxelNet', | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=50, | ||
| num_stages=4, | ||
| out_indices=(0, 1, 2, 3), | ||
| frozen_stages=1, | ||
| norm_cfg=dict(type='BN', requires_grad=False), | ||
| norm_eval=True, | ||
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), | ||
| style='pytorch'), | ||
| neck=dict( | ||
| type='FPN', | ||
| in_channels=[256, 512, 1024, 2048], | ||
| out_channels=64, | ||
| num_outs=4), | ||
| neck_3d=dict(type='OutdoorImVoxelNeck', in_channels=64, out_channels=256), | ||
| bbox_head=dict( | ||
| type='Anchor3DHead', | ||
| num_classes=1, | ||
| in_channels=256, | ||
| feat_channels=256, | ||
| use_direction_classifier=True, | ||
| anchor_generator=dict( | ||
| type='AlignedAnchor3DRangeGenerator', | ||
| ranges=[[0, -39.68, -1.78, 92.16, 39.68, -1.78]], | ||
| sizes=[[3.9, 1.6, 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)), | ||
| n_voxels=output_shape, | ||
| anchor_generator=dict( | ||
| type='AlignedAnchor3DRangeGenerator', | ||
| ranges=[[0, -39.68, -3.08, 92.16, 39.68, 0.76]], | ||
| rotations=[.0]), | ||
| 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=0.1, | ||
| min_bbox_size=0, | ||
| nms_pre=100, | ||
| max_num=50)) | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadAnnotations3D'), | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5), | ||
| dict( | ||
| type='Resize', | ||
| img_scale=img_resize_scale, | ||
| keep_ratio=True, | ||
| multiscale_mode='range'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range), | ||
| dict(type='DefaultFormatBundle3D', class_names=class_names), | ||
| dict(type='Collect3D', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d']) | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='Resize', img_scale=img_scale, keep_ratio=True), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict( | ||
| type='DefaultFormatBundle3D', | ||
| class_names=class_names, | ||
| with_label=False), | ||
| dict(type='Collect3D', keys=['img']) | ||
| ] | ||
|
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||
| data = dict( | ||
| samples_per_gpu=1, | ||
| workers_per_gpu=1, | ||
| train=dict( | ||
| type='RepeatDataset', | ||
| times=1, | ||
| dataset=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, | ||
| box_type_3d="Lidar", | ||
| test_mode=True)) | ||
|
|
||
| optimizer = dict( | ||
| type='AdamW', | ||
| lr=0.0001, | ||
| weight_decay=0.0001, | ||
| paramwise_cfg=dict( | ||
| custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)})) | ||
| optimizer_config = dict(grad_clip=dict(max_norm=35., norm_type=2)) | ||
| lr_config = dict(policy='step', step=[8, 11]) | ||
| total_epochs = 12 | ||
|
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| checkpoint_config = dict(interval=1, max_keep_ckpts=1) | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[dict(type='TextLoggerHook'), | ||
| dict(type='TensorboardLoggerHook')]) | ||
| evaluation = dict(interval=1) | ||
| dist_params = dict(backend='nccl') | ||
| find_unused_parameters = True # only 1 of 4 FPN outputs is used | ||
| log_level = 'INFO' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] |
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