imvoxelnet_kitti.py

model = dict(
    type='ImVoxelNet',
    pretrained='torchvision://resnet50',
    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,
        style='pytorch'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=64,
        num_outs=4),
    neck_3d=dict(
        type='KittiImVoxelNeck',
        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='Anchor3DRangeGenerator',
            ranges=[[0, -39.68, -1.78, 69.12 - .32, 39.68 - .32, -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)),
    n_voxels=(216, 248, 12),
    voxel_size=(.32, .32, .32))
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)
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

dataset_type = 'KittiMultiViewDataset'
data_root = 'data/kitti/'
class_names = ['Car']
input_modality = dict(use_lidar=False, use_camera=True)
point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]

train_pipeline = [
    dict(type='LoadAnnotations3D'),
    dict(
        type='MultiViewPipeline',
        n_images=1,
        transforms=[
            dict(type='LoadImageFromFile'),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Resize',
                img_scale=[(1173, 352), (1387, 416)],
                keep_ratio=True,
                multiscale_mode='range'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32)]),
    dict(type='KittiRandomFlip'),
    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='KittiSetOrigin', 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='MultiViewPipeline',
        n_images=1,
        transforms=[
            dict(type='LoadImageFromFile'),
            dict(type='Resize', img_scale=(1280, 384), keep_ratio=True),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32)]),
    dict(type='KittiSetOrigin', point_cloud_range=point_cloud_range),
    dict(type='DefaultFormatBundle3D', class_names=class_names, with_label=False),
    dict(type='Collect3D', keys=['img'])]

data = dict(
    samples_per_gpu=4,
    workers_per_gpu=3,
    train=dict(
        type='RepeatDataset',
        times=3,
        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,
        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

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  # todo: fix number of FPN outputs
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]