# model settings
model = dict(
    type='VoteNet',
    backbone=dict(
        type='PointNet2SASSG',
        in_channels=4,
        num_points=(2048, 1024, 512, 256),
        radius=(0.2, 0.4, 0.8, 1.2),
        num_samples=(64, 32, 16, 16),
        sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
                     (128, 128, 256)),
        fp_channels=((256, 256), (256, 256)),
        norm_cfg=dict(type='BN2d'),
        pool_mod='max'),
    bbox_head=dict(
        type='VoteHead',
        num_classes=18,
        bbox_coder=dict(
            type='PartialBinBasedBBoxCoder',
            num_sizes=18,
            num_dir_bins=1,
            with_rot=False,
            mean_sizes=[[0.76966727, 0.8116021, 0.92573744],
                        [1.876858, 1.8425595, 1.1931566],
                        [0.61328, 0.6148609, 0.7182701],
                        [1.3955007, 1.5121545, 0.83443564],
                        [0.97949594, 1.0675149, 0.6329687],
                        [0.531663, 0.5955577, 1.7500148],
                        [0.9624706, 0.72462326, 1.1481868],
                        [0.83221924, 1.0490936, 1.6875663],
                        [0.21132214, 0.4206159, 0.5372846],
                        [1.4440073, 1.8970833, 0.26985747],
                        [1.0294262, 1.4040797, 0.87554324],
                        [1.3766412, 0.65521795, 1.6813129],
                        [0.6650819, 0.71111923, 1.298853],
                        [0.41999173, 0.37906948, 1.7513971],
                        [0.59359556, 0.5912492, 0.73919016],
                        [0.50867593, 0.50656086, 0.30136237],
                        [1.1511526, 1.0546296, 0.49706793],
                        [0.47535285, 0.49249494, 0.5802117]]),
        vote_moudule_cfg=dict(
            in_channels=256,
            vote_per_seed=1,
            gt_per_seed=3,
            conv_channels=(256, 256),
            conv_cfg=dict(type='Conv1d'),
            norm_cfg=dict(type='BN1d'),
            norm_feats=True,
            vote_loss=dict(
                type='ChamferDistance',
                mode='l1',
                reduction='none',
                loss_dst_weight=10.0)),
        vote_aggregation_cfg=dict(
            num_point=256,
            radius=0.3,
            num_sample=16,
            mlp_channels=[256, 128, 128, 128],
            use_xyz=True,
            normalize_xyz=True),
        feat_channels=(128, 128),
        conv_cfg=dict(type='Conv1d'),
        norm_cfg=dict(type='BN1d'),
        objectness_loss=dict(
            type='CrossEntropyLoss',
            class_weight=[0.2, 0.8],
            reduction='sum',
            loss_weight=5.0),
        center_loss=dict(
            type='ChamferDistance',
            mode='l2',
            reduction='sum',
            loss_src_weight=10.0,
            loss_dst_weight=10.0),
        dir_class_loss=dict(
            type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
        dir_res_loss=dict(
            type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
        size_class_loss=dict(
            type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
        size_res_loss=dict(
            type='SmoothL1Loss', reduction='sum', loss_weight=10.0 / 3.0),
        semantic_loss=dict(
            type='CrossEntropyLoss', reduction='sum', loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(pos_distance_thr=0.3, neg_distance_thr=0.6, sample_mod='vote')
test_cfg = dict(
    sample_mod='seed', nms_thr=0.25, score_thr=0.05, per_class_proposal=True)

# dataset settings
dataset_type = 'ScanNetDataset'
data_root = './data/scannet/'
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
               'bookshelf', 'picture', 'counter', 'desk', 'curtain',
               'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
               'garbagebin')
train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        shift_height=True,
        load_dim=6,
        use_dim=[0, 1, 2]),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=True,
        with_label_3d=True,
        with_mask_3d=True,
        with_seg_3d=True),
    dict(
        type='PointSegClassMapping',
        valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
                       36, 39)),
    dict(type='IndoorPointSample', num_points=40000),
    dict(type='IndoorFlipData', flip_ratio_yz=0.5, flip_ratio_xz=0.5),
    dict(
        type='IndoorGlobalRotScale',
        shift_height=True,
        rot_range=[-1 / 36, 1 / 36],
        scale_range=None),
    dict(type='DefaultFormatBundle3D', class_names=class_names),
    dict(
        type='Collect3D',
        keys=[
            'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
            'pts_instance_mask'
        ])
]
test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        shift_height=True,
        load_dim=6,
        use_dim=[0, 1, 2]),
    dict(type='IndoorPointSample', num_points=40000),
    dict(type='DefaultFormatBundle3D', class_names=class_names),
    dict(type='Collect3D', keys=['points'])
]

data = dict(
    samples_per_gpu=8,
    workers_per_gpu=4,
    train=dict(
        type='RepeatDataset',
        times=5,
        dataset=dict(
            type=dataset_type,
            data_root=data_root,
            ann_file=data_root + 'scannet_infos_train.pkl',
            pipeline=train_pipeline,
            filter_empty_gt=False,
            classes=class_names)),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=data_root + 'scannet_infos_val.pkl',
        pipeline=test_pipeline,
        classes=class_names,
        test_mode=True),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=data_root + 'scannet_infos_val.pkl',
        pipeline=test_pipeline,
        classes=class_names,
        test_mode=True))
# optimizer
lr = 0.008  # max learning rate
optimizer = dict(type='Adam', lr=lr)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[24, 32])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=10,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
# runtime settings
total_epochs = 36
dist_params = dict(backend='nccl')
log_level = 'INFO'
find_unused_parameters = True
work_dir = './work_dirs/votenet_scannet'
load_from = None
resume_from = None
workflow = [('train', 1)]