moca_simipu_kitti.txt

2021-07-11 15:13:29,443 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.7 (default, May  7 2020, 21:25:33) [GCC 7.3.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: Tesla V100-SXM2-32GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 10.1, V10.1.243
GCC: gcc (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
PyTorch: 1.6.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.1 Product Build 20200208 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
  - CuDNN 7.6.3
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, 

TorchVision: 0.7.0
OpenCV: 4.5.2
MMCV: 1.3.4
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMDetection: 2.11.0
MMSegmentation: 0.13.0
MMDetection3D: 0.13.0+c81aa4f
------------------------------------------------------------

2021-07-11 15:13:33,801 - mmdet - INFO - Distributed training: True
2021-07-11 15:13:38,093 - mmdet - INFO - Config:
voxel_size = [0.05, 0.05, 0.1]
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
model = dict(
    type='DynamicMVXFasterRCNN',
    img_backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=False,
        style='pytorch'),
    img_neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    pts_voxel_layer=dict(
        max_num_points=-1,
        point_cloud_range=[0, -40, -3, 70.4, 40, 1],
        voxel_size=[0.05, 0.05, 0.1],
        max_voxels=(-1, -1)),
    pts_voxel_encoder=dict(
        type='DynamicVFE',
        in_channels=4,
        feat_channels=[64, 64],
        with_distance=False,
        voxel_size=[0.05, 0.05, 0.1],
        with_cluster_center=True,
        with_voxel_center=True,
        point_cloud_range=[0, -40, -3, 70.4, 40, 1],
        fusion_layer=dict(
            type='PointFusion',
            img_channels=256,
            pts_channels=64,
            mid_channels=128,
            out_channels=128,
            img_levels=[0, 1, 2, 3, 4],
            align_corners=False,
            activate_out=True,
            fuse_out=False)),
    pts_middle_encoder=dict(
        type='SparseEncoder',
        in_channels=128,
        sparse_shape=[41, 1600, 1408],
        order=('conv', 'norm', 'act')),
    pts_backbone=dict(
        type='SECOND',
        in_channels=256,
        layer_nums=[5, 5],
        layer_strides=[1, 2],
        out_channels=[128, 256]),
    pts_neck=dict(
        type='SECONDFPN',
        in_channels=[128, 256],
        upsample_strides=[1, 2],
        out_channels=[256, 256]),
    pts_bbox_head=dict(
        type='Anchor3DHead',
        num_classes=3,
        in_channels=512,
        feat_channels=512,
        use_direction_classifier=True,
        anchor_generator=dict(
            type='Anchor3DRangeGenerator',
            ranges=[[0, -40.0, -0.6, 70.4, 40.0, -0.6],
                    [0, -40.0, -0.6, 70.4, 40.0, -0.6],
                    [0, -40.0, -1.78, 70.4, 40.0, -1.78]],
            sizes=[[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]],
            rotations=[0, 1.57],
            reshape_out=False),
        assigner_per_size=True,
        diff_rad_by_sin=True,
        assign_per_class=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=0.1111111111111111, loss_weight=2.0),
        loss_dir=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
    train_cfg=dict(
        pts=dict(
            assigner=[
                dict(
                    type='MaxIoUAssigner',
                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
                    pos_iou_thr=0.35,
                    neg_iou_thr=0.2,
                    min_pos_iou=0.2,
                    ignore_iof_thr=-1),
                dict(
                    type='MaxIoUAssigner',
                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
                    pos_iou_thr=0.35,
                    neg_iou_thr=0.2,
                    min_pos_iou=0.2,
                    ignore_iof_thr=-1),
                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(
        pts=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)))
dataset_type = 'KittiDataset'
data_root = '/nfs/lizhenyu1/kitti_det/public_datalist_14/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
img_norm_cfg = dict(
    mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
db_sampler = dict(
    type='MMDataBaseSampler',
    data_root='/nfs/lizhenyu1/kitti_det/public_datalist_14/',
    info_path=
    '/nfs/lizhenyu1/kitti_det/public_datalist_14/kitti_dbinfos_train.pkl',
    rate=1.0,
    blending_type=None,
    depth_consistent=True,
    check_2D_collision=True,
    collision_thr=[0, 0.3, 0.5, 0.7],
    prepare=dict(
        filter_by_difficulty=[-1],
        filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
    classes=['Pedestrian', 'Cyclist', 'Car'],
    sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6))
input_modality = dict(use_lidar=True, use_camera=True)
train_pipeline = [
    dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
    dict(type='LoadImageFromFile'),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=True,
        with_label_3d=True,
        with_bbox=True,
        with_label=True),
    dict(
        type='ObjectSample',
        db_sampler=dict(
            type='MMDataBaseSampler',
            data_root='/nfs/lizhenyu1/kitti_det/public_datalist_14/',
            info_path=
            '/nfs/lizhenyu1/kitti_det/public_datalist_14/kitti_dbinfos_train.pkl',
            rate=1.0,
            blending_type=None,
            depth_consistent=True,
            check_2D_collision=True,
            collision_thr=[0, 0.3, 0.5, 0.7],
            prepare=dict(
                filter_by_difficulty=[-1],
                filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
            classes=['Pedestrian', 'Cyclist', 'Car'],
            sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6)),
        sample_2d=True),
    dict(
        type='Resize',
        img_scale=[(640, 192), (2560, 768)],
        multiscale_mode='range',
        keep_ratio=True),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-0.78539816, 0.78539816],
        scale_ratio_range=[0.95, 1.05],
        translation_std=[0.2, 0.2, 0.2]),
    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
    dict(
        type='PointsRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
    dict(
        type='ObjectRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
    dict(type='PointShuffle'),
    dict(
        type='Normalize',
        mean=[103.53, 116.28, 123.675],
        std=[1.0, 1.0, 1.0],
        to_rgb=False),
    dict(type='Pad', size_divisor=32),
    dict(
        type='DefaultFormatBundle3D',
        class_names=['Pedestrian', 'Cyclist', 'Car']),
    dict(
        type='Collect3D',
        keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
    dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug3D',
        img_scale=(1280, 384),
        pts_scale_ratio=1,
        flip=False,
        transforms=[
            dict(type='Resize', multiscale_mode='value', keep_ratio=True),
            dict(
                type='GlobalRotScaleTrans',
                rot_range=[0, 0],
                scale_ratio_range=[1.0, 1.0],
                translation_std=[0, 0, 0]),
            dict(type='RandomFlip3D'),
            dict(
                type='Normalize',
                mean=[103.53, 116.28, 123.675],
                std=[1.0, 1.0, 1.0],
                to_rgb=False),
            dict(type='Pad', size_divisor=32),
            dict(
                type='PointsRangeFilter',
                point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
            dict(
                type='DefaultFormatBundle3D',
                class_names=['Pedestrian', 'Cyclist', 'Car'],
                with_label=False),
            dict(type='Collect3D', keys=['points', 'img'])
        ])
]
data = dict(
    samples_per_gpu=4,
    workers_per_gpu=4,
    train=dict(
        type='RepeatDataset',
        times=2,
        dataset=dict(
            type='KittiDataset',
            data_root='/nfs/lizhenyu1/kitti_det/public_datalist_14/',
            ann_file=
            '/nfs/lizhenyu1/kitti_det/public_datalist_14/kitti_infos_train.pkl',
            split='training',
            pts_prefix='velodyne_reduced',
            pipeline=[
                dict(
                    type='LoadPointsFromFile',
                    coord_type='LIDAR',
                    load_dim=4,
                    use_dim=4),
                dict(type='LoadImageFromFile'),
                dict(
                    type='LoadAnnotations3D',
                    with_bbox_3d=True,
                    with_label_3d=True,
                    with_bbox=True,
                    with_label=True),
                dict(
                    type='ObjectSample',
                    db_sampler=dict(
                        type='MMDataBaseSampler',
                        data_root=
                        '/nfs/lizhenyu1/kitti_det/public_datalist_14/',
                        info_path=
                        '/nfs/lizhenyu1/kitti_det/public_datalist_14/kitti_dbinfos_train.pkl',
                        rate=1.0,
                        blending_type=None,
                        depth_consistent=True,
                        check_2D_collision=True,
                        collision_thr=[0, 0.3, 0.5, 0.7],
                        prepare=dict(
                            filter_by_difficulty=[-1],
                            filter_by_min_points=dict(
                                Car=5, Pedestrian=10, Cyclist=10)),
                        classes=['Pedestrian', 'Cyclist', 'Car'],
                        sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6)),
                    sample_2d=True),
                dict(
                    type='Resize',
                    img_scale=[(640, 192), (2560, 768)],
                    multiscale_mode='range',
                    keep_ratio=True),
                dict(
                    type='GlobalRotScaleTrans',
                    rot_range=[-0.78539816, 0.78539816],
                    scale_ratio_range=[0.95, 1.05],
                    translation_std=[0.2, 0.2, 0.2]),
                dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
                dict(
                    type='PointsRangeFilter',
                    point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
                dict(
                    type='ObjectRangeFilter',
                    point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
                dict(type='PointShuffle'),
                dict(
                    type='Normalize',
                    mean=[103.53, 116.28, 123.675],
                    std=[1.0, 1.0, 1.0],
                    to_rgb=False),
                dict(type='Pad', size_divisor=32),
                dict(
                    type='DefaultFormatBundle3D',
                    class_names=['Pedestrian', 'Cyclist', 'Car']),
                dict(
                    type='Collect3D',
                    keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
            ],
            modality=dict(use_lidar=True, use_camera=True),
            classes=['Pedestrian', 'Cyclist', 'Car'],
            test_mode=False)),
    val=dict(
        type='KittiDataset',
        data_root='/nfs/lizhenyu1/kitti_det/public_datalist_14/',
        ann_file=
        '/nfs/lizhenyu1/kitti_det/public_datalist_14/kitti_infos_val.pkl',
        split='training',
        pts_prefix='velodyne_reduced',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=4,
                use_dim=4),
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug3D',
                img_scale=(1280, 384),
                pts_scale_ratio=1,
                flip=False,
                transforms=[
                    dict(
                        type='Resize',
                        multiscale_mode='value',
                        keep_ratio=True),
                    dict(
                        type='GlobalRotScaleTrans',
                        rot_range=[0, 0],
                        scale_ratio_range=[1.0, 1.0],
                        translation_std=[0, 0, 0]),
                    dict(type='RandomFlip3D'),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[1.0, 1.0, 1.0],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(
                        type='PointsRangeFilter',
                        point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
                    dict(
                        type='DefaultFormatBundle3D',
                        class_names=['Pedestrian', 'Cyclist', 'Car'],
                        with_label=False),
                    dict(type='Collect3D', keys=['points', 'img'])
                ])
        ],
        modality=dict(use_lidar=True, use_camera=True),
        classes=['Pedestrian', 'Cyclist', 'Car'],
        test_mode=True),
    test=dict(
        type='KittiDataset',
        data_root='/nfs/lizhenyu1/kitti_det/public_datalist_14/',
        ann_file=
        '/nfs/lizhenyu1/kitti_det/public_datalist_14/kitti_infos_val.pkl',
        split='training',
        pts_prefix='velodyne_reduced',
        pipeline=[
            dict(
                type='LoadPointsFromFile',
                coord_type='LIDAR',
                load_dim=4,
                use_dim=4),
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug3D',
                img_scale=(1280, 384),
                pts_scale_ratio=1,
                flip=False,
                transforms=[
                    dict(
                        type='Resize',
                        multiscale_mode='value',
                        keep_ratio=True),
                    dict(
                        type='GlobalRotScaleTrans',
                        rot_range=[0, 0],
                        scale_ratio_range=[1.0, 1.0],
                        translation_std=[0, 0, 0]),
                    dict(type='RandomFlip3D'),
                    dict(
                        type='Normalize',
                        mean=[103.53, 116.28, 123.675],
                        std=[1.0, 1.0, 1.0],
                        to_rgb=False),
                    dict(type='Pad', size_divisor=32),
                    dict(
                        type='PointsRangeFilter',
                        point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
                    dict(
                        type='DefaultFormatBundle3D',
                        class_names=['Pedestrian', 'Cyclist', 'Car'],
                        with_label=False),
                    dict(type='Collect3D', keys=['points', 'img'])
                ])
        ],
        modality=dict(use_lidar=True, use_camera=True),
        classes=['Pedestrian', 'Cyclist', 'Car'],
        test_mode=True))
optimizer = dict(
    constructor='HybridOptimizerConstructor',
    pts=dict(
        type='AdamW',
        lr=0.003,
        betas=(0.95, 0.99),
        weight_decay=0.01,
        step_interval=1),
    img=dict(
        type='SGD',
        lr=0.005,
        momentum=0.9,
        weight_decay=0.0001,
        step_interval=1))
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=1000,
    warmup_ratio=0.1,
    min_lr_ratio=1e-05)
momentum_config = None
checkpoint_config = dict(interval=1)
log_config = dict(
    interval=50,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')])
evaluation = dict(interval=1, start=30)
total_epochs = 40
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = 'nfs/lzy/cross_intro_moco_pointCenterRotate/downstream/kitti_pretrain_ep50_rp4'
load_from = 'nfs/lzy/cross_intro_moco_pointCenterRotate/pretrain_fix_moco_bugs/epoch_50.pth'
resume_from = None
workflow = [('train', 1)]
gpu_ids = range(0, 8)

2021-07-11 15:13:38,093 - mmdet - INFO - Set random seed to 0, deterministic: False
2021-07-11 15:13:38,980 - mmdet - INFO - Model:
DynamicMVXFasterRCNN(
  (pts_voxel_layer): Voxelization(voxel_size=[0.05, 0.05, 0.1], point_cloud_range=[0, -40, -3, 70.4, 40, 1], max_num_points=-1, max_voxels=(-1, -1))
  (pts_voxel_encoder): DynamicVFE(
    (scatter): DynamicScatter(voxel_size=[0.05, 0.05, 0.1], point_cloud_range=[0, -40, -3, 70.4, 40, 1], average_points=True)
    (vfe_layers): ModuleList(
      (0): Sequential(
        (0): Linear(in_features=10, out_features=64, bias=False)
        (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (1): Sequential(
        (0): Linear(in_features=128, out_features=64, bias=False)
        (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
    )
    (vfe_scatter): DynamicScatter(voxel_size=[0.05, 0.05, 0.1], point_cloud_range=[0, -40, -3, 70.4, 40, 1], average_points=False)
    (cluster_scatter): DynamicScatter(voxel_size=[0.05, 0.05, 0.1], point_cloud_range=[0, -40, -3, 70.4, 40, 1], average_points=True)
    (fusion_layer): PointFusion(
      (lateral_convs): ModuleList(
        (0): ConvModule(
          (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (1): ConvModule(
          (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (2): ConvModule(
          (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (3): ConvModule(
          (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (4): ConvModule(
          (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
      )
      (img_transform): Sequential(
        (0): Linear(in_features=640, out_features=128, bias=True)
        (1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
      (pts_transform): Sequential(
        (0): Linear(in_features=64, out_features=128, bias=True)
        (1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (pts_middle_encoder): SparseEncoder(
    (conv_input): SparseSequential(
      (0): SubMConv3d()
      (1): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (encoder_layers): SparseSequential(
      (encoder_layer1): SparseSequential(
        (0): SparseSequential(
          (0): SubMConv3d()
          (1): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
      )
      (encoder_layer2): SparseSequential(
        (0): SparseSequential(
          (0): SparseConv3d()
          (1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (1): SparseSequential(
          (0): SubMConv3d()
          (1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (2): SparseSequential(
          (0): SubMConv3d()
          (1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
      )
      (encoder_layer3): SparseSequential(
        (0): SparseSequential(
          (0): SparseConv3d()
          (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (1): SparseSequential(
          (0): SubMConv3d()
          (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (2): SparseSequential(
          (0): SubMConv3d()
          (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
      )
      (encoder_layer4): SparseSequential(
        (0): SparseSequential(
          (0): SparseConv3d()
          (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (1): SparseSequential(
          (0): SubMConv3d()
          (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (2): SparseSequential(
          (0): SubMConv3d()
          (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
      )
    )
    (conv_out): SparseSequential(
      (0): SparseConv3d()
      (1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
  )
  (pts_backbone): SECOND(
    (blocks): ModuleList(
      (0): Sequential(
        (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (5): ReLU(inplace=True)
        (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (7): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (8): ReLU(inplace=True)
        (9): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (10): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (11): ReLU(inplace=True)
        (12): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (13): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (14): ReLU(inplace=True)
        (15): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (16): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (17): ReLU(inplace=True)
      )
      (1): Sequential(
        (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (5): ReLU(inplace=True)
        (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (7): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (8): ReLU(inplace=True)
        (9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (10): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (11): ReLU(inplace=True)
        (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (13): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (14): ReLU(inplace=True)
        (15): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (16): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (17): ReLU(inplace=True)
      )
    )
  )
  (pts_neck): SECONDFPN(
    (deblocks): ModuleList(
      (0): Sequential(
        (0): ConvTranspose2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (1): Sequential(
        (0): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2), bias=False)
        (1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
    )
  )
  (pts_bbox_head): Anchor3DHead(
    (loss_cls): FocalLoss()
    (loss_bbox): SmoothL1Loss()
    (loss_dir): CrossEntropyLoss()
    (conv_cls): Conv2d(512, 18, kernel_size=(1, 1), stride=(1, 1))
    (conv_reg): Conv2d(512, 42, kernel_size=(1, 1), stride=(1, 1))
    (conv_dir_cls): Conv2d(512, 12, kernel_size=(1, 1), stride=(1, 1))
  )
  (img_backbone): ResNet(
    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): ResLayer(
      (0): Bottleneck(
        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
    (layer2): ResLayer(
      (0): Bottleneck(
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (3): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
    (layer3): ResLayer(
      (0): Bottleneck(
        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (3): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (4): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (5): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
    (layer4): ResLayer(
      (0): Bottleneck(
        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): SyncBatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
  )
  (img_neck): FPN(
    (lateral_convs): ModuleList(
      (0): ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      )
      (1): ConvModule(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      )
      (2): ConvModule(
        (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
      )
      (3): ConvModule(
        (conv): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
      )
    )
    (fpn_convs): ModuleList(
      (0): ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
      (1): ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
      (2): ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
      (3): ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
    )
  )
)
2021-07-11 15:13:49,916 - mmdet - INFO - load checkpoint from nfs/lzy/cross_intro_moco_pointCenterRotate/pretrain_fix_moco_bugs/epoch_50.pth
2021-07-11 15:13:49,916 - mmdet - INFO - Use load_from_local loader
2021-07-11 15:13:51,664 - mmdet - WARNING - The model and loaded state dict do not match exactly

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pts_backbone.blocks.1.7.running_mean, pts_backbone.blocks.1.7.running_var, pts_backbone.blocks.1.9.weight, pts_backbone.blocks.1.10.weight, pts_backbone.blocks.1.10.bias, pts_backbone.blocks.1.10.running_mean, pts_backbone.blocks.1.10.running_var, pts_backbone.blocks.1.12.weight, pts_backbone.blocks.1.13.weight, pts_backbone.blocks.1.13.bias, pts_backbone.blocks.1.13.running_mean, pts_backbone.blocks.1.13.running_var, pts_backbone.blocks.1.15.weight, pts_backbone.blocks.1.16.weight, pts_backbone.blocks.1.16.bias, pts_backbone.blocks.1.16.running_mean, pts_backbone.blocks.1.16.running_var, pts_neck.deblocks.0.0.weight, pts_neck.deblocks.0.1.weight, pts_neck.deblocks.0.1.bias, pts_neck.deblocks.0.1.running_mean, pts_neck.deblocks.0.1.running_var, pts_neck.deblocks.1.0.weight, pts_neck.deblocks.1.1.weight, pts_neck.deblocks.1.1.bias, pts_neck.deblocks.1.1.running_mean, pts_neck.deblocks.1.1.running_var, pts_bbox_head.conv_cls.weight, pts_bbox_head.conv_cls.bias, pts_bbox_head.conv_reg.weight, pts_bbox_head.conv_reg.bias, pts_bbox_head.conv_dir_cls.weight, pts_bbox_head.conv_dir_cls.bias, img_neck.lateral_convs.0.conv.weight, img_neck.lateral_convs.0.conv.bias, img_neck.lateral_convs.1.conv.weight, img_neck.lateral_convs.1.conv.bias, img_neck.lateral_convs.2.conv.weight, img_neck.lateral_convs.2.conv.bias, img_neck.lateral_convs.3.conv.weight, img_neck.lateral_convs.3.conv.bias, img_neck.fpn_convs.0.conv.weight, img_neck.fpn_convs.0.conv.bias, img_neck.fpn_convs.1.conv.weight, img_neck.fpn_convs.1.conv.bias, img_neck.fpn_convs.2.conv.weight, img_neck.fpn_convs.2.conv.bias, img_neck.fpn_convs.3.conv.weight, img_neck.fpn_convs.3.conv.bias

2021-07-11 15:13:51,716 - mmdet - INFO - Start running, host: root@kitti-downstream-rp4, work_dir: /nfs/lizhenyu1/workspace/python_workspace/mmdetection3d/nfs/lzy/cross_intro_moco_pointCenterRotate/downstream/kitti_pretrain_ep50_rp4
2021-07-11 15:13:51,717 - mmdet - INFO - workflow: [('train', 1)], max: 40 epochs
2021-07-11 15:15:46,953 - mmdet - INFO - Epoch [1][50/232]	lr: 4.323e-04, eta: 5:53:54, time: 2.301, data_time: 0.258, memory: 12989, loss_cls: 1.0330, loss_bbox: 1.7601, loss_dir: 0.1456, loss: 2.9387, grad_norm: 15.9746
2021-07-11 15:17:25,812 - mmdet - INFO - Epoch [1][100/232]	lr: 5.673e-04, eta: 5:27:16, time: 1.978, data_time: 0.021, memory: 13220, loss_cls: 0.6284, loss_bbox: 1.3548, loss_dir: 0.1364, loss: 2.1196, grad_norm: 7.2180
2021-07-11 15:19:04,976 - mmdet - INFO - Epoch [1][150/232]	lr: 7.023e-04, eta: 5:17:35, time: 1.983, data_time: 0.020, memory: 13355, loss_cls: 0.5082, loss_bbox: 1.1257, loss_dir: 0.1302, loss: 1.7641, grad_norm: 7.5062
2021-07-11 15:20:44,297 - mmdet - INFO - Epoch [1][200/232]	lr: 8.373e-04, eta: 5:12:01, time: 1.986, data_time: 0.023, memory: 13355, loss_cls: 0.4517, loss_bbox: 1.0446, loss_dir: 0.1230, loss: 1.6193, grad_norm: 6.3970
2021-07-11 15:21:47,573 - mmdet - INFO - Saving checkpoint at 1 epochs
2021-07-11 15:23:39,968 - mmdet - INFO - Epoch [2][50/232]	lr: 1.057e-03, eta: 4:38:17, time: 2.219, data_time: 0.260, memory: 13355, loss_cls: 0.3549, loss_bbox: 0.8913, loss_dir: 0.1124, loss: 1.3587, grad_norm: 6.4741
2021-07-11 15:25:18,377 - mmdet - INFO - Epoch [2][100/232]	lr: 1.192e-03, eta: 4:39:16, time: 1.968, data_time: 0.021, memory: 13355, loss_cls: 0.3088, loss_bbox: 0.8166, loss_dir: 0.1065, loss: 1.2319, grad_norm: 6.0065
2021-07-11 15:26:57,382 - mmdet - INFO - Epoch [2][150/232]	lr: 1.327e-03, eta: 4:39:47, time: 1.980, data_time: 0.021, memory: 13355, loss_cls: 0.2912, loss_bbox: 0.7990, loss_dir: 0.1035, loss: 1.1937, grad_norm: 5.9348
2021-07-11 15:28:36,720 - mmdet - INFO - Epoch [2][200/232]	lr: 1.461e-03, eta: 4:39:55, time: 1.986, data_time: 0.022, memory: 13355, loss_cls: 0.2698, loss_bbox: 0.7659, loss_dir: 0.1024, loss: 1.1380, grad_norm: 4.9763
2021-07-11 15:29:40,138 - mmdet - INFO - Saving checkpoint at 2 epochs
2021-07-11 15:31:32,657 - mmdet - INFO - Epoch [3][50/232]	lr: 1.675e-03, eta: 4:24:38, time: 2.221, data_time: 0.255, memory: 13355, loss_cls: 0.2348, loss_bbox: 0.7271, loss_dir: 0.0945, loss: 1.0563, grad_norm: 5.4995
2021-07-11 15:33:11,308 - mmdet - INFO - Epoch [3][100/232]	lr: 1.809e-03, eta: 4:25:13, time: 1.973, data_time: 0.022, memory: 13355, loss_cls: 0.2203, loss_bbox: 0.7006, loss_dir: 0.0933, loss: 1.0142, grad_norm: 5.5415
2021-07-11 15:34:50,087 - mmdet - INFO - Epoch [3][150/232]	lr: 1.943e-03, eta: 4:25:27, time: 1.976, data_time: 0.020, memory: 13355, loss_cls: 0.2173, loss_bbox: 0.7084, loss_dir: 0.0929, loss: 1.0187, grad_norm: 5.0554
2021-07-11 15:36:28,722 - mmdet - INFO - Epoch [3][200/232]	lr: 2.077e-03, eta: 4:25:23, time: 1.972, data_time: 0.020, memory: 13355, loss_cls: 0.2103, loss_bbox: 0.6754, loss_dir: 0.0906, loss: 0.9763, grad_norm: 3.9328
2021-07-11 15:37:32,321 - mmdet - INFO - Saving checkpoint at 3 epochs
2021-07-11 15:39:24,167 - mmdet - INFO - Epoch [4][50/232]	lr: 2.280e-03, eta: 4:15:03, time: 2.213, data_time: 0.251, memory: 13355, loss_cls: 0.1943, loss_bbox: 0.6661, loss_dir: 0.0866, loss: 0.9470, grad_norm: 4.4528
2021-07-11 15:41:03,451 - mmdet - INFO - Epoch [4][100/232]	lr: 2.413e-03, eta: 4:15:16, time: 1.986, data_time: 0.021, memory: 13355, loss_cls: 0.1869, loss_bbox: 0.6484, loss_dir: 0.0856, loss: 0.9209, grad_norm: 3.5733
2021-07-11 15:42:42,159 - mmdet - INFO - Epoch [4][150/232]	lr: 2.546e-03, eta: 4:15:10, time: 1.974, data_time: 0.019, memory: 13355, loss_cls: 0.1853, loss_bbox: 0.6419, loss_dir: 0.0866, loss: 0.9137, grad_norm: 3.3656
2021-07-11 15:44:21,199 - mmdet - INFO - Epoch [4][200/232]	lr: 2.679e-03, eta: 4:14:57, time: 1.981, data_time: 0.021, memory: 13366, loss_cls: 0.1835, loss_bbox: 0.6254, loss_dir: 0.0849, loss: 0.8938, grad_norm: 3.2595
2021-07-11 15:45:24,675 - mmdet - INFO - Saving checkpoint at 4 epochs
2021-07-11 15:47:16,643 - mmdet - INFO - Epoch [5][50/232]	lr: 2.866e-03, eta: 4:06:55, time: 2.209, data_time: 0.257, memory: 13366, loss_cls: 0.1823, loss_bbox: 0.6144, loss_dir: 0.0795, loss: 0.8762, grad_norm: 3.3069
2021-07-11 15:48:54,887 - mmdet - INFO - Epoch [5][100/232]	lr: 2.927e-03, eta: 4:06:38, time: 1.965, data_time: 0.022, memory: 13366, loss_cls: 0.1782, loss_bbox: 0.6078, loss_dir: 0.0831, loss: 0.8691, grad_norm: 3.2023
2021-07-11 15:50:33,539 - mmdet - INFO - Epoch [5][150/232]	lr: 2.927e-03, eta: 4:06:16, time: 1.973, data_time: 0.019, memory: 13387, loss_cls: 0.1755, loss_bbox: 0.5777, loss_dir: 0.0822, loss: 0.8354, grad_norm: 2.8246
2021-07-11 15:52:12,291 - mmdet - INFO - Epoch [5][200/232]	lr: 2.927e-03, eta: 4:05:49, time: 1.975, data_time: 0.020, memory: 13387, loss_cls: 0.1702, loss_bbox: 0.5745, loss_dir: 0.0774, loss: 0.8221, grad_norm: 2.3913
2021-07-11 15:53:15,851 - mmdet - INFO - Saving checkpoint at 5 epochs
2021-07-11 15:55:08,163 - mmdet - INFO - Epoch [6][50/232]	lr: 2.886e-03, eta: 3:59:11, time: 2.218, data_time: 0.256, memory: 13387, loss_cls: 0.1642, loss_bbox: 0.5479, loss_dir: 0.0726, loss: 0.7846, grad_norm: 2.1436
2021-07-11 15:56:46,892 - mmdet - INFO - Epoch [6][100/232]	lr: 2.886e-03, eta: 3:58:44, time: 1.974, data_time: 0.021, memory: 13387, loss_cls: 0.1626, loss_bbox: 0.5568, loss_dir: 0.0694, loss: 0.7889, grad_norm: 2.2527
2021-07-11 15:58:25,265 - mmdet - INFO - Epoch [6][150/232]	lr: 2.886e-03, eta: 3:58:10, time: 1.968, data_time: 0.020, memory: 13387, loss_cls: 0.1571, loss_bbox: 0.5365, loss_dir: 0.0710, loss: 0.7645, grad_norm: 2.3117
2021-07-11 16:00:04,072 - mmdet - INFO - Epoch [6][200/232]	lr: 2.886e-03, eta: 3:57:34, time: 1.976, data_time: 0.021, memory: 13387, loss_cls: 0.1562, loss_bbox: 0.5312, loss_dir: 0.0638, loss: 0.7513, grad_norm: 1.8886
2021-07-11 16:01:07,843 - mmdet - INFO - Saving checkpoint at 6 epochs
2021-07-11 16:03:00,265 - mmdet - INFO - Epoch [7][50/232]	lr: 2.837e-03, eta: 3:51:47, time: 2.218, data_time: 0.256, memory: 13387, loss_cls: 0.1515, loss_bbox: 0.5221, loss_dir: 0.0628, loss: 0.7364, grad_norm: 1.8673
2021-07-11 16:04:39,239 - mmdet - INFO - Epoch [7][100/232]	lr: 2.837e-03, eta: 3:51:12, time: 1.980, data_time: 0.020, memory: 13387, loss_cls: 0.1479, loss_bbox: 0.5151, loss_dir: 0.0611, loss: 0.7242, grad_norm: 1.6473
2021-07-11 16:06:18,432 - mmdet - INFO - Epoch [7][150/232]	lr: 2.837e-03, eta: 3:50:34, time: 1.984, data_time: 0.020, memory: 13387, loss_cls: 0.1462, loss_bbox: 0.5129, loss_dir: 0.0609, loss: 0.7200, grad_norm: 1.6338
2021-07-11 16:07:57,993 - mmdet - INFO - Epoch [7][200/232]	lr: 2.837e-03, eta: 3:49:53, time: 1.991, data_time: 0.021, memory: 13387, loss_cls: 0.1475, loss_bbox: 0.5063, loss_dir: 0.0582, loss: 0.7120, grad_norm: 1.4538
2021-07-11 16:09:01,910 - mmdet - INFO - Saving checkpoint at 7 epochs
2021-07-11 16:10:55,268 - mmdet - INFO - Epoch [8][50/232]	lr: 2.779e-03, eta: 3:44:47, time: 2.240, data_time: 0.229, memory: 13387, loss_cls: 0.1461, loss_bbox: 0.5041, loss_dir: 0.0576, loss: 0.7079, grad_norm: 1.4875
2021-07-11 16:12:34,257 - mmdet - INFO - Epoch [8][100/232]	lr: 2.779e-03, eta: 3:44:03, time: 1.980, data_time: 0.021, memory: 13387, loss_cls: 0.1374, loss_bbox: 0.4963, loss_dir: 0.0544, loss: 0.6881, grad_norm: 1.5881
2021-07-11 16:14:14,353 - mmdet - INFO - Epoch [8][150/232]	lr: 2.779e-03, eta: 3:43:21, time: 2.002, data_time: 0.020, memory: 13387, loss_cls: 0.1367, loss_bbox: 0.4943, loss_dir: 0.0589, loss: 0.6898, grad_norm: 1.5008
2021-07-11 16:15:53,892 - mmdet - INFO - Epoch [8][200/232]	lr: 2.779e-03, eta: 3:42:34, time: 1.991, data_time: 0.023, memory: 13387, loss_cls: 0.1358, loss_bbox: 0.4719, loss_dir: 0.0539, loss: 0.6615, grad_norm: 1.3218
2021-07-11 16:16:57,578 - mmdet - INFO - Saving checkpoint at 8 epochs
2021-07-11 16:18:50,348 - mmdet - INFO - Epoch [9][50/232]	lr: 2.714e-03, eta: 3:37:49, time: 2.225, data_time: 0.254, memory: 13387, loss_cls: 0.1349, loss_bbox: 0.4754, loss_dir: 0.0519, loss: 0.6621, grad_norm: 1.5146
2021-07-11 16:20:29,514 - mmdet - INFO - Epoch [9][100/232]	lr: 2.714e-03, eta: 3:37:00, time: 1.983, data_time: 0.021, memory: 13387, loss_cls: 0.1296, loss_bbox: 0.4787, loss_dir: 0.0525, loss: 0.6609, grad_norm: 1.4022
2021-07-11 16:22:08,776 - mmdet - INFO - Epoch [9][150/232]	lr: 2.714e-03, eta: 3:36:09, time: 1.985, data_time: 0.020, memory: 13387, loss_cls: 0.1313, loss_bbox: 0.4694, loss_dir: 0.0541, loss: 0.6548, grad_norm: 1.5006
2021-07-11 16:23:48,036 - mmdet - INFO - Epoch [9][200/232]	lr: 2.714e-03, eta: 3:35:15, time: 1.986, data_time: 0.022, memory: 13387, loss_cls: 0.1305, loss_bbox: 0.4612, loss_dir: 0.0518, loss: 0.6435, grad_norm: 1.2324
2021-07-11 16:24:51,503 - mmdet - INFO - Saving checkpoint at 9 epochs
2021-07-11 16:26:44,644 - mmdet - INFO - Epoch [10][50/232]	lr: 2.641e-03, eta: 3:30:52, time: 2.233, data_time: 0.263, memory: 13387, loss_cls: 0.1274, loss_bbox: 0.4594, loss_dir: 0.0491, loss: 0.6360, grad_norm: 1.2031
2021-07-11 16:28:23,209 - mmdet - INFO - Epoch [10][100/232]	lr: 2.641e-03, eta: 3:29:56, time: 1.971, data_time: 0.020, memory: 13387, loss_cls: 0.1239, loss_bbox: 0.4595, loss_dir: 0.0493, loss: 0.6327, grad_norm: 1.2149
2021-07-11 16:30:02,478 - mmdet - INFO - Epoch [10][150/232]	lr: 2.641e-03, eta: 3:29:00, time: 1.985, data_time: 0.021, memory: 13387, loss_cls: 0.1224, loss_bbox: 0.4552, loss_dir: 0.0500, loss: 0.6277, grad_norm: 1.2579
2021-07-11 16:31:42,666 - mmdet - INFO - Epoch [10][200/232]	lr: 2.641e-03, eta: 3:28:05, time: 2.004, data_time: 0.022, memory: 13387, loss_cls: 0.1267, loss_bbox: 0.4486, loss_dir: 0.0483, loss: 0.6236, grad_norm: 1.1071
2021-07-11 16:32:46,667 - mmdet - INFO - Saving checkpoint at 10 epochs
2021-07-11 16:34:39,416 - mmdet - INFO - Epoch [11][50/232]	lr: 2.561e-03, eta: 3:23:57, time: 2.231, data_time: 0.254, memory: 13387, loss_cls: 0.1253, loss_bbox: 0.4351, loss_dir: 0.0452, loss: 0.6055, grad_norm: 1.1472
2021-07-11 16:36:18,667 - mmdet - INFO - Epoch [11][100/232]	lr: 2.561e-03, eta: 3:22:59, time: 1.985, data_time: 0.019, memory: 13387, loss_cls: 0.1179, loss_bbox: 0.4321, loss_dir: 0.0445, loss: 0.5944, grad_norm: 1.1689
2021-07-11 16:37:58,395 - mmdet - INFO - Epoch [11][150/232]	lr: 2.561e-03, eta: 3:22:00, time: 1.994, data_time: 0.020, memory: 13387, loss_cls: 0.1161, loss_bbox: 0.4297, loss_dir: 0.0460, loss: 0.5917, grad_norm: 1.1279
2021-07-11 16:39:37,908 - mmdet - INFO - Epoch [11][200/232]	lr: 2.561e-03, eta: 3:20:59, time: 1.990, data_time: 0.021, memory: 13387, loss_cls: 0.1196, loss_bbox: 0.4342, loss_dir: 0.0452, loss: 0.5990, grad_norm: 1.0708
2021-07-11 16:40:41,134 - mmdet - INFO - Saving checkpoint at 11 epochs
2021-07-11 16:42:33,835 - mmdet - INFO - Epoch [12][50/232]	lr: 2.474e-03, eta: 3:17:03, time: 2.228, data_time: 0.241, memory: 13387, loss_cls: 0.1181, loss_bbox: 0.4218, loss_dir: 0.0430, loss: 0.5829, grad_norm: 1.0978
2021-07-11 16:44:13,422 - mmdet - INFO - Epoch [12][100/232]	lr: 2.474e-03, eta: 3:16:02, time: 1.992, data_time: 0.020, memory: 13387, loss_cls: 0.1161, loss_bbox: 0.4267, loss_dir: 0.0430, loss: 0.5858, grad_norm: 1.1200
2021-07-11 16:45:52,246 - mmdet - INFO - Epoch [12][150/232]	lr: 2.474e-03, eta: 3:14:58, time: 1.976, data_time: 0.020, memory: 13387, loss_cls: 0.1171, loss_bbox: 0.4318, loss_dir: 0.0445, loss: 0.5934, grad_norm: 1.1773
2021-07-11 16:47:31,292 - mmdet - INFO - Epoch [12][200/232]	lr: 2.474e-03, eta: 3:13:53, time: 1.981, data_time: 0.020, memory: 13387, loss_cls: 0.1158, loss_bbox: 0.4135, loss_dir: 0.0421, loss: 0.5714, grad_norm: 1.0773
2021-07-11 16:48:34,868 - mmdet - INFO - Saving checkpoint at 12 epochs
2021-07-11 16:50:28,074 - mmdet - INFO - Epoch [13][50/232]	lr: 2.382e-03, eta: 3:10:09, time: 2.235, data_time: 0.254, memory: 13387, loss_cls: 0.1119, loss_bbox: 0.4056, loss_dir: 0.0410, loss: 0.5584, grad_norm: 1.0298
2021-07-11 16:52:06,853 - mmdet - INFO - Epoch [13][100/232]	lr: 2.382e-03, eta: 3:09:03, time: 1.976, data_time: 0.019, memory: 13387, loss_cls: 0.1094, loss_bbox: 0.4064, loss_dir: 0.0405, loss: 0.5563, grad_norm: 1.0687
2021-07-11 16:53:46,137 - mmdet - INFO - Epoch [13][150/232]	lr: 2.382e-03, eta: 3:07:58, time: 1.985, data_time: 0.019, memory: 13387, loss_cls: 0.1113, loss_bbox: 0.4065, loss_dir: 0.0436, loss: 0.5614, grad_norm: 1.1020
2021-07-11 16:55:25,794 - mmdet - INFO - Epoch [13][200/232]	lr: 2.382e-03, eta: 3:06:51, time: 1.993, data_time: 0.021, memory: 13387, loss_cls: 0.1127, loss_bbox: 0.4010, loss_dir: 0.0396, loss: 0.5533, grad_norm: 0.9998
2021-07-11 16:56:29,695 - mmdet - INFO - Saving checkpoint at 13 epochs
2021-07-11 16:58:22,939 - mmdet - INFO - Epoch [14][50/232]	lr: 2.284e-03, eta: 3:03:16, time: 2.235, data_time: 0.262, memory: 13387, loss_cls: 0.1096, loss_bbox: 0.3905, loss_dir: 0.0387, loss: 0.5389, grad_norm: 1.0710
2021-07-11 17:00:02,506 - mmdet - INFO - Epoch [14][100/232]	lr: 2.284e-03, eta: 3:02:09, time: 1.991, data_time: 0.022, memory: 13387, loss_cls: 0.1085, loss_bbox: 0.3981, loss_dir: 0.0389, loss: 0.5455, grad_norm: 1.0234
2021-07-11 17:01:42,353 - mmdet - INFO - Epoch [14][150/232]	lr: 2.284e-03, eta: 3:01:02, time: 1.997, data_time: 0.019, memory: 13387, loss_cls: 0.1075, loss_bbox: 0.3992, loss_dir: 0.0390, loss: 0.5457, grad_norm: 0.9754
2021-07-11 17:03:21,397 - mmdet - INFO - Epoch [14][200/232]	lr: 2.284e-03, eta: 2:59:53, time: 1.981, data_time: 0.021, memory: 13387, loss_cls: 0.1097, loss_bbox: 0.3925, loss_dir: 0.0383, loss: 0.5404, grad_norm: 0.9678
2021-07-11 17:04:24,985 - mmdet - INFO - Saving checkpoint at 14 epochs
2021-07-11 17:06:17,836 - mmdet - INFO - Epoch [15][50/232]	lr: 2.181e-03, eta: 2:56:25, time: 2.232, data_time: 0.249, memory: 13387, loss_cls: 0.1058, loss_bbox: 0.3838, loss_dir: 0.0376, loss: 0.5271, grad_norm: 0.9435
2021-07-11 17:07:56,808 - mmdet - INFO - Epoch [15][100/232]	lr: 2.181e-03, eta: 2:55:15, time: 1.979, data_time: 0.020, memory: 13387, loss_cls: 0.1040, loss_bbox: 0.3893, loss_dir: 0.0358, loss: 0.5291, grad_norm: 0.9500
2021-07-11 17:09:36,459 - mmdet - INFO - Epoch [15][150/232]	lr: 2.181e-03, eta: 2:54:05, time: 1.993, data_time: 0.018, memory: 13387, loss_cls: 0.1038, loss_bbox: 0.3830, loss_dir: 0.0377, loss: 0.5245, grad_norm: 1.0048
2021-07-11 17:11:15,746 - mmdet - INFO - Epoch [15][200/232]	lr: 2.181e-03, eta: 2:52:54, time: 1.986, data_time: 0.021, memory: 13387, loss_cls: 0.1072, loss_bbox: 0.3787, loss_dir: 0.0369, loss: 0.5227, grad_norm: 0.9848
2021-07-11 17:12:20,333 - mmdet - INFO - Saving checkpoint at 15 epochs
2021-07-11 17:14:12,965 - mmdet - INFO - Epoch [16][50/232]	lr: 2.074e-03, eta: 2:49:32, time: 2.225, data_time: 0.255, memory: 13387, loss_cls: 0.1055, loss_bbox: 0.3760, loss_dir: 0.0363, loss: 0.5178, grad_norm: 0.9393
2021-07-11 17:17:33,749 - mmdet - INFO - Epoch [16][100/232]	lr: 2.074e-03, eta: 2:51:02, time: 4.015, data_time: 0.022, memory: 13387, loss_cls: 0.1042, loss_bbox: 0.3814, loss_dir: 0.0361, loss: 0.5217, grad_norm: 1.0254
2021-07-11 17:21:58,973 - mmdet - INFO - Epoch [16][150/232]	lr: 2.074e-03, eta: 2:54:05, time: 5.305, data_time: 0.027, memory: 13387, loss_cls: 0.1034, loss_bbox: 0.3746, loss_dir: 0.0356, loss: 0.5136, grad_norm: 0.9405
2021-07-11 17:24:43,249 - mmdet - INFO - Epoch [16][200/232]	lr: 2.074e-03, eta: 2:54:22, time: 3.285, data_time: 0.024, memory: 13387, loss_cls: 0.1026, loss_bbox: 0.3602, loss_dir: 0.0341, loss: 0.4970, grad_norm: 0.9423
2021-07-11 17:25:47,254 - mmdet - INFO - Saving checkpoint at 16 epochs
2021-07-11 17:27:40,473 - mmdet - INFO - Epoch [17][50/232]	lr: 1.964e-03, eta: 2:50:48, time: 2.238, data_time: 0.256, memory: 13387, loss_cls: 0.1007, loss_bbox: 0.3549, loss_dir: 0.0325, loss: 0.4881, grad_norm: 0.9208
2021-07-11 17:29:19,227 - mmdet - INFO - Epoch [17][100/232]	lr: 1.964e-03, eta: 2:49:24, time: 1.975, data_time: 0.022, memory: 13387, loss_cls: 0.1005, loss_bbox: 0.3595, loss_dir: 0.0320, loss: 0.4920, grad_norm: 0.9495
2021-07-11 17:30:59,041 - mmdet - INFO - Epoch [17][150/232]	lr: 1.964e-03, eta: 2:48:00, time: 1.996, data_time: 0.022, memory: 13387, loss_cls: 0.1011, loss_bbox: 0.3625, loss_dir: 0.0343, loss: 0.4980, grad_norm: 0.9710
2021-07-11 17:32:38,494 - mmdet - INFO - Epoch [17][200/232]	lr: 1.964e-03, eta: 2:46:36, time: 1.989, data_time: 0.022, memory: 13387, loss_cls: 0.1003, loss_bbox: 0.3617, loss_dir: 0.0335, loss: 0.4955, grad_norm: 0.9530
2021-07-11 17:33:42,022 - mmdet - INFO - Saving checkpoint at 17 epochs
2021-07-11 17:35:34,991 - mmdet - INFO - Epoch [18][50/232]	lr: 1.850e-03, eta: 2:43:09, time: 2.232, data_time: 0.264, memory: 13387, loss_cls: 0.0970, loss_bbox: 0.3457, loss_dir: 0.0312, loss: 0.4739, grad_norm: 0.9203
2021-07-11 17:37:14,027 - mmdet - INFO - Epoch [18][100/232]	lr: 1.850e-03, eta: 2:41:45, time: 1.981, data_time: 0.021, memory: 13387, loss_cls: 0.0952, loss_bbox: 0.3499, loss_dir: 0.0317, loss: 0.4767, grad_norm: 0.9311
2021-07-11 17:38:53,209 - mmdet - INFO - Epoch [18][150/232]	lr: 1.850e-03, eta: 2:40:20, time: 1.984, data_time: 0.020, memory: 13387, loss_cls: 0.0974, loss_bbox: 0.3495, loss_dir: 0.0328, loss: 0.4796, grad_norm: 0.9583
2021-07-11 17:40:32,792 - mmdet - INFO - Epoch [18][200/232]	lr: 1.850e-03, eta: 2:38:56, time: 1.992, data_time: 0.021, memory: 13387, loss_cls: 0.0989, loss_bbox: 0.3474, loss_dir: 0.0326, loss: 0.4789, grad_norm: 0.8884
2021-07-11 17:41:36,523 - mmdet - INFO - Saving checkpoint at 18 epochs
2021-07-11 17:43:29,479 - mmdet - INFO - Epoch [19][50/232]	lr: 1.735e-03, eta: 2:35:35, time: 2.228, data_time: 0.253, memory: 13387, loss_cls: 0.0956, loss_bbox: 0.3403, loss_dir: 0.0309, loss: 0.4669, grad_norm: 0.9316
2021-07-11 17:45:08,752 - mmdet - INFO - Epoch [19][100/232]	lr: 1.735e-03, eta: 2:34:11, time: 1.986, data_time: 0.021, memory: 13387, loss_cls: 0.0956, loss_bbox: 0.3512, loss_dir: 0.0309, loss: 0.4777, grad_norm: 0.9507
2021-07-11 17:46:48,162 - mmdet - INFO - Epoch [19][150/232]	lr: 1.735e-03, eta: 2:32:46, time: 1.988, data_time: 0.022, memory: 13387, loss_cls: 0.0947, loss_bbox: 0.3468, loss_dir: 0.0313, loss: 0.4728, grad_norm: 0.8807
2021-07-11 17:48:27,990 - mmdet - INFO - Epoch [19][200/232]	lr: 1.735e-03, eta: 2:31:22, time: 1.996, data_time: 0.021, memory: 13387, loss_cls: 0.0972, loss_bbox: 0.3409, loss_dir: 0.0307, loss: 0.4688, grad_norm: 0.9698
2021-07-11 17:49:31,942 - mmdet - INFO - Saving checkpoint at 19 epochs
2021-07-11 17:51:24,818 - mmdet - INFO - Epoch [20][50/232]	lr: 1.618e-03, eta: 2:28:06, time: 2.229, data_time: 0.225, memory: 13387, loss_cls: 0.0951, loss_bbox: 0.3307, loss_dir: 0.0303, loss: 0.4562, grad_norm: 0.9795
2021-07-11 17:53:03,825 - mmdet - INFO - Epoch [20][100/232]	lr: 1.618e-03, eta: 2:26:41, time: 1.980, data_time: 0.021, memory: 13387, loss_cls: 0.0938, loss_bbox: 0.3386, loss_dir: 0.0308, loss: 0.4632, grad_norm: 0.8269
2021-07-11 17:54:43,345 - mmdet - INFO - Epoch [20][150/232]	lr: 1.618e-03, eta: 2:25:17, time: 1.991, data_time: 0.020, memory: 13387, loss_cls: 0.0908, loss_bbox: 0.3327, loss_dir: 0.0297, loss: 0.4531, grad_norm: 0.9651
2021-07-11 17:56:22,976 - mmdet - INFO - Epoch [20][200/232]	lr: 1.618e-03, eta: 2:23:52, time: 1.993, data_time: 0.022, memory: 13387, loss_cls: 0.0926, loss_bbox: 0.3258, loss_dir: 0.0303, loss: 0.4487, grad_norm: 0.8369
2021-07-11 17:57:26,855 - mmdet - INFO - Saving checkpoint at 20 epochs
2021-07-11 17:59:19,548 - mmdet - INFO - Epoch [21][50/232]	lr: 1.500e-03, eta: 2:20:41, time: 2.227, data_time: 0.244, memory: 13387, loss_cls: 0.0919, loss_bbox: 0.3240, loss_dir: 0.0276, loss: 0.4434, grad_norm: 0.9192
2021-07-11 18:00:59,067 - mmdet - INFO - Epoch [21][100/232]	lr: 1.500e-03, eta: 2:19:16, time: 1.991, data_time: 0.020, memory: 13387, loss_cls: 0.0903, loss_bbox: 0.3229, loss_dir: 0.0284, loss: 0.4415, grad_norm: 0.9065
2021-07-11 18:02:38,470 - mmdet - INFO - Epoch [21][150/232]	lr: 1.500e-03, eta: 2:17:51, time: 1.988, data_time: 0.019, memory: 13387, loss_cls: 0.0906, loss_bbox: 0.3282, loss_dir: 0.0282, loss: 0.4470, grad_norm: 0.9147
2021-07-11 18:04:17,817 - mmdet - INFO - Epoch [21][200/232]	lr: 1.500e-03, eta: 2:16:26, time: 1.987, data_time: 0.019, memory: 13387, loss_cls: 0.0901, loss_bbox: 0.3181, loss_dir: 0.0291, loss: 0.4372, grad_norm: 0.8395
2021-07-11 18:05:21,696 - mmdet - INFO - Saving checkpoint at 21 epochs
2021-07-11 18:07:13,688 - mmdet - INFO - Epoch [22][50/232]	lr: 1.382e-03, eta: 2:13:19, time: 2.211, data_time: 0.248, memory: 13387, loss_cls: 0.0884, loss_bbox: 0.3115, loss_dir: 0.0266, loss: 0.4265, grad_norm: 0.9200
2021-07-11 18:08:52,793 - mmdet - INFO - Epoch [22][100/232]	lr: 1.382e-03, eta: 2:11:53, time: 1.982, data_time: 0.020, memory: 13387, loss_cls: 0.0891, loss_bbox: 0.3109, loss_dir: 0.0258, loss: 0.4258, grad_norm: 0.9239
2021-07-11 18:10:32,771 - mmdet - INFO - Epoch [22][150/232]	lr: 1.382e-03, eta: 2:10:28, time: 2.000, data_time: 0.018, memory: 13387, loss_cls: 0.0871, loss_bbox: 0.3243, loss_dir: 0.0271, loss: 0.4384, grad_norm: 0.9566
2021-07-11 18:12:12,735 - mmdet - INFO - Epoch [22][200/232]	lr: 1.382e-03, eta: 2:09:03, time: 1.999, data_time: 0.020, memory: 13387, loss_cls: 0.0892, loss_bbox: 0.3150, loss_dir: 0.0279, loss: 0.4321, grad_norm: 0.9151
2021-07-11 18:13:16,608 - mmdet - INFO - Saving checkpoint at 22 epochs
2021-07-11 18:15:09,444 - mmdet - INFO - Epoch [23][50/232]	lr: 1.265e-03, eta: 2:06:01, time: 2.233, data_time: 0.251, memory: 13387, loss_cls: 0.0881, loss_bbox: 0.3039, loss_dir: 0.0259, loss: 0.4179, grad_norm: 0.8934
2021-07-11 18:16:48,600 - mmdet - INFO - Epoch [23][100/232]	lr: 1.265e-03, eta: 2:04:35, time: 1.983, data_time: 0.020, memory: 13387, loss_cls: 0.0860, loss_bbox: 0.3120, loss_dir: 0.0262, loss: 0.4242, grad_norm: 0.9289
2021-07-11 18:18:27,563 - mmdet - INFO - Epoch [23][150/232]	lr: 1.265e-03, eta: 2:03:09, time: 1.979, data_time: 0.019, memory: 13387, loss_cls: 0.0851, loss_bbox: 0.3133, loss_dir: 0.0279, loss: 0.4263, grad_norm: 0.8687
2021-07-11 18:20:07,103 - mmdet - INFO - Epoch [23][200/232]	lr: 1.265e-03, eta: 2:01:43, time: 1.991, data_time: 0.019, memory: 13387, loss_cls: 0.0875, loss_bbox: 0.3105, loss_dir: 0.0263, loss: 0.4242, grad_norm: 0.8559
2021-07-11 18:21:11,038 - mmdet - INFO - Saving checkpoint at 23 epochs
2021-07-11 18:23:04,124 - mmdet - INFO - Epoch [24][50/232]	lr: 1.150e-03, eta: 1:58:44, time: 2.233, data_time: 0.262, memory: 13387, loss_cls: 0.0856, loss_bbox: 0.3008, loss_dir: 0.0257, loss: 0.4121, grad_norm: 0.8595
2021-07-11 18:24:43,354 - mmdet - INFO - Epoch [24][100/232]	lr: 1.150e-03, eta: 1:57:18, time: 1.985, data_time: 0.024, memory: 13387, loss_cls: 0.0853, loss_bbox: 0.2982, loss_dir: 0.0249, loss: 0.4084, grad_norm: 0.9025
2021-07-11 18:26:22,367 - mmdet - INFO - Epoch [24][150/232]	lr: 1.150e-03, eta: 1:55:52, time: 1.980, data_time: 0.023, memory: 13387, loss_cls: 0.0850, loss_bbox: 0.2965, loss_dir: 0.0248, loss: 0.4063, grad_norm: 0.8277
2021-07-11 18:28:01,570 - mmdet - INFO - Epoch [24][200/232]	lr: 1.150e-03, eta: 1:54:25, time: 1.984, data_time: 0.023, memory: 13387, loss_cls: 0.0844, loss_bbox: 0.2925, loss_dir: 0.0241, loss: 0.4010, grad_norm: 0.8438
2021-07-11 18:29:05,133 - mmdet - INFO - Saving checkpoint at 24 epochs
2021-07-11 18:30:57,958 - mmdet - INFO - Epoch [25][50/232]	lr: 1.036e-03, eta: 1:51:29, time: 2.225, data_time: 0.244, memory: 13387, loss_cls: 0.0835, loss_bbox: 0.2909, loss_dir: 0.0239, loss: 0.3984, grad_norm: 0.9323
2021-07-11 18:32:37,894 - mmdet - INFO - Epoch [25][100/232]	lr: 1.036e-03, eta: 1:50:03, time: 1.999, data_time: 0.019, memory: 13387, loss_cls: 0.0803, loss_bbox: 0.2898, loss_dir: 0.0236, loss: 0.3938, grad_norm: 0.9450
2021-07-11 18:34:17,145 - mmdet - INFO - Epoch [25][150/232]	lr: 1.036e-03, eta: 1:48:37, time: 1.985, data_time: 0.018, memory: 13387, loss_cls: 0.0803, loss_bbox: 0.2939, loss_dir: 0.0241, loss: 0.3983, grad_norm: 0.8761
2021-07-11 18:35:56,876 - mmdet - INFO - Epoch [25][200/232]	lr: 1.036e-03, eta: 1:47:10, time: 1.994, data_time: 0.021, memory: 13387, loss_cls: 0.0833, loss_bbox: 0.2938, loss_dir: 0.0255, loss: 0.4025, grad_norm: 0.9362
2021-07-11 18:37:00,839 - mmdet - INFO - Saving checkpoint at 25 epochs
2021-07-11 18:38:53,915 - mmdet - INFO - Epoch [26][50/232]	lr: 9.260e-04, eta: 1:44:18, time: 2.233, data_time: 0.256, memory: 13387, loss_cls: 0.0823, loss_bbox: 0.2896, loss_dir: 0.0242, loss: 0.3961, grad_norm: 0.8449
2021-07-11 18:40:32,965 - mmdet - INFO - Epoch [26][100/232]	lr: 9.260e-04, eta: 1:42:51, time: 1.981, data_time: 0.020, memory: 13387, loss_cls: 0.0816, loss_bbox: 0.2851, loss_dir: 0.0231, loss: 0.3898, grad_norm: 0.8064
2021-07-11 18:42:12,333 - mmdet - INFO - Epoch [26][150/232]	lr: 9.260e-04, eta: 1:41:24, time: 1.987, data_time: 0.019, memory: 13387, loss_cls: 0.0783, loss_bbox: 0.2892, loss_dir: 0.0233, loss: 0.3909, grad_norm: 0.9753
2021-07-11 18:43:51,883 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 18:43:51,883 - mmdet - INFO - Epoch [26][200/232]	lr: 9.260e-04, eta: 1:39:57, time: 1.991, data_time: 0.020, memory: 13387, loss_cls: 0.0827, loss_bbox: 0.2873, loss_dir: 0.0231, loss: 0.3931, grad_norm: 0.9178
2021-07-11 18:44:55,911 - mmdet - INFO - Saving checkpoint at 26 epochs
2021-07-11 18:46:48,750 - mmdet - INFO - Epoch [27][50/232]	lr: 8.190e-04, eta: 1:37:07, time: 2.229, data_time: 0.251, memory: 13387, loss_cls: 0.0778, loss_bbox: 0.2791, loss_dir: 0.0224, loss: 0.3794, grad_norm: 0.8385
2021-07-11 18:48:28,230 - mmdet - INFO - Epoch [27][100/232]	lr: 8.190e-04, eta: 1:35:40, time: 1.990, data_time: 0.022, memory: 13387, loss_cls: 0.0783, loss_bbox: 0.2759, loss_dir: 0.0236, loss: 0.3779, grad_norm: 0.8550
2021-07-11 18:50:07,869 - mmdet - INFO - Epoch [27][150/232]	lr: 8.190e-04, eta: 1:34:13, time: 1.993, data_time: 0.020, memory: 13387, loss_cls: 0.0798, loss_bbox: 0.2740, loss_dir: 0.0221, loss: 0.3760, grad_norm: 0.9299
2021-07-11 18:51:47,781 - mmdet - INFO - Epoch [27][200/232]	lr: 8.190e-04, eta: 1:32:46, time: 1.998, data_time: 0.022, memory: 13387, loss_cls: 0.0821, loss_bbox: 0.2756, loss_dir: 0.0223, loss: 0.3800, grad_norm: 0.8704
2021-07-11 18:52:51,334 - mmdet - INFO - Saving checkpoint at 27 epochs
2021-07-11 18:54:44,404 - mmdet - INFO - Epoch [28][50/232]	lr: 7.163e-04, eta: 1:29:59, time: 2.231, data_time: 0.252, memory: 13387, loss_cls: 0.0778, loss_bbox: 0.2700, loss_dir: 0.0226, loss: 0.3704, grad_norm: 0.8861
2021-07-11 18:56:24,206 - mmdet - INFO - Epoch [28][100/232]	lr: 7.163e-04, eta: 1:28:32, time: 1.996, data_time: 0.022, memory: 13387, loss_cls: 0.0770, loss_bbox: 0.2710, loss_dir: 0.0215, loss: 0.3695, grad_norm: 0.8304
2021-07-11 18:58:03,015 - mmdet - INFO - Epoch [28][150/232]	lr: 7.163e-04, eta: 1:27:04, time: 1.976, data_time: 0.019, memory: 13387, loss_cls: 0.0774, loss_bbox: 0.2782, loss_dir: 0.0225, loss: 0.3781, grad_norm: 0.9639
2021-07-11 18:59:42,922 - mmdet - INFO - Epoch [28][200/232]	lr: 7.163e-04, eta: 1:25:37, time: 1.998, data_time: 0.022, memory: 13387, loss_cls: 0.0810, loss_bbox: 0.2742, loss_dir: 0.0221, loss: 0.3773, grad_norm: 0.8724
2021-07-11 19:00:46,822 - mmdet - INFO - Saving checkpoint at 28 epochs
2021-07-11 19:02:40,246 - mmdet - INFO - Epoch [29][50/232]	lr: 6.183e-04, eta: 1:22:51, time: 2.241, data_time: 0.258, memory: 13387, loss_cls: 0.0770, loss_bbox: 0.2701, loss_dir: 0.0220, loss: 0.3691, grad_norm: 0.9398
2021-07-11 19:04:19,767 - mmdet - INFO - Epoch [29][100/232]	lr: 6.183e-04, eta: 1:21:24, time: 1.991, data_time: 0.021, memory: 13387, loss_cls: 0.0758, loss_bbox: 0.2674, loss_dir: 0.0214, loss: 0.3647, grad_norm: 0.8958
2021-07-11 19:05:59,067 - mmdet - INFO - Epoch [29][150/232]	lr: 6.183e-04, eta: 1:19:56, time: 1.986, data_time: 0.019, memory: 13387, loss_cls: 0.0753, loss_bbox: 0.2618, loss_dir: 0.0212, loss: 0.3582, grad_norm: 0.8191
2021-07-11 19:07:38,249 - mmdet - INFO - Epoch [29][200/232]	lr: 6.183e-04, eta: 1:18:28, time: 1.984, data_time: 0.021, memory: 13387, loss_cls: 0.0801, loss_bbox: 0.2652, loss_dir: 0.0211, loss: 0.3665, grad_norm: 0.9842
2021-07-11 19:08:41,928 - mmdet - INFO - Saving checkpoint at 29 epochs
2021-07-11 19:10:34,853 - mmdet - INFO - Epoch [30][50/232]	lr: 5.259e-04, eta: 1:15:45, time: 2.230, data_time: 0.258, memory: 13387, loss_cls: 0.0756, loss_bbox: 0.2643, loss_dir: 0.0209, loss: 0.3607, grad_norm: 0.9486
2021-07-11 19:12:14,308 - mmdet - INFO - Epoch [30][100/232]	lr: 5.259e-04, eta: 1:14:17, time: 1.989, data_time: 0.021, memory: 13387, loss_cls: 0.0767, loss_bbox: 0.2594, loss_dir: 0.0222, loss: 0.3584, grad_norm: 0.8354
2021-07-11 19:13:53,843 - mmdet - INFO - Epoch [30][150/232]	lr: 5.259e-04, eta: 1:12:49, time: 1.991, data_time: 0.019, memory: 13387, loss_cls: 0.0737, loss_bbox: 0.2582, loss_dir: 0.0206, loss: 0.3525, grad_norm: 1.0588
2021-07-11 19:15:33,356 - mmdet - INFO - Epoch [30][200/232]	lr: 5.259e-04, eta: 1:11:21, time: 1.991, data_time: 0.020, memory: 13387, loss_cls: 0.0777, loss_bbox: 0.2593, loss_dir: 0.0209, loss: 0.3579, grad_norm: 0.8905
2021-07-11 19:18:49,071 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:77.7392, 73.4463, 70.8619
bev  AP:76.5168, 70.7384, 66.7630
3d   AP:70.5321, 63.2314, 59.6432
aos  AP:65.90, 62.69, 60.49
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:77.7392, 73.4463, 70.8619
bev  AP:85.6309, 83.3878, 79.9115
3d   AP:85.5832, 83.1174, 79.4154
aos  AP:65.90, 62.69, 60.49
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.7145, 80.4536, 75.7484
bev  AP:85.0273, 71.3721, 68.1335
3d   AP:82.5517, 67.1273, 63.4477
aos  AP:88.12, 77.35, 72.95
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.7145, 80.4536, 75.7484
bev  AP:88.2498, 78.8611, 76.0418
3d   AP:88.2498, 78.8583, 76.0370
aos  AP:88.12, 77.35, 72.95
Car AP@0.70, 0.70, 0.70:
bbox AP:96.3820, 89.7096, 88.7479
bev  AP:90.0018, 87.8563, 85.8619
3d   AP:87.9158, 77.1880, 74.4712
aos  AP:96.26, 89.21, 87.85
Car AP@0.70, 0.50, 0.50:
bbox AP:96.3820, 89.7096, 88.7479
bev  AP:96.5408, 90.0851, 89.5516
3d   AP:96.4075, 90.0304, 89.4245
aos  AP:96.26, 89.21, 87.85

Overall AP@easy, moderate, hard:
bbox AP:87.6119, 81.2032, 78.4527
bev  AP:83.8486, 76.6556, 73.5861
3d   AP:80.3332, 69.1822, 65.8540
aos  AP:83.43, 76.41, 73.77

2021-07-11 19:18:49,103 - mmdet - INFO - Saving checkpoint at 30 epochs
2021-07-11 19:18:50,262 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 19:18:50,262 - mmdet - INFO - Epoch(val) [30][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 70.5321, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 76.5168, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 77.7392, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 63.2314, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.7384, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 73.4463, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 59.6432, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 66.7630, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 70.8619, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 85.5832, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 85.6309, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 77.7392, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 83.1174, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 83.3878, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 73.4463, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 79.4154, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 79.9115, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 70.8619, pts_bbox/KITTI/Cyclist_3D_easy_strict: 82.5517, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 85.0273, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.7145, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 67.1273, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 71.3721, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 80.4536, pts_bbox/KITTI/Cyclist_3D_hard_strict: 63.4477, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 68.1335, pts_bbox/KITTI/Cyclist_2D_hard_strict: 75.7484, pts_bbox/KITTI/Cyclist_3D_easy_loose: 88.2498, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 88.2498, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.7145, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 78.8583, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 78.8611, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 80.4536, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.0370, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.0418, pts_bbox/KITTI/Cyclist_2D_hard_loose: 75.7484, pts_bbox/KITTI/Car_3D_easy_strict: 87.9158, pts_bbox/KITTI/Car_BEV_easy_strict: 90.0018, pts_bbox/KITTI/Car_2D_easy_strict: 96.3820, pts_bbox/KITTI/Car_3D_moderate_strict: 77.1880, pts_bbox/KITTI/Car_BEV_moderate_strict: 87.8563, pts_bbox/KITTI/Car_2D_moderate_strict: 89.7096, pts_bbox/KITTI/Car_3D_hard_strict: 74.4712, pts_bbox/KITTI/Car_BEV_hard_strict: 85.8619, pts_bbox/KITTI/Car_2D_hard_strict: 88.7479, pts_bbox/KITTI/Car_3D_easy_loose: 96.4075, pts_bbox/KITTI/Car_BEV_easy_loose: 96.5408, pts_bbox/KITTI/Car_2D_easy_loose: 96.3820, pts_bbox/KITTI/Car_3D_moderate_loose: 90.0304, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.0851, pts_bbox/KITTI/Car_2D_moderate_loose: 89.7096, pts_bbox/KITTI/Car_3D_hard_loose: 89.4245, pts_bbox/KITTI/Car_BEV_hard_loose: 89.5516, pts_bbox/KITTI/Car_2D_hard_loose: 88.7479, pts_bbox/KITTI/Overall_3D_easy: 80.3332, pts_bbox/KITTI/Overall_BEV_easy: 83.8486, pts_bbox/KITTI/Overall_2D_easy: 87.6119, pts_bbox/KITTI/Overall_3D_moderate: 69.1822, pts_bbox/KITTI/Overall_BEV_moderate: 76.6556, pts_bbox/KITTI/Overall_2D_moderate: 81.2032, pts_bbox/KITTI/Overall_3D_hard: 65.8540, pts_bbox/KITTI/Overall_BEV_hard: 73.5861, pts_bbox/KITTI/Overall_2D_hard: 78.4527
2021-07-11 19:20:38,804 - mmdet - INFO - Epoch [31][50/232]	lr: 4.394e-04, eta: 1:08:39, time: 2.170, data_time: 0.189, memory: 13387, loss_cls: 0.0749, loss_bbox: 0.2559, loss_dir: 0.0197, loss: 0.3505, grad_norm: 0.9603
2021-07-11 19:22:18,172 - mmdet - INFO - Epoch [31][100/232]	lr: 4.394e-04, eta: 1:07:11, time: 1.987, data_time: 0.020, memory: 13387, loss_cls: 0.0739, loss_bbox: 0.2510, loss_dir: 0.0192, loss: 0.3441, grad_norm: 0.8524
2021-07-11 19:23:57,749 - mmdet - INFO - Epoch [31][150/232]	lr: 4.394e-04, eta: 1:05:43, time: 1.992, data_time: 0.020, memory: 13387, loss_cls: 0.0734, loss_bbox: 0.2498, loss_dir: 0.0195, loss: 0.3427, grad_norm: 0.8512
2021-07-11 19:25:38,337 - mmdet - INFO - Epoch [31][200/232]	lr: 4.394e-04, eta: 1:04:15, time: 2.012, data_time: 0.021, memory: 13387, loss_cls: 0.0754, loss_bbox: 0.2588, loss_dir: 0.0202, loss: 0.3543, grad_norm: 0.8875
2021-07-11 19:28:41,979 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:79.4357, 74.2470, 71.8973
bev  AP:76.9174, 70.4438, 66.4680
3d   AP:71.2596, 66.1801, 60.2417
aos  AP:69.24, 65.36, 63.08
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:79.4357, 74.2470, 71.8973
bev  AP:85.7192, 83.5756, 79.7695
3d   AP:85.6806, 83.2858, 79.2474
aos  AP:69.24, 65.36, 63.08
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.8702, 80.6859, 76.4377
bev  AP:86.3009, 72.1999, 68.5041
3d   AP:84.8364, 69.1156, 64.0441
aos  AP:88.36, 78.22, 74.14
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.8702, 80.6859, 76.4377
bev  AP:93.3626, 79.2573, 76.7735
3d   AP:93.3626, 79.2539, 76.7685
aos  AP:88.36, 78.22, 74.14
Car AP@0.70, 0.70, 0.70:
bbox AP:96.3707, 89.9258, 89.0280
bev  AP:89.9713, 87.7847, 85.3816
3d   AP:87.8724, 77.3658, 74.3087
aos  AP:96.25, 89.36, 88.05
Car AP@0.70, 0.50, 0.50:
bbox AP:96.3707, 89.9258, 89.0280
bev  AP:96.6103, 90.2377, 89.6928
3d   AP:96.4813, 90.1811, 89.5900
aos  AP:96.25, 89.36, 88.05

Overall AP@easy, moderate, hard:
bbox AP:88.2255, 81.6196, 79.1210
bev  AP:84.3965, 76.8095, 73.4512
3d   AP:81.3228, 70.8872, 66.1982
aos  AP:84.62, 77.64, 75.09

2021-07-11 19:28:42,017 - mmdet - INFO - Saving checkpoint at 31 epochs
2021-07-11 19:28:43,315 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 19:28:43,316 - mmdet - INFO - Epoch(val) [31][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 71.2596, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 76.9174, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 79.4357, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 66.1801, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.4438, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 74.2470, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 60.2417, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 66.4680, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 71.8973, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 85.6806, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 85.7192, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 79.4357, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 83.2858, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 83.5756, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 74.2470, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 79.2474, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 79.7695, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 71.8973, pts_bbox/KITTI/Cyclist_3D_easy_strict: 84.8364, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 86.3009, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.8702, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 69.1156, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 72.1999, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 80.6859, pts_bbox/KITTI/Cyclist_3D_hard_strict: 64.0441, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 68.5041, pts_bbox/KITTI/Cyclist_2D_hard_strict: 76.4377, pts_bbox/KITTI/Cyclist_3D_easy_loose: 93.3626, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 93.3626, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.8702, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 79.2539, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 79.2573, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 80.6859, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.7685, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.7735, pts_bbox/KITTI/Cyclist_2D_hard_loose: 76.4377, pts_bbox/KITTI/Car_3D_easy_strict: 87.8724, pts_bbox/KITTI/Car_BEV_easy_strict: 89.9713, pts_bbox/KITTI/Car_2D_easy_strict: 96.3707, pts_bbox/KITTI/Car_3D_moderate_strict: 77.3658, pts_bbox/KITTI/Car_BEV_moderate_strict: 87.7847, pts_bbox/KITTI/Car_2D_moderate_strict: 89.9258, pts_bbox/KITTI/Car_3D_hard_strict: 74.3087, pts_bbox/KITTI/Car_BEV_hard_strict: 85.3816, pts_bbox/KITTI/Car_2D_hard_strict: 89.0280, pts_bbox/KITTI/Car_3D_easy_loose: 96.4813, pts_bbox/KITTI/Car_BEV_easy_loose: 96.6103, pts_bbox/KITTI/Car_2D_easy_loose: 96.3707, pts_bbox/KITTI/Car_3D_moderate_loose: 90.1811, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.2377, pts_bbox/KITTI/Car_2D_moderate_loose: 89.9258, pts_bbox/KITTI/Car_3D_hard_loose: 89.5900, pts_bbox/KITTI/Car_BEV_hard_loose: 89.6928, pts_bbox/KITTI/Car_2D_hard_loose: 89.0280, pts_bbox/KITTI/Overall_3D_easy: 81.3228, pts_bbox/KITTI/Overall_BEV_easy: 84.3965, pts_bbox/KITTI/Overall_2D_easy: 88.2255, pts_bbox/KITTI/Overall_3D_moderate: 70.8872, pts_bbox/KITTI/Overall_BEV_moderate: 76.8095, pts_bbox/KITTI/Overall_2D_moderate: 81.6196, pts_bbox/KITTI/Overall_3D_hard: 66.1982, pts_bbox/KITTI/Overall_BEV_hard: 73.4512, pts_bbox/KITTI/Overall_2D_hard: 79.1210
2021-07-11 19:30:30,868 - mmdet - INFO - Epoch [32][50/232]	lr: 3.594e-04, eta: 1:01:34, time: 2.150, data_time: 0.144, memory: 13387, loss_cls: 0.0720, loss_bbox: 0.2479, loss_dir: 0.0190, loss: 0.3389, grad_norm: 0.8949
2021-07-11 19:32:12,260 - mmdet - INFO - Epoch [32][100/232]	lr: 3.594e-04, eta: 1:00:06, time: 2.028, data_time: 0.021, memory: 13387, loss_cls: 0.0725, loss_bbox: 0.2486, loss_dir: 0.0196, loss: 0.3408, grad_norm: 0.7888
2021-07-11 19:33:53,159 - mmdet - INFO - Epoch [32][150/232]	lr: 3.594e-04, eta: 0:58:38, time: 2.018, data_time: 0.019, memory: 13387, loss_cls: 0.0725, loss_bbox: 0.2506, loss_dir: 0.0199, loss: 0.3431, grad_norm: 0.9049
2021-07-11 19:35:34,493 - mmdet - INFO - Epoch [32][200/232]	lr: 3.594e-04, eta: 0:57:10, time: 2.027, data_time: 0.020, memory: 13387, loss_cls: 0.0736, loss_bbox: 0.2489, loss_dir: 0.0192, loss: 0.3418, grad_norm: 0.8089
2021-07-11 19:38:42,232 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:80.0977, 74.4661, 71.9724
bev  AP:76.8554, 70.4680, 66.6900
3d   AP:70.9256, 63.1067, 59.4482
aos  AP:71.03, 66.27, 63.87
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:80.0977, 74.4661, 71.9724
bev  AP:85.7687, 83.6219, 80.3702
3d   AP:85.7641, 83.5450, 80.0269
aos  AP:71.03, 66.27, 63.87
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.8693, 79.8378, 75.7710
bev  AP:87.2726, 72.2283, 68.6149
3d   AP:84.2689, 67.5141, 63.2636
aos  AP:87.93, 77.03, 73.22
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.8693, 79.8378, 75.7710
bev  AP:88.0716, 77.8628, 76.0840
3d   AP:88.0716, 77.8628, 76.0840
aos  AP:87.93, 77.03, 73.22
Car AP@0.70, 0.70, 0.70:
bbox AP:96.4346, 89.9474, 89.0322
bev  AP:89.9971, 87.8687, 85.7367
3d   AP:88.3250, 77.7770, 74.8057
aos  AP:96.33, 89.57, 88.33
Car AP@0.70, 0.50, 0.50:
bbox AP:96.4346, 89.9474, 89.0322
bev  AP:96.6247, 90.2382, 89.6694
3d   AP:96.5208, 90.2020, 89.5881
aos  AP:96.33, 89.57, 88.33

Overall AP@easy, moderate, hard:
bbox AP:88.4672, 81.4171, 78.9252
bev  AP:84.7084, 76.8550, 73.6805
3d   AP:81.1732, 69.4659, 65.8391
aos  AP:85.10, 77.62, 75.14

2021-07-11 19:38:42,269 - mmdet - INFO - Saving checkpoint at 32 epochs
2021-07-11 19:38:43,580 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 19:38:43,580 - mmdet - INFO - Epoch(val) [32][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 70.9256, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 76.8554, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 80.0977, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 63.1067, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.4680, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 74.4661, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 59.4482, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 66.6900, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 71.9724, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 85.7641, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 85.7687, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 80.0977, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 83.5450, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 83.6219, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 74.4661, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 80.0269, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 80.3702, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 71.9724, pts_bbox/KITTI/Cyclist_3D_easy_strict: 84.2689, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 87.2726, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.8693, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 67.5141, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 72.2283, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 79.8378, pts_bbox/KITTI/Cyclist_3D_hard_strict: 63.2636, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 68.6149, pts_bbox/KITTI/Cyclist_2D_hard_strict: 75.7710, pts_bbox/KITTI/Cyclist_3D_easy_loose: 88.0716, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 88.0716, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.8693, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 77.8628, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 77.8628, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 79.8378, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.0840, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.0840, pts_bbox/KITTI/Cyclist_2D_hard_loose: 75.7710, pts_bbox/KITTI/Car_3D_easy_strict: 88.3250, pts_bbox/KITTI/Car_BEV_easy_strict: 89.9971, pts_bbox/KITTI/Car_2D_easy_strict: 96.4346, pts_bbox/KITTI/Car_3D_moderate_strict: 77.7770, pts_bbox/KITTI/Car_BEV_moderate_strict: 87.8687, pts_bbox/KITTI/Car_2D_moderate_strict: 89.9474, pts_bbox/KITTI/Car_3D_hard_strict: 74.8057, pts_bbox/KITTI/Car_BEV_hard_strict: 85.7367, pts_bbox/KITTI/Car_2D_hard_strict: 89.0322, pts_bbox/KITTI/Car_3D_easy_loose: 96.5208, pts_bbox/KITTI/Car_BEV_easy_loose: 96.6247, pts_bbox/KITTI/Car_2D_easy_loose: 96.4346, pts_bbox/KITTI/Car_3D_moderate_loose: 90.2020, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.2382, pts_bbox/KITTI/Car_2D_moderate_loose: 89.9474, pts_bbox/KITTI/Car_3D_hard_loose: 89.5881, pts_bbox/KITTI/Car_BEV_hard_loose: 89.6694, pts_bbox/KITTI/Car_2D_hard_loose: 89.0322, pts_bbox/KITTI/Overall_3D_easy: 81.1732, pts_bbox/KITTI/Overall_BEV_easy: 84.7084, pts_bbox/KITTI/Overall_2D_easy: 88.4672, pts_bbox/KITTI/Overall_3D_moderate: 69.4659, pts_bbox/KITTI/Overall_BEV_moderate: 76.8550, pts_bbox/KITTI/Overall_2D_moderate: 81.4171, pts_bbox/KITTI/Overall_3D_hard: 65.8391, pts_bbox/KITTI/Overall_BEV_hard: 73.6805, pts_bbox/KITTI/Overall_2D_hard: 78.9252
2021-07-11 19:40:33,210 - mmdet - INFO - Epoch [33][50/232]	lr: 2.865e-04, eta: 0:54:31, time: 2.191, data_time: 0.178, memory: 13387, loss_cls: 0.0708, loss_bbox: 0.2440, loss_dir: 0.0185, loss: 0.3332, grad_norm: 0.9018
2021-07-11 19:42:13,836 - mmdet - INFO - Epoch [33][100/232]	lr: 2.865e-04, eta: 0:53:03, time: 2.013, data_time: 0.022, memory: 13387, loss_cls: 0.0712, loss_bbox: 0.2438, loss_dir: 0.0185, loss: 0.3336, grad_norm: 0.7745
2021-07-11 19:43:54,708 - mmdet - INFO - Epoch [33][150/232]	lr: 2.865e-04, eta: 0:51:35, time: 2.018, data_time: 0.020, memory: 13387, loss_cls: 0.0719, loss_bbox: 0.2437, loss_dir: 0.0194, loss: 0.3350, grad_norm: 0.8076
2021-07-11 19:45:36,630 - mmdet - INFO - Epoch [33][200/232]	lr: 2.865e-04, eta: 0:50:07, time: 2.039, data_time: 0.021, memory: 13387, loss_cls: 0.0739, loss_bbox: 0.2421, loss_dir: 0.0191, loss: 0.3352, grad_norm: 0.9489
2021-07-11 19:48:50,578 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:79.4377, 74.6928, 72.3953
bev  AP:76.7224, 70.0376, 63.3904
3d   AP:69.4131, 61.9428, 57.7886
aos  AP:69.41, 65.30, 62.92
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:79.4377, 74.6928, 72.3953
bev  AP:86.1488, 83.9280, 80.2538
3d   AP:86.1437, 83.8005, 80.1495
aos  AP:69.41, 65.30, 62.92
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.3609, 80.1790, 78.5742
bev  AP:86.6113, 72.4015, 67.8738
3d   AP:84.7259, 68.2401, 63.9715
aos  AP:87.73, 78.31, 76.50
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.3609, 80.1790, 78.5742
bev  AP:88.2970, 79.4366, 76.2855
3d   AP:88.2970, 79.4330, 76.2808
aos  AP:87.73, 78.31, 76.50
Car AP@0.70, 0.70, 0.70:
bbox AP:96.1744, 89.9100, 88.9781
bev  AP:90.2024, 88.0776, 85.8836
3d   AP:88.3069, 77.8122, 74.6669
aos  AP:96.09, 89.47, 88.09
Car AP@0.70, 0.50, 0.50:
bbox AP:96.1744, 89.9100, 88.9781
bev  AP:96.3520, 90.1835, 89.6173
3d   AP:96.2530, 90.1465, 89.5227
aos  AP:96.09, 89.47, 88.09

Overall AP@easy, moderate, hard:
bbox AP:87.9910, 81.5939, 79.9825
bev  AP:84.5120, 76.8389, 72.3826
3d   AP:80.8153, 69.3317, 65.4757
aos  AP:84.41, 77.70, 75.83

2021-07-11 19:48:50,625 - mmdet - INFO - Saving checkpoint at 33 epochs
2021-07-11 19:48:52,032 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 19:48:52,033 - mmdet - INFO - Epoch(val) [33][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 69.4131, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 76.7224, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 79.4377, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 61.9428, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.0376, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 74.6928, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 57.7886, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 63.3904, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 72.3953, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 86.1437, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 86.1488, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 79.4377, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 83.8005, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 83.9280, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 74.6928, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 80.1495, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 80.2538, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 72.3953, pts_bbox/KITTI/Cyclist_3D_easy_strict: 84.7259, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 86.6113, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.3609, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 68.2401, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 72.4015, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 80.1790, pts_bbox/KITTI/Cyclist_3D_hard_strict: 63.9715, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 67.8738, pts_bbox/KITTI/Cyclist_2D_hard_strict: 78.5742, pts_bbox/KITTI/Cyclist_3D_easy_loose: 88.2970, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 88.2970, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.3609, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 79.4330, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 79.4366, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 80.1790, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.2808, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.2855, pts_bbox/KITTI/Cyclist_2D_hard_loose: 78.5742, pts_bbox/KITTI/Car_3D_easy_strict: 88.3069, pts_bbox/KITTI/Car_BEV_easy_strict: 90.2024, pts_bbox/KITTI/Car_2D_easy_strict: 96.1744, pts_bbox/KITTI/Car_3D_moderate_strict: 77.8122, pts_bbox/KITTI/Car_BEV_moderate_strict: 88.0776, pts_bbox/KITTI/Car_2D_moderate_strict: 89.9100, pts_bbox/KITTI/Car_3D_hard_strict: 74.6669, pts_bbox/KITTI/Car_BEV_hard_strict: 85.8836, pts_bbox/KITTI/Car_2D_hard_strict: 88.9781, pts_bbox/KITTI/Car_3D_easy_loose: 96.2530, pts_bbox/KITTI/Car_BEV_easy_loose: 96.3520, pts_bbox/KITTI/Car_2D_easy_loose: 96.1744, pts_bbox/KITTI/Car_3D_moderate_loose: 90.1465, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.1835, pts_bbox/KITTI/Car_2D_moderate_loose: 89.9100, pts_bbox/KITTI/Car_3D_hard_loose: 89.5227, pts_bbox/KITTI/Car_BEV_hard_loose: 89.6173, pts_bbox/KITTI/Car_2D_hard_loose: 88.9781, pts_bbox/KITTI/Overall_3D_easy: 80.8153, pts_bbox/KITTI/Overall_BEV_easy: 84.5120, pts_bbox/KITTI/Overall_2D_easy: 87.9910, pts_bbox/KITTI/Overall_3D_moderate: 69.3317, pts_bbox/KITTI/Overall_BEV_moderate: 76.8389, pts_bbox/KITTI/Overall_2D_moderate: 81.5939, pts_bbox/KITTI/Overall_3D_hard: 65.4757, pts_bbox/KITTI/Overall_BEV_hard: 72.3826, pts_bbox/KITTI/Overall_2D_hard: 79.9825
2021-07-11 19:50:41,194 - mmdet - INFO - Epoch [34][50/232]	lr: 2.211e-04, eta: 0:47:30, time: 2.182, data_time: 0.172, memory: 13387, loss_cls: 0.0701, loss_bbox: 0.2378, loss_dir: 0.0185, loss: 0.3264, grad_norm: 0.7430
2021-07-11 19:52:22,378 - mmdet - INFO - Epoch [34][100/232]	lr: 2.211e-04, eta: 0:46:01, time: 2.023, data_time: 0.020, memory: 13387, loss_cls: 0.0705, loss_bbox: 0.2395, loss_dir: 0.0181, loss: 0.3281, grad_norm: 0.7704
2021-07-11 19:54:03,456 - mmdet - INFO - Epoch [34][150/232]	lr: 2.211e-04, eta: 0:44:32, time: 2.022, data_time: 0.019, memory: 13387, loss_cls: 0.0710, loss_bbox: 0.2428, loss_dir: 0.0187, loss: 0.3325, grad_norm: 0.9086
2021-07-11 19:55:45,288 - mmdet - INFO - Epoch [34][200/232]	lr: 2.211e-04, eta: 0:43:04, time: 2.037, data_time: 0.022, memory: 13387, loss_cls: 0.0752, loss_bbox: 0.2409, loss_dir: 0.0187, loss: 0.3348, grad_norm: 0.7002
2021-07-11 19:58:54,072 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:79.9196, 75.1689, 72.7635
bev  AP:76.8724, 70.3983, 63.4891
3d   AP:71.4691, 63.7668, 60.3118
aos  AP:70.90, 66.83, 64.53
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:79.9196, 75.1689, 72.7635
bev  AP:86.1160, 84.1166, 80.2777
3d   AP:86.1062, 84.0571, 79.9923
aos  AP:70.90, 66.83, 64.53
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.7777, 80.1222, 78.9236
bev  AP:84.5334, 71.4686, 68.4386
3d   AP:82.1022, 67.6202, 62.9070
aos  AP:88.15, 78.18, 76.88
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.7777, 80.1222, 78.9236
bev  AP:87.9250, 79.4742, 76.5008
3d   AP:87.9250, 79.4742, 76.5008
aos  AP:88.15, 78.18, 76.88
Car AP@0.70, 0.70, 0.70:
bbox AP:96.1968, 89.8637, 88.8969
bev  AP:90.1174, 87.6273, 85.8009
3d   AP:88.2652, 77.5266, 74.6963
aos  AP:96.11, 89.44, 88.11
Car AP@0.70, 0.50, 0.50:
bbox AP:96.1968, 89.8637, 88.8969
bev  AP:96.3653, 90.1843, 89.6144
3d   AP:96.2619, 90.1499, 89.5375
aos  AP:96.11, 89.44, 88.11

Overall AP@easy, moderate, hard:
bbox AP:88.2980, 81.7183, 80.1946
bev  AP:83.8411, 76.4981, 72.5762
3d   AP:80.6121, 69.6379, 65.9717
aos  AP:85.06, 78.15, 76.51

2021-07-11 19:58:54,108 - mmdet - INFO - Saving checkpoint at 34 epochs
2021-07-11 19:58:55,290 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 19:58:55,290 - mmdet - INFO - Epoch(val) [34][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 71.4691, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 76.8724, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 79.9196, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 63.7668, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.3983, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 75.1689, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 60.3118, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 63.4891, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 72.7635, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 86.1062, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 86.1160, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 79.9196, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 84.0571, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 84.1166, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 75.1689, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 79.9923, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 80.2777, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 72.7635, pts_bbox/KITTI/Cyclist_3D_easy_strict: 82.1022, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 84.5334, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.7777, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 67.6202, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 71.4686, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 80.1222, pts_bbox/KITTI/Cyclist_3D_hard_strict: 62.9070, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 68.4386, pts_bbox/KITTI/Cyclist_2D_hard_strict: 78.9236, pts_bbox/KITTI/Cyclist_3D_easy_loose: 87.9250, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 87.9250, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.7777, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 79.4742, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 79.4742, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 80.1222, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.5008, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.5008, pts_bbox/KITTI/Cyclist_2D_hard_loose: 78.9236, pts_bbox/KITTI/Car_3D_easy_strict: 88.2652, pts_bbox/KITTI/Car_BEV_easy_strict: 90.1174, pts_bbox/KITTI/Car_2D_easy_strict: 96.1968, pts_bbox/KITTI/Car_3D_moderate_strict: 77.5266, pts_bbox/KITTI/Car_BEV_moderate_strict: 87.6273, pts_bbox/KITTI/Car_2D_moderate_strict: 89.8637, pts_bbox/KITTI/Car_3D_hard_strict: 74.6963, pts_bbox/KITTI/Car_BEV_hard_strict: 85.8009, pts_bbox/KITTI/Car_2D_hard_strict: 88.8969, pts_bbox/KITTI/Car_3D_easy_loose: 96.2619, pts_bbox/KITTI/Car_BEV_easy_loose: 96.3653, pts_bbox/KITTI/Car_2D_easy_loose: 96.1968, pts_bbox/KITTI/Car_3D_moderate_loose: 90.1499, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.1843, pts_bbox/KITTI/Car_2D_moderate_loose: 89.8637, pts_bbox/KITTI/Car_3D_hard_loose: 89.5375, pts_bbox/KITTI/Car_BEV_hard_loose: 89.6144, pts_bbox/KITTI/Car_2D_hard_loose: 88.8969, pts_bbox/KITTI/Overall_3D_easy: 80.6121, pts_bbox/KITTI/Overall_BEV_easy: 83.8411, pts_bbox/KITTI/Overall_2D_easy: 88.2980, pts_bbox/KITTI/Overall_3D_moderate: 69.6379, pts_bbox/KITTI/Overall_BEV_moderate: 76.4981, pts_bbox/KITTI/Overall_2D_moderate: 81.7183, pts_bbox/KITTI/Overall_3D_hard: 65.9717, pts_bbox/KITTI/Overall_BEV_hard: 72.5762, pts_bbox/KITTI/Overall_2D_hard: 80.1946
2021-07-11 20:00:45,199 - mmdet - INFO - Epoch [35][50/232]	lr: 1.635e-04, eta: 0:40:28, time: 2.197, data_time: 0.183, memory: 13387, loss_cls: 0.0694, loss_bbox: 0.2371, loss_dir: 0.0179, loss: 0.3245, grad_norm: 0.9709
2021-07-11 20:02:26,020 - mmdet - INFO - Epoch [35][100/232]	lr: 1.635e-04, eta: 0:39:00, time: 2.016, data_time: 0.021, memory: 13387, loss_cls: 0.0694, loss_bbox: 0.2379, loss_dir: 0.0178, loss: 0.3250, grad_norm: 0.7843
2021-07-11 20:04:06,666 - mmdet - INFO - Epoch [35][150/232]	lr: 1.635e-04, eta: 0:37:31, time: 2.013, data_time: 0.020, memory: 13387, loss_cls: 0.0697, loss_bbox: 0.2408, loss_dir: 0.0177, loss: 0.3282, grad_norm: 0.9326
2021-07-11 20:05:47,444 - mmdet - INFO - Epoch [35][200/232]	lr: 1.635e-04, eta: 0:36:01, time: 2.016, data_time: 0.021, memory: 13387, loss_cls: 0.0733, loss_bbox: 0.2378, loss_dir: 0.0178, loss: 0.3290, grad_norm: 0.9056
2021-07-11 20:08:58,487 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:79.6891, 74.9216, 72.6582
bev  AP:77.1621, 70.4358, 65.7824
3d   AP:71.1405, 63.4344, 59.6511
aos  AP:69.43, 65.71, 63.43
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:79.6891, 74.9216, 72.6582
bev  AP:86.2058, 84.0016, 80.4186
3d   AP:86.2008, 83.9407, 80.2308
aos  AP:69.43, 65.71, 63.43
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.5082, 80.5169, 78.3858
bev  AP:85.9492, 71.4943, 68.3319
3d   AP:83.7039, 68.7030, 63.9581
aos  AP:87.67, 78.47, 76.38
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.5082, 80.5169, 78.3858
bev  AP:87.9244, 79.0774, 76.0587
3d   AP:87.9244, 79.0774, 76.0587
aos  AP:87.67, 78.47, 76.38
Car AP@0.70, 0.70, 0.70:
bbox AP:96.2356, 89.9042, 88.9878
bev  AP:90.1123, 87.6336, 85.7390
3d   AP:88.3335, 77.6895, 74.8221
aos  AP:96.13, 89.50, 88.13
Car AP@0.70, 0.50, 0.50:
bbox AP:96.2356, 89.9042, 88.9878
bev  AP:96.4285, 90.1976, 89.6092
3d   AP:96.3280, 90.1691, 89.5223
aos  AP:96.13, 89.50, 88.13

Overall AP@easy, moderate, hard:
bbox AP:88.1443, 81.7809, 80.0106
bev  AP:84.4079, 76.5212, 73.2844
3d   AP:81.0593, 69.9423, 66.1438
aos  AP:84.41, 77.89, 75.98

2021-07-11 20:08:58,523 - mmdet - INFO - Saving checkpoint at 35 epochs
2021-07-11 20:08:59,800 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 20:08:59,800 - mmdet - INFO - Epoch(val) [35][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 71.1405, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 77.1621, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 79.6891, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 63.4344, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.4358, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 74.9216, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 59.6511, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 65.7824, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 72.6582, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 86.2008, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 86.2058, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 79.6891, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 83.9407, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 84.0016, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 74.9216, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 80.2308, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 80.4186, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 72.6582, pts_bbox/KITTI/Cyclist_3D_easy_strict: 83.7039, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 85.9492, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.5082, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 68.7030, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 71.4943, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 80.5169, pts_bbox/KITTI/Cyclist_3D_hard_strict: 63.9581, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 68.3319, pts_bbox/KITTI/Cyclist_2D_hard_strict: 78.3858, pts_bbox/KITTI/Cyclist_3D_easy_loose: 87.9244, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 87.9244, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.5082, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 79.0774, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 79.0774, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 80.5169, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.0587, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.0587, pts_bbox/KITTI/Cyclist_2D_hard_loose: 78.3858, pts_bbox/KITTI/Car_3D_easy_strict: 88.3335, pts_bbox/KITTI/Car_BEV_easy_strict: 90.1123, pts_bbox/KITTI/Car_2D_easy_strict: 96.2356, pts_bbox/KITTI/Car_3D_moderate_strict: 77.6895, pts_bbox/KITTI/Car_BEV_moderate_strict: 87.6336, pts_bbox/KITTI/Car_2D_moderate_strict: 89.9042, pts_bbox/KITTI/Car_3D_hard_strict: 74.8221, pts_bbox/KITTI/Car_BEV_hard_strict: 85.7390, pts_bbox/KITTI/Car_2D_hard_strict: 88.9878, pts_bbox/KITTI/Car_3D_easy_loose: 96.3280, pts_bbox/KITTI/Car_BEV_easy_loose: 96.4285, pts_bbox/KITTI/Car_2D_easy_loose: 96.2356, pts_bbox/KITTI/Car_3D_moderate_loose: 90.1691, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.1976, pts_bbox/KITTI/Car_2D_moderate_loose: 89.9042, pts_bbox/KITTI/Car_3D_hard_loose: 89.5223, pts_bbox/KITTI/Car_BEV_hard_loose: 89.6092, pts_bbox/KITTI/Car_2D_hard_loose: 88.9878, pts_bbox/KITTI/Overall_3D_easy: 81.0593, pts_bbox/KITTI/Overall_BEV_easy: 84.4079, pts_bbox/KITTI/Overall_2D_easy: 88.1443, pts_bbox/KITTI/Overall_3D_moderate: 69.9423, pts_bbox/KITTI/Overall_BEV_moderate: 76.5212, pts_bbox/KITTI/Overall_2D_moderate: 81.7809, pts_bbox/KITTI/Overall_3D_hard: 66.1438, pts_bbox/KITTI/Overall_BEV_hard: 73.2844, pts_bbox/KITTI/Overall_2D_hard: 80.0106
2021-07-11 20:10:46,510 - mmdet - INFO - Epoch [36][50/232]	lr: 1.142e-04, eta: 0:33:27, time: 2.132, data_time: 0.145, memory: 13387, loss_cls: 0.0694, loss_bbox: 0.2346, loss_dir: 0.0177, loss: 0.3217, grad_norm: 0.7960
2021-07-11 20:12:25,872 - mmdet - INFO - Epoch [36][100/232]	lr: 1.142e-04, eta: 0:31:58, time: 1.987, data_time: 0.020, memory: 13387, loss_cls: 0.0688, loss_bbox: 0.2337, loss_dir: 0.0179, loss: 0.3203, grad_norm: 0.7852
2021-07-11 20:14:04,543 - mmdet - INFO - Epoch [36][150/232]	lr: 1.142e-04, eta: 0:30:28, time: 1.973, data_time: 0.019, memory: 13390, loss_cls: 0.0689, loss_bbox: 0.2355, loss_dir: 0.0184, loss: 0.3228, grad_norm: 0.7785
2021-07-11 20:15:44,742 - mmdet - INFO - Epoch [36][200/232]	lr: 1.142e-04, eta: 0:28:59, time: 2.004, data_time: 0.021, memory: 13390, loss_cls: 0.0738, loss_bbox: 0.2348, loss_dir: 0.0176, loss: 0.3263, grad_norm: 0.8020
2021-07-11 20:18:48,139 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:78.6621, 74.3172, 72.0516
bev  AP:77.2642, 70.3051, 63.8736
3d   AP:71.6745, 64.0329, 60.5968
aos  AP:70.45, 66.37, 63.84
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:78.6621, 74.3172, 72.0516
bev  AP:86.1807, 84.1692, 80.3761
3d   AP:86.1431, 83.9186, 80.0099
aos  AP:70.45, 66.37, 63.84
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.6029, 80.5999, 79.1660
bev  AP:86.7651, 72.7034, 69.3351
3d   AP:84.0409, 68.8904, 63.8303
aos  AP:87.46, 78.23, 76.92
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.6029, 80.5999, 79.1660
bev  AP:92.6964, 79.7127, 76.7210
3d   AP:92.6964, 79.7127, 76.7210
aos  AP:87.46, 78.23, 76.92
Car AP@0.70, 0.70, 0.70:
bbox AP:96.5569, 89.8696, 89.0056
bev  AP:90.1487, 87.7698, 85.4721
3d   AP:88.1640, 77.7001, 74.9083
aos  AP:96.44, 89.46, 88.23
Car AP@0.70, 0.50, 0.50:
bbox AP:96.5569, 89.8696, 89.0056
bev  AP:96.7584, 90.1437, 89.5933
3d   AP:96.6491, 90.1080, 89.5183
aos  AP:96.44, 89.46, 88.23

Overall AP@easy, moderate, hard:
bbox AP:87.9407, 81.5956, 80.0744
bev  AP:84.7260, 76.9261, 72.8936
3d   AP:81.2931, 70.2078, 66.4451
aos  AP:84.78, 78.02, 76.33

2021-07-11 20:18:48,175 - mmdet - INFO - Saving checkpoint at 36 epochs
2021-07-11 20:18:49,463 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 20:18:49,464 - mmdet - INFO - Epoch(val) [36][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 71.6745, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 77.2642, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 78.6621, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 64.0329, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.3051, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 74.3172, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 60.5968, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 63.8736, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 72.0516, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 86.1431, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 86.1807, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 78.6621, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 83.9186, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 84.1692, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 74.3172, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 80.0099, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 80.3761, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 72.0516, pts_bbox/KITTI/Cyclist_3D_easy_strict: 84.0409, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 86.7651, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.6029, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 68.8904, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 72.7034, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 80.5999, pts_bbox/KITTI/Cyclist_3D_hard_strict: 63.8303, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 69.3351, pts_bbox/KITTI/Cyclist_2D_hard_strict: 79.1660, pts_bbox/KITTI/Cyclist_3D_easy_loose: 92.6964, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 92.6964, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.6029, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 79.7127, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 79.7127, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 80.5999, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.7210, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.7210, pts_bbox/KITTI/Cyclist_2D_hard_loose: 79.1660, pts_bbox/KITTI/Car_3D_easy_strict: 88.1640, pts_bbox/KITTI/Car_BEV_easy_strict: 90.1487, pts_bbox/KITTI/Car_2D_easy_strict: 96.5569, pts_bbox/KITTI/Car_3D_moderate_strict: 77.7001, pts_bbox/KITTI/Car_BEV_moderate_strict: 87.7698, pts_bbox/KITTI/Car_2D_moderate_strict: 89.8696, pts_bbox/KITTI/Car_3D_hard_strict: 74.9083, pts_bbox/KITTI/Car_BEV_hard_strict: 85.4721, pts_bbox/KITTI/Car_2D_hard_strict: 89.0056, pts_bbox/KITTI/Car_3D_easy_loose: 96.6491, pts_bbox/KITTI/Car_BEV_easy_loose: 96.7584, pts_bbox/KITTI/Car_2D_easy_loose: 96.5569, pts_bbox/KITTI/Car_3D_moderate_loose: 90.1080, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.1437, pts_bbox/KITTI/Car_2D_moderate_loose: 89.8696, pts_bbox/KITTI/Car_3D_hard_loose: 89.5183, pts_bbox/KITTI/Car_BEV_hard_loose: 89.5933, pts_bbox/KITTI/Car_2D_hard_loose: 89.0056, pts_bbox/KITTI/Overall_3D_easy: 81.2931, pts_bbox/KITTI/Overall_BEV_easy: 84.7260, pts_bbox/KITTI/Overall_2D_easy: 87.9407, pts_bbox/KITTI/Overall_3D_moderate: 70.2078, pts_bbox/KITTI/Overall_BEV_moderate: 76.9261, pts_bbox/KITTI/Overall_2D_moderate: 81.5956, pts_bbox/KITTI/Overall_3D_hard: 66.4451, pts_bbox/KITTI/Overall_BEV_hard: 72.8936, pts_bbox/KITTI/Overall_2D_hard: 80.0744
2021-07-11 20:20:38,022 - mmdet - INFO - Epoch [37][50/232]	lr: 7.344e-05, eta: 0:26:26, time: 2.170, data_time: 0.158, memory: 13390, loss_cls: 0.0698, loss_bbox: 0.2314, loss_dir: 0.0170, loss: 0.3182, grad_norm: 0.7151
2021-07-11 20:22:19,290 - mmdet - INFO - Epoch [37][100/232]	lr: 7.344e-05, eta: 0:24:57, time: 2.025, data_time: 0.021, memory: 13390, loss_cls: 0.0682, loss_bbox: 0.2325, loss_dir: 0.0175, loss: 0.3183, grad_norm: 0.9049
2021-07-11 20:24:00,712 - mmdet - INFO - Epoch [37][150/232]	lr: 7.344e-05, eta: 0:23:27, time: 2.028, data_time: 0.020, memory: 13390, loss_cls: 0.0697, loss_bbox: 0.2317, loss_dir: 0.0181, loss: 0.3195, grad_norm: 0.9145
2021-07-11 20:25:41,439 - mmdet - INFO - Epoch [37][200/232]	lr: 7.344e-05, eta: 0:21:58, time: 2.015, data_time: 0.021, memory: 13390, loss_cls: 0.0701, loss_bbox: 0.2322, loss_dir: 0.0184, loss: 0.3207, grad_norm: 0.6794
2021-07-11 20:28:50,707 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:79.1378, 74.6456, 72.3396
bev  AP:77.2048, 70.4187, 63.9911
3d   AP:70.9244, 63.3600, 59.4411
aos  AP:71.38, 67.16, 64.74
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:79.1378, 74.6456, 72.3396
bev  AP:86.0167, 83.8363, 80.1300
3d   AP:85.9394, 83.6055, 79.0036
aos  AP:71.38, 67.16, 64.74
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.6928, 80.4903, 79.0948
bev  AP:85.0876, 71.6444, 67.7740
3d   AP:81.2134, 67.1237, 62.6172
aos  AP:87.49, 78.17, 76.87
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.6928, 80.4903, 79.0948
bev  AP:87.9327, 79.6268, 76.5590
3d   AP:87.9327, 79.6268, 76.5590
aos  AP:87.49, 78.17, 76.87
Car AP@0.70, 0.70, 0.70:
bbox AP:90.8185, 89.8362, 88.9187
bev  AP:90.0742, 87.7000, 85.5534
3d   AP:88.2613, 77.6332, 74.6586
aos  AP:90.77, 89.40, 88.09
Car AP@0.70, 0.50, 0.50:
bbox AP:90.8185, 89.8362, 88.9187
bev  AP:96.9076, 90.1334, 89.6059
3d   AP:90.8040, 90.0960, 89.5059
aos  AP:90.77, 89.40, 88.09

Overall AP@easy, moderate, hard:
bbox AP:86.2164, 81.6574, 80.1177
bev  AP:84.1222, 76.5877, 72.4395
3d   AP:80.1331, 69.3723, 65.5723
aos  AP:83.21, 78.24, 76.57

2021-07-11 20:28:50,748 - mmdet - INFO - Saving checkpoint at 37 epochs
2021-07-11 20:28:52,039 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 20:28:52,039 - mmdet - INFO - Epoch(val) [37][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 70.9244, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 77.2048, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 79.1378, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 63.3600, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.4187, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 74.6456, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 59.4411, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 63.9911, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 72.3396, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 85.9394, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 86.0167, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 79.1378, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 83.6055, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 83.8363, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 74.6456, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 79.0036, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 80.1300, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 72.3396, pts_bbox/KITTI/Cyclist_3D_easy_strict: 81.2134, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 85.0876, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.6928, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 67.1237, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 71.6444, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 80.4903, pts_bbox/KITTI/Cyclist_3D_hard_strict: 62.6172, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 67.7740, pts_bbox/KITTI/Cyclist_2D_hard_strict: 79.0948, pts_bbox/KITTI/Cyclist_3D_easy_loose: 87.9327, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 87.9327, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.6928, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 79.6268, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 79.6268, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 80.4903, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.5590, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.5590, pts_bbox/KITTI/Cyclist_2D_hard_loose: 79.0948, pts_bbox/KITTI/Car_3D_easy_strict: 88.2613, pts_bbox/KITTI/Car_BEV_easy_strict: 90.0742, pts_bbox/KITTI/Car_2D_easy_strict: 90.8185, pts_bbox/KITTI/Car_3D_moderate_strict: 77.6332, pts_bbox/KITTI/Car_BEV_moderate_strict: 87.7000, pts_bbox/KITTI/Car_2D_moderate_strict: 89.8362, pts_bbox/KITTI/Car_3D_hard_strict: 74.6586, pts_bbox/KITTI/Car_BEV_hard_strict: 85.5534, pts_bbox/KITTI/Car_2D_hard_strict: 88.9187, pts_bbox/KITTI/Car_3D_easy_loose: 90.8040, pts_bbox/KITTI/Car_BEV_easy_loose: 96.9076, pts_bbox/KITTI/Car_2D_easy_loose: 90.8185, pts_bbox/KITTI/Car_3D_moderate_loose: 90.0960, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.1334, pts_bbox/KITTI/Car_2D_moderate_loose: 89.8362, pts_bbox/KITTI/Car_3D_hard_loose: 89.5059, pts_bbox/KITTI/Car_BEV_hard_loose: 89.6059, pts_bbox/KITTI/Car_2D_hard_loose: 88.9187, pts_bbox/KITTI/Overall_3D_easy: 80.1331, pts_bbox/KITTI/Overall_BEV_easy: 84.1222, pts_bbox/KITTI/Overall_2D_easy: 86.2164, pts_bbox/KITTI/Overall_3D_moderate: 69.3723, pts_bbox/KITTI/Overall_BEV_moderate: 76.5877, pts_bbox/KITTI/Overall_2D_moderate: 81.6574, pts_bbox/KITTI/Overall_3D_hard: 65.5723, pts_bbox/KITTI/Overall_BEV_hard: 72.4395, pts_bbox/KITTI/Overall_2D_hard: 80.1177
2021-07-11 20:30:40,255 - mmdet - INFO - Epoch [38][50/232]	lr: 4.147e-05, eta: 0:19:26, time: 2.163, data_time: 0.150, memory: 13390, loss_cls: 0.0695, loss_bbox: 0.2275, loss_dir: 0.0173, loss: 0.3143, grad_norm: 0.9524
2021-07-11 20:32:21,064 - mmdet - INFO - Epoch [38][100/232]	lr: 4.147e-05, eta: 0:17:57, time: 2.016, data_time: 0.021, memory: 13390, loss_cls: 0.0674, loss_bbox: 0.2290, loss_dir: 0.0171, loss: 0.3135, grad_norm: 0.7581
2021-07-11 20:34:01,736 - mmdet - INFO - Epoch [38][150/232]	lr: 4.147e-05, eta: 0:16:27, time: 2.013, data_time: 0.020, memory: 13390, loss_cls: 0.0690, loss_bbox: 0.2314, loss_dir: 0.0173, loss: 0.3178, grad_norm: 0.7445
2021-07-11 20:35:42,906 - mmdet - INFO - Epoch [38][200/232]	lr: 4.147e-05, eta: 0:14:57, time: 2.024, data_time: 0.022, memory: 13390, loss_cls: 0.0698, loss_bbox: 0.2309, loss_dir: 0.0171, loss: 0.3178, grad_norm: 0.7429
2021-07-11 20:38:51,219 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:78.9319, 74.2280, 71.9384
bev  AP:76.8271, 70.0439, 63.5503
3d   AP:70.7463, 63.1007, 58.6751
aos  AP:70.55, 66.31, 63.82
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:78.9319, 74.2280, 71.9384
bev  AP:85.8312, 83.8738, 80.1826
3d   AP:85.7723, 83.7319, 79.6491
aos  AP:70.55, 66.31, 63.82
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.5469, 81.0710, 79.3353
bev  AP:85.8849, 72.4483, 69.0208
3d   AP:82.5676, 68.2557, 63.4109
aos  AP:88.08, 79.30, 77.60
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.5469, 81.0710, 79.3353
bev  AP:87.9643, 79.5952, 76.7632
3d   AP:87.9643, 79.5952, 76.7632
aos  AP:88.08, 79.30, 77.60
Car AP@0.70, 0.70, 0.70:
bbox AP:96.5282, 89.8986, 88.9728
bev  AP:90.1277, 87.7860, 85.6224
3d   AP:88.3463, 77.7534, 74.8406
aos  AP:96.43, 89.49, 88.22
Car AP@0.70, 0.50, 0.50:
bbox AP:96.5282, 89.8986, 88.9728
bev  AP:96.7325, 90.1905, 89.6293
3d   AP:96.6388, 90.1606, 89.5481
aos  AP:96.43, 89.49, 88.22

Overall AP@easy, moderate, hard:
bbox AP:88.0023, 81.7325, 80.0821
bev  AP:84.2799, 76.7594, 72.7312
3d   AP:80.5534, 69.7032, 65.6422
aos  AP:85.02, 78.37, 76.55

2021-07-11 20:38:51,258 - mmdet - INFO - Saving checkpoint at 38 epochs
2021-07-11 20:38:52,585 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 20:38:52,586 - mmdet - INFO - Epoch(val) [38][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 70.7463, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 76.8271, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 78.9319, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 63.1007, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.0439, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 74.2280, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 58.6751, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 63.5503, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 71.9384, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 85.7723, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 85.8312, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 78.9319, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 83.7319, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 83.8738, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 74.2280, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 79.6491, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 80.1826, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 71.9384, pts_bbox/KITTI/Cyclist_3D_easy_strict: 82.5676, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 85.8849, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.5469, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 68.2557, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 72.4483, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 81.0710, pts_bbox/KITTI/Cyclist_3D_hard_strict: 63.4109, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 69.0208, pts_bbox/KITTI/Cyclist_2D_hard_strict: 79.3353, pts_bbox/KITTI/Cyclist_3D_easy_loose: 87.9643, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 87.9643, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.5469, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 79.5952, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 79.5952, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 81.0710, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.7632, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.7632, pts_bbox/KITTI/Cyclist_2D_hard_loose: 79.3353, pts_bbox/KITTI/Car_3D_easy_strict: 88.3463, pts_bbox/KITTI/Car_BEV_easy_strict: 90.1277, pts_bbox/KITTI/Car_2D_easy_strict: 96.5282, pts_bbox/KITTI/Car_3D_moderate_strict: 77.7534, pts_bbox/KITTI/Car_BEV_moderate_strict: 87.7860, pts_bbox/KITTI/Car_2D_moderate_strict: 89.8986, pts_bbox/KITTI/Car_3D_hard_strict: 74.8406, pts_bbox/KITTI/Car_BEV_hard_strict: 85.6224, pts_bbox/KITTI/Car_2D_hard_strict: 88.9728, pts_bbox/KITTI/Car_3D_easy_loose: 96.6388, pts_bbox/KITTI/Car_BEV_easy_loose: 96.7325, pts_bbox/KITTI/Car_2D_easy_loose: 96.5282, pts_bbox/KITTI/Car_3D_moderate_loose: 90.1606, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.1905, pts_bbox/KITTI/Car_2D_moderate_loose: 89.8986, pts_bbox/KITTI/Car_3D_hard_loose: 89.5481, pts_bbox/KITTI/Car_BEV_hard_loose: 89.6293, pts_bbox/KITTI/Car_2D_hard_loose: 88.9728, pts_bbox/KITTI/Overall_3D_easy: 80.5534, pts_bbox/KITTI/Overall_BEV_easy: 84.2799, pts_bbox/KITTI/Overall_2D_easy: 88.0023, pts_bbox/KITTI/Overall_3D_moderate: 69.7032, pts_bbox/KITTI/Overall_BEV_moderate: 76.7594, pts_bbox/KITTI/Overall_2D_moderate: 81.7325, pts_bbox/KITTI/Overall_3D_hard: 65.6422, pts_bbox/KITTI/Overall_BEV_hard: 72.7312, pts_bbox/KITTI/Overall_2D_hard: 80.0821
2021-07-11 20:40:42,169 - mmdet - INFO - Epoch [39][50/232]	lr: 1.850e-05, eta: 0:12:27, time: 2.190, data_time: 0.178, memory: 13390, loss_cls: 0.0675, loss_bbox: 0.2270, loss_dir: 0.0174, loss: 0.3119, grad_norm: 0.7099
2021-07-11 20:42:22,614 - mmdet - INFO - Epoch [39][100/232]	lr: 1.850e-05, eta: 0:10:57, time: 2.009, data_time: 0.023, memory: 13390, loss_cls: 0.0686, loss_bbox: 0.2297, loss_dir: 0.0174, loss: 0.3158, grad_norm: 0.7830
2021-07-11 20:44:04,041 - mmdet - INFO - Epoch [39][150/232]	lr: 1.850e-05, eta: 0:09:27, time: 2.028, data_time: 0.022, memory: 13390, loss_cls: 0.0677, loss_bbox: 0.2303, loss_dir: 0.0174, loss: 0.3154, grad_norm: 0.6968
2021-07-11 20:45:44,303 - mmdet - INFO - Epoch [39][200/232]	lr: 1.850e-05, eta: 0:07:57, time: 2.005, data_time: 0.023, memory: 13390, loss_cls: 0.0733, loss_bbox: 0.2327, loss_dir: 0.0178, loss: 0.3238, grad_norm: 0.6883
2021-07-11 20:48:53,115 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:79.1846, 74.4868, 72.2944
bev  AP:76.9588, 70.1905, 63.5450
3d   AP:71.0305, 63.4542, 59.4091
aos  AP:70.82, 66.62, 64.20
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:79.1846, 74.4868, 72.2944
bev  AP:86.0107, 84.0034, 80.3106
3d   AP:85.9338, 83.8535, 79.6312
aos  AP:70.82, 66.62, 64.20
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.7250, 80.6038, 79.1095
bev  AP:85.5279, 71.7530, 68.3103
3d   AP:82.4995, 67.5842, 63.3616
aos  AP:88.23, 78.79, 77.38
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.7250, 80.6038, 79.1095
bev  AP:88.0252, 79.5808, 76.6183
3d   AP:88.0252, 79.5808, 76.6183
aos  AP:88.23, 78.79, 77.38
Car AP@0.70, 0.70, 0.70:
bbox AP:96.5795, 89.8951, 88.9479
bev  AP:90.1292, 87.7208, 85.5941
3d   AP:88.4770, 77.8351, 74.9077
aos  AP:96.48, 89.47, 88.14
Car AP@0.70, 0.50, 0.50:
bbox AP:96.5795, 89.8951, 88.9479
bev  AP:96.7574, 90.1835, 89.6279
3d   AP:96.6628, 90.1453, 89.5358
aos  AP:96.48, 89.47, 88.14

Overall AP@easy, moderate, hard:
bbox AP:88.1630, 81.6619, 80.1173
bev  AP:84.2053, 76.5548, 72.4831
3d   AP:80.6690, 69.6245, 65.8928
aos  AP:85.18, 78.29, 76.58

2021-07-11 20:48:53,152 - mmdet - INFO - Saving checkpoint at 39 epochs
2021-07-11 20:48:54,540 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 20:48:54,540 - mmdet - INFO - Epoch(val) [39][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 71.0305, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 76.9588, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 79.1846, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 63.4542, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.1905, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 74.4868, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 59.4091, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 63.5450, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 72.2944, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 85.9338, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 86.0107, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 79.1846, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 83.8535, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 84.0034, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 74.4868, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 79.6312, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 80.3106, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 72.2944, pts_bbox/KITTI/Cyclist_3D_easy_strict: 82.4995, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 85.5279, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.7250, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 67.5842, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 71.7530, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 80.6038, pts_bbox/KITTI/Cyclist_3D_hard_strict: 63.3616, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 68.3103, pts_bbox/KITTI/Cyclist_2D_hard_strict: 79.1095, pts_bbox/KITTI/Cyclist_3D_easy_loose: 88.0252, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 88.0252, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.7250, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 79.5808, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 79.5808, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 80.6038, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.6183, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.6183, pts_bbox/KITTI/Cyclist_2D_hard_loose: 79.1095, pts_bbox/KITTI/Car_3D_easy_strict: 88.4770, pts_bbox/KITTI/Car_BEV_easy_strict: 90.1292, pts_bbox/KITTI/Car_2D_easy_strict: 96.5795, pts_bbox/KITTI/Car_3D_moderate_strict: 77.8351, pts_bbox/KITTI/Car_BEV_moderate_strict: 87.7208, pts_bbox/KITTI/Car_2D_moderate_strict: 89.8951, pts_bbox/KITTI/Car_3D_hard_strict: 74.9077, pts_bbox/KITTI/Car_BEV_hard_strict: 85.5941, pts_bbox/KITTI/Car_2D_hard_strict: 88.9479, pts_bbox/KITTI/Car_3D_easy_loose: 96.6628, pts_bbox/KITTI/Car_BEV_easy_loose: 96.7574, pts_bbox/KITTI/Car_2D_easy_loose: 96.5795, pts_bbox/KITTI/Car_3D_moderate_loose: 90.1453, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.1835, pts_bbox/KITTI/Car_2D_moderate_loose: 89.8951, pts_bbox/KITTI/Car_3D_hard_loose: 89.5358, pts_bbox/KITTI/Car_BEV_hard_loose: 89.6279, pts_bbox/KITTI/Car_2D_hard_loose: 88.9479, pts_bbox/KITTI/Overall_3D_easy: 80.6690, pts_bbox/KITTI/Overall_BEV_easy: 84.2053, pts_bbox/KITTI/Overall_2D_easy: 88.1630, pts_bbox/KITTI/Overall_3D_moderate: 69.6245, pts_bbox/KITTI/Overall_BEV_moderate: 76.5548, pts_bbox/KITTI/Overall_2D_moderate: 81.6619, pts_bbox/KITTI/Overall_3D_hard: 65.8928, pts_bbox/KITTI/Overall_BEV_hard: 72.4831, pts_bbox/KITTI/Overall_2D_hard: 80.1173
2021-07-11 20:50:44,542 - mmdet - INFO - Epoch [40][50/232]	lr: 4.654e-06, eta: 0:05:28, time: 2.199, data_time: 0.187, memory: 13390, loss_cls: 0.0682, loss_bbox: 0.2269, loss_dir: 0.0176, loss: 0.3127, grad_norm: 0.9392
2021-07-11 20:52:25,543 - mmdet - INFO - Epoch [40][100/232]	lr: 4.654e-06, eta: 0:03:58, time: 2.020, data_time: 0.022, memory: 13390, loss_cls: 0.0674, loss_bbox: 0.2272, loss_dir: 0.0168, loss: 0.3114, grad_norm: 0.9343
2021-07-11 20:54:06,436 - mmdet - INFO - Epoch [40][150/232]	lr: 4.654e-06, eta: 0:02:28, time: 2.018, data_time: 0.021, memory: 13390, loss_cls: 0.0699, loss_bbox: 0.2278, loss_dir: 0.0170, loss: 0.3146, grad_norm: 0.7153
2021-07-11 20:55:47,540 - mmdet - INFO - Epoch [40][200/232]	lr: 4.654e-06, eta: 0:00:57, time: 2.022, data_time: 0.021, memory: 13390, loss_cls: 0.0700, loss_bbox: 0.2270, loss_dir: 0.0177, loss: 0.3147, grad_norm: 0.7409
2021-07-11 20:58:52,891 - mmdet - INFO - Results of pts_bbox:
Pedestrian AP@0.50, 0.50, 0.50:
bbox AP:79.1596, 74.4311, 72.2691
bev  AP:76.9007, 70.1309, 63.4959
3d   AP:70.9628, 63.1975, 59.1421
aos  AP:70.98, 66.67, 64.31
Pedestrian AP@0.50, 0.25, 0.25:
bbox AP:79.1596, 74.4311, 72.2691
bev  AP:86.0199, 83.9618, 80.4320
3d   AP:85.9304, 83.7613, 79.7796
aos  AP:70.98, 66.67, 64.31
Cyclist AP@0.50, 0.50, 0.50:
bbox AP:88.6391, 80.7563, 79.1608
bev  AP:85.5628, 71.6144, 68.3125
3d   AP:82.5453, 68.5558, 63.4207
aos  AP:88.14, 78.94, 77.40
Cyclist AP@0.50, 0.25, 0.25:
bbox AP:88.6391, 80.7563, 79.1608
bev  AP:88.0248, 79.5318, 76.6065
3d   AP:88.0248, 79.5318, 76.6065
aos  AP:88.14, 78.94, 77.40
Car AP@0.70, 0.70, 0.70:
bbox AP:96.5502, 89.8792, 88.9328
bev  AP:90.1240, 87.8114, 85.5808
3d   AP:88.3785, 77.7759, 74.8716
aos  AP:96.46, 89.47, 88.15
Car AP@0.70, 0.50, 0.50:
bbox AP:96.5502, 89.8792, 88.9328
bev  AP:96.7330, 90.1844, 89.6281
3d   AP:96.6353, 90.1464, 89.5390
aos  AP:96.46, 89.47, 88.15

Overall AP@easy, moderate, hard:
bbox AP:88.1163, 81.6889, 80.1209
bev  AP:84.1958, 76.5189, 72.4630
3d   AP:80.6288, 69.8430, 65.8115
aos  AP:85.19, 78.36, 76.62

2021-07-11 20:58:52,928 - mmdet - INFO - Saving checkpoint at 40 epochs
2021-07-11 20:58:54,257 - mmdet - INFO - Exp name: MVXNET_moca.py
2021-07-11 20:58:54,258 - mmdet - INFO - Epoch(val) [40][232]	pts_bbox/KITTI/Pedestrian_3D_easy_strict: 70.9628, pts_bbox/KITTI/Pedestrian_BEV_easy_strict: 76.9007, pts_bbox/KITTI/Pedestrian_2D_easy_strict: 79.1596, pts_bbox/KITTI/Pedestrian_3D_moderate_strict: 63.1975, pts_bbox/KITTI/Pedestrian_BEV_moderate_strict: 70.1309, pts_bbox/KITTI/Pedestrian_2D_moderate_strict: 74.4311, pts_bbox/KITTI/Pedestrian_3D_hard_strict: 59.1421, pts_bbox/KITTI/Pedestrian_BEV_hard_strict: 63.4959, pts_bbox/KITTI/Pedestrian_2D_hard_strict: 72.2691, pts_bbox/KITTI/Pedestrian_3D_easy_loose: 85.9304, pts_bbox/KITTI/Pedestrian_BEV_easy_loose: 86.0199, pts_bbox/KITTI/Pedestrian_2D_easy_loose: 79.1596, pts_bbox/KITTI/Pedestrian_3D_moderate_loose: 83.7613, pts_bbox/KITTI/Pedestrian_BEV_moderate_loose: 83.9618, pts_bbox/KITTI/Pedestrian_2D_moderate_loose: 74.4311, pts_bbox/KITTI/Pedestrian_3D_hard_loose: 79.7796, pts_bbox/KITTI/Pedestrian_BEV_hard_loose: 80.4320, pts_bbox/KITTI/Pedestrian_2D_hard_loose: 72.2691, pts_bbox/KITTI/Cyclist_3D_easy_strict: 82.5453, pts_bbox/KITTI/Cyclist_BEV_easy_strict: 85.5628, pts_bbox/KITTI/Cyclist_2D_easy_strict: 88.6391, pts_bbox/KITTI/Cyclist_3D_moderate_strict: 68.5558, pts_bbox/KITTI/Cyclist_BEV_moderate_strict: 71.6144, pts_bbox/KITTI/Cyclist_2D_moderate_strict: 80.7563, pts_bbox/KITTI/Cyclist_3D_hard_strict: 63.4207, pts_bbox/KITTI/Cyclist_BEV_hard_strict: 68.3125, pts_bbox/KITTI/Cyclist_2D_hard_strict: 79.1608, pts_bbox/KITTI/Cyclist_3D_easy_loose: 88.0248, pts_bbox/KITTI/Cyclist_BEV_easy_loose: 88.0248, pts_bbox/KITTI/Cyclist_2D_easy_loose: 88.6391, pts_bbox/KITTI/Cyclist_3D_moderate_loose: 79.5318, pts_bbox/KITTI/Cyclist_BEV_moderate_loose: 79.5318, pts_bbox/KITTI/Cyclist_2D_moderate_loose: 80.7563, pts_bbox/KITTI/Cyclist_3D_hard_loose: 76.6065, pts_bbox/KITTI/Cyclist_BEV_hard_loose: 76.6065, pts_bbox/KITTI/Cyclist_2D_hard_loose: 79.1608, pts_bbox/KITTI/Car_3D_easy_strict: 88.3785, pts_bbox/KITTI/Car_BEV_easy_strict: 90.1240, pts_bbox/KITTI/Car_2D_easy_strict: 96.5502, pts_bbox/KITTI/Car_3D_moderate_strict: 77.7759, pts_bbox/KITTI/Car_BEV_moderate_strict: 87.8114, pts_bbox/KITTI/Car_2D_moderate_strict: 89.8792, pts_bbox/KITTI/Car_3D_hard_strict: 74.8716, pts_bbox/KITTI/Car_BEV_hard_strict: 85.5808, pts_bbox/KITTI/Car_2D_hard_strict: 88.9328, pts_bbox/KITTI/Car_3D_easy_loose: 96.6353, pts_bbox/KITTI/Car_BEV_easy_loose: 96.7330, pts_bbox/KITTI/Car_2D_easy_loose: 96.5502, pts_bbox/KITTI/Car_3D_moderate_loose: 90.1464, pts_bbox/KITTI/Car_BEV_moderate_loose: 90.1844, pts_bbox/KITTI/Car_2D_moderate_loose: 89.8792, pts_bbox/KITTI/Car_3D_hard_loose: 89.5390, pts_bbox/KITTI/Car_BEV_hard_loose: 89.6281, pts_bbox/KITTI/Car_2D_hard_loose: 88.9328, pts_bbox/KITTI/Overall_3D_easy: 80.6288, pts_bbox/KITTI/Overall_BEV_easy: 84.1958, pts_bbox/KITTI/Overall_2D_easy: 88.1163, pts_bbox/KITTI/Overall_3D_moderate: 69.8430, pts_bbox/KITTI/Overall_BEV_moderate: 76.5189, pts_bbox/KITTI/Overall_2D_moderate: 81.6889, pts_bbox/KITTI/Overall_3D_hard: 65.8115, pts_bbox/KITTI/Overall_BEV_hard: 72.4630, pts_bbox/KITTI/Overall_2D_hard: 80.1209