How to reproduce the result of ResNet50 version?

[increase24]

I have tried to reproduce the result of replacing the backbone as ResNet50, but I cannot reach the accuracy present in the paper. The reproduced NDS and mAP are 0.3510 and 0.2821 respectively.

Could you provide some tips to train the ResNet50 version or release the correspongding config?

[increase24]

Here is my config of the ResNet50 version. The ResNet50 checkpoint is pretrained on ImageNet. Need it be pretrained on nuScenes with FCOS3D network?

_base_ = [
    '../../../mmdetection3d/configs/_base_/datasets/nus-3d.py',
    '../../../mmdetection3d/configs/_base_/default_runtime.py'
]

plugin=True
plugin_dir='projects/mmdet3d_plugin/'

# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
voxel_size = [0.2, 0.2, 8]

img_norm_cfg = dict(
    #mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
# For nuScenes we usually do 10-class detection
class_names = [
    'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
    'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]

input_modality = dict(
    use_lidar=False,
    use_camera=True,
    use_radar=False,
    use_map=False,
    use_external=False)

model = dict(
    type='Detr3D',
    use_grid_mask=True,
    # img_backbone=dict(
    #     type='ResNet',
    #     depth=50,
    #     num_stages=4,
    #     out_indices=(0, 1, 2, 3),
    #     frozen_stages=1,
    #     norm_cfg=dict(type='BN2d', requires_grad=False),
    #     norm_eval=True,
    #     style='caffe',
    #     dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
    #     stage_with_dcn=(False, False, True, True)),
    pretrained=dict(img='./ckpts/resnet50-19c8e357.pth'),
    img_backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        norm_eval=True,
        style='pytorch'),
    img_neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=4,
        relu_before_extra_convs=True),
    pts_bbox_head=dict(
        type='Detr3DHead',
        num_query=900,
        num_classes=10,
        in_channels=256,
        sync_cls_avg_factor=True,
        with_box_refine=True,
        as_two_stage=False,
        transformer=dict(
            type='Detr3DTransformer',
            decoder=dict(
                type='Detr3DTransformerDecoder',
                num_layers=6,
                return_intermediate=True,
                transformerlayers=dict(
                    type='DetrTransformerDecoderLayer',
                    attn_cfgs=[
                        dict(
                            type='MultiheadAttention',
                            embed_dims=256,
                            num_heads=8,
                            dropout=0.1),
                        dict(
                            type='Detr3DCrossAtten',
                            pc_range=point_cloud_range,
                            num_points=1,
                            embed_dims=256)
                    ],
                    feedforward_channels=512,
                    ffn_dropout=0.1,
                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
                                     'ffn', 'norm')))),
        bbox_coder=dict(
            type='NMSFreeCoder',
            post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
            pc_range=point_cloud_range,
            max_num=300,
            voxel_size=voxel_size,
            num_classes=10), 
        positional_encoding=dict(
            type='SinePositionalEncoding',
            num_feats=128,
            normalize=True,
            offset=-0.5),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=2.0),
        loss_bbox=dict(type='L1Loss', loss_weight=0.25),
        loss_iou=dict(type='GIoULoss', loss_weight=0.0)),
    # model training and testing settings
    train_cfg=dict(pts=dict(
        grid_size=[512, 512, 1],
        voxel_size=voxel_size,
        point_cloud_range=point_cloud_range,
        out_size_factor=4,
        assigner=dict(
            type='HungarianAssigner3D',
            cls_cost=dict(type='FocalLossCost', weight=2.0),
            reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
            iou_cost=dict(type='IoUCost', weight=0.0), # Fake cost. This is just to make it compatible with DETR head. 
            pc_range=point_cloud_range))))

dataset_type = 'CustomNuScenesDataset'
data_root = 'data/nuscenes/'
# file_client_args = dict(backend='disk')
file_client_args = dict(
    backend='petrel',
    path_mapping=dict({
        './data/nuscenes-all/': 's3://nus_bevf/',
        'data/nuscenes-all/': 's3://nus_bevf/',
        './data/nuscenes/': 's3://nus_bevf/',
        'data/nuscenes/': 's3://nus_bevf/',
    }))

db_sampler = dict(
    data_root=data_root,
    info_path=data_root + 'nuscenes_dbinfos_train.pkl',
    rate=1.0,
    prepare=dict(
        filter_by_difficulty=[-1],
        filter_by_min_points=dict(
            car=5,
            truck=5,
            bus=5,
            trailer=5,
            construction_vehicle=5,
            traffic_cone=5,
            barrier=5,
            motorcycle=5,
            bicycle=5,
            pedestrian=5)),
    classes=class_names,
    sample_groups=dict(
        car=2,
        truck=3,
        construction_vehicle=7,
        bus=4,
        trailer=6,
        barrier=2,
        motorcycle=6,
        bicycle=6,
        pedestrian=2,
        traffic_cone=2),
    points_loader=dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=[0, 1, 2, 3, 4],
        file_client_args=file_client_args))

train_pipeline = [
    dict(type='LoadMultiViewImageFromFilesInCeph', to_float32=True, file_client_args=file_client_args),
    dict(type='PhotoMetricDistortionMultiViewImage'),
    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=False),
    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='ObjectNameFilter', classes=class_names),
    dict(type='NormalizeMultiviewImage', **img_norm_cfg),
    dict(type='PadMultiViewImage', size_divisor=32),
    dict(type='DefaultFormatBundle3D', class_names=class_names),
    dict(type='Collect3D', keys=['gt_bboxes_3d', 'gt_labels_3d', 'img'])
]
test_pipeline = [
    dict(type='LoadMultiViewImageFromFilesInCeph', to_float32=True, file_client_args=file_client_args),
    dict(type='NormalizeMultiviewImage', **img_norm_cfg),
    dict(type='PadMultiViewImage', size_divisor=32),
    dict(
        type='MultiScaleFlipAug3D',
        img_scale=(1333, 800),
        pts_scale_ratio=1,
        flip=False,
        transforms=[
            dict(
                type='DefaultFormatBundle3D',
                class_names=class_names,
                with_label=False),
            dict(type='Collect3D', keys=['img'])
        ])
]


data = dict(
    samples_per_gpu=1,
    workers_per_gpu=4,
    train=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=data_root + 'nuscenes_infos_train.pkl',
        pipeline=train_pipeline,
        classes=class_names,
        modality=input_modality,
        test_mode=False,
        use_valid_flag=True,
        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
        box_type_3d='LiDAR',
        file_client_args = file_client_args,),
    val=dict(type=dataset_type, pipeline=test_pipeline, classes=class_names, modality=input_modality, file_client_args = file_client_args,),
    test=dict(type=dataset_type, pipeline=test_pipeline, classes=class_names, modality=input_modality, file_client_args = file_client_args,))

optimizer = dict(
    type='AdamW', 
    lr=2e-4,
    paramwise_cfg=dict(
        custom_keys={
            'img_backbone': dict(lr_mult=0.1),
        }),
    weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    min_lr_ratio=1e-3)
total_epochs = 24
evaluation = dict(interval=2, pipeline=test_pipeline)

runner = dict(type='EpochBasedRunner', max_epochs=total_epochs)
# load_from='ckpts/fcos3d.pth'