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projects/DETR3D/configs/detr3d_r50_bert_gridmask_halfdata_decoder.py
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|---|---|---|
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| _base_ = [ | ||
| # 'mmdet3d::_base_/datasets/nus-3d.py', | ||
| 'mmdet3d::_base_/default_runtime.py' | ||
| ] | ||
|
|
||
| custom_imports = dict(imports=['projects.DETR3D.detr3d']) | ||
| # 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], bgr_to_rgb=False) | ||
| # For nuScenes we usually do 10-class detection | ||
| class_names = [ | ||
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', | ||
| 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' | ||
| ] | ||
| lang_model_name = 'bert-base-uncased' | ||
| input_modality = dict( | ||
| use_lidar=False, | ||
| use_camera=True, | ||
| use_radar=False, | ||
| use_map=False, | ||
| use_external=False) | ||
| # this means type='DETR3D' will be processed as 'mmdet3d.DETR3D' | ||
| default_scope = 'mmdet3d' | ||
| model = dict( | ||
| type='DETR3D', | ||
| use_grid_mask=True, | ||
| data_preprocessor=dict( | ||
| type='Det3DDataPreprocessor', **img_norm_cfg, pad_size_divisor=32), | ||
| language_model=dict( | ||
| type='BertModel', | ||
| name=lang_model_name, | ||
| pad_to_max=False, | ||
| use_sub_sentence_represent=True, | ||
| special_tokens_list=['[CLS]', '[SEP]', '.', '?'], | ||
| add_pooling_layer=False, | ||
| ), | ||
| encoder=dict( | ||
| num_layers=6, | ||
| num_cp=6, | ||
| # visual layer config | ||
| layer_cfg=dict( | ||
| self_attn_cfg=dict(embed_dims=256, num_levels=4, dropout=0.0), | ||
| ffn_cfg=dict( | ||
| embed_dims=256, feedforward_channels=2048, ffn_drop=0.0)), | ||
| # text layer config | ||
| text_layer_cfg=dict( | ||
| self_attn_cfg=dict(num_heads=4, embed_dims=256, dropout=0.0), | ||
| ffn_cfg=dict( | ||
| embed_dims=256, feedforward_channels=1024, ffn_drop=0.0)), | ||
| # fusion layer config | ||
| fusion_layer_cfg=dict( | ||
| v_dim=256, | ||
| l_dim=256, | ||
| embed_dim=1024, | ||
| num_heads=4, | ||
| init_values=1e-4), | ||
| ), | ||
| positional_encoding_single=dict( | ||
| num_feats=128, | ||
| normalize=True, | ||
| temperature=20, | ||
| offset=0.0), | ||
| img_backbone=dict( | ||
| type='mmdet.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)), | ||
| img_neck=dict( | ||
| type='mmdet.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='mmdet.DetrTransformerDecoderLayer', | ||
| attn_cfgs=[ | ||
| dict( | ||
| type='MultiheadAttention', # mmcv. | ||
| embed_dims=256, | ||
| num_heads=8, | ||
| dropout=0.1), | ||
| dict( | ||
| type='MultiheadAttention', # mmcv. | ||
| embed_dims=256, | ||
| num_heads=8, | ||
| dropout=0.0), | ||
| 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','self_attn_text','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='mmdet.SinePositionalEncoding', | ||
| num_feats=128, | ||
| normalize=True, | ||
| offset=-0.5), | ||
| loss_cls=dict( | ||
| type='mmdet.FocalLoss', | ||
| use_sigmoid=True, | ||
| gamma=2.0, | ||
| alpha=0.25, | ||
| loss_weight=2.0), | ||
| loss_bbox=dict(type='mmdet.L1Loss', loss_weight=0.25), | ||
| loss_iou=dict(type='mmdet.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='mmdet.FocalLossCost', weight=2.0), | ||
| reg_cost=dict(type='BBox3DL1Cost', weight=0.25), | ||
| # ↓ Fake cost. This is just to get compatible with DETR head | ||
| iou_cost=dict(type='mmdet.IoUCost', weight=0.0), | ||
| pc_range=point_cloud_range)))) | ||
|
|
||
| dataset_type = 'NuScenesDataset' | ||
| data_root = 'data/nuscenes/' | ||
|
|
||
| test_transforms = [ | ||
| dict( | ||
| type='RandomResize3D', | ||
| scale=(1600, 900), | ||
| ratio_range=(1., 1.), | ||
| keep_ratio=True) | ||
| ] | ||
| train_transforms = [dict(type='PhotoMetricDistortion3D')] + test_transforms | ||
|
|
||
| backend_args = None | ||
| train_pipeline = [ | ||
| dict( | ||
| type='LoadMultiViewImageFromFiles', | ||
| to_float32=True, | ||
| num_views=6, | ||
| backend_args=backend_args), | ||
| dict( | ||
| type='LoadAnnotations3D', | ||
| with_bbox_3d=True, | ||
| with_label_3d=True, | ||
| with_attr_label=False), | ||
| dict(type='MultiViewWrapper', transforms=train_transforms), | ||
| dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range), | ||
| dict(type='ObjectNameFilter', classes=class_names), | ||
| dict(type='Pack3DDetInputs', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d']) | ||
| ] | ||
|
|
||
| test_pipeline = [ | ||
| dict( | ||
| type='LoadMultiViewImageFromFiles', | ||
| to_float32=True, | ||
| num_views=6, | ||
| backend_args=backend_args), | ||
| dict(type='MultiViewWrapper', transforms=test_transforms), | ||
| dict(type='Pack3DDetInputs', keys=['img']) | ||
| ] | ||
|
|
||
| metainfo = dict(classes=class_names) | ||
| data_prefix = dict( | ||
| pts='', | ||
| CAM_FRONT='samples/CAM_FRONT', | ||
| CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT', | ||
| CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT', | ||
| CAM_BACK='samples/CAM_BACK', | ||
| CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT', | ||
| CAM_BACK_LEFT='samples/CAM_BACK_LEFT') | ||
|
|
||
| train_dataloader = dict( | ||
| batch_size=1, | ||
| num_workers=4, | ||
| persistent_workers=True, | ||
| drop_last=False, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file='nuscenes_infos_train.pkl', | ||
| pipeline=train_pipeline, | ||
| load_type='frame_based', | ||
| metainfo=metainfo, | ||
| modality=input_modality, | ||
| test_mode=False, | ||
| data_prefix=data_prefix, | ||
| # 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', | ||
| load_interval = 2, | ||
| backend_args=backend_args)) | ||
|
|
||
| val_dataloader = dict( | ||
| batch_size=1, | ||
| num_workers=4, | ||
| persistent_workers=True, | ||
| drop_last=False, | ||
| sampler=dict(type='DefaultSampler', shuffle=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file='nuscenes_infos_val.pkl', | ||
| load_type='frame_based', | ||
| pipeline=test_pipeline, | ||
| metainfo=metainfo, | ||
| modality=input_modality, | ||
| test_mode=True, | ||
| data_prefix=data_prefix, | ||
| box_type_3d='LiDAR', | ||
| backend_args=backend_args)) | ||
|
|
||
| test_dataloader = val_dataloader | ||
|
|
||
| val_evaluator = dict( | ||
| type='NuScenesMetric', | ||
| data_root=data_root, | ||
| ann_file=data_root + 'nuscenes_infos_val.pkl', | ||
| metric='bbox', | ||
| backend_args=backend_args) | ||
| test_evaluator = val_evaluator | ||
|
|
||
| optim_wrapper = dict( | ||
| type='OptimWrapper', | ||
| optimizer=dict(type='AdamW', lr=2e-4, weight_decay=0.01), | ||
| paramwise_cfg=dict(custom_keys={'img_backbone': dict(lr_mult=0.1)}), | ||
| clip_grad=dict(max_norm=35, norm_type=2), | ||
| ) | ||
|
|
||
| # learning policy | ||
| param_scheduler = [ | ||
| dict( | ||
| type='LinearLR', | ||
| start_factor=1.0 / 3, | ||
| by_epoch=False, | ||
| begin=0, | ||
| end=500), | ||
| dict( | ||
| type='CosineAnnealingLR', | ||
| by_epoch=True, | ||
| begin=0, | ||
| end=24, | ||
| T_max=24, | ||
| eta_min_ratio=1e-3) | ||
| ] | ||
|
|
||
| total_epochs = 24 | ||
|
|
||
| train_cfg = dict( | ||
| type='EpochBasedTrainLoop', max_epochs=total_epochs, val_interval=2) | ||
| val_cfg = dict(type='ValLoop') | ||
| test_cfg = dict(type='TestLoop') | ||
| default_hooks = dict( | ||
| checkpoint=dict( | ||
| type='CheckpointHook', interval=1, max_keep_ckpts=1, save_last=True)) | ||
| load_from = 'pretrained/fcos3d.pth' | ||
|
|
||
| # setuptools 65 downgrades to 58. | ||
| # In mmlab-node we use setuptools 61 but occurs NO errors | ||
| vis_backends = [dict(type='TensorboardVisBackend')] | ||
| visualizer = dict( | ||
| type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer') |
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