feat: 3d backbone exp

configs/mvxnet/mvxnet_deep_fusion.py

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+_base_ = ['./mvxnet_true_pseudolidar.py']
+
+model = dict(
+    pts_backbone=dict(
+        fusion_type='deep',  # Multi-level fusion
+        use_attention=True  # With attention
+    )
+)
+
+# Unique work directory
+work_dir = './work_dirs/mvxnet_deep_fusion'

configs/mvxnet/mvxnet_early_fusion.py

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+_base_ = ['./mvxnet_true_pseudolidar.py']
+
+model = dict(
+    pts_backbone=dict(
+        fusion_type='early',  # Early feature fusion
+        use_attention=True   # With attention
+    )
+)
+
+# Unique work directory
+work_dir = './work_dirs/mvxnet_early_fusion'

configs/mvxnet/mvxnet_fusion.py

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+_base_ = ['../_base_/schedules/cosine.py', '../_base_/default_runtime.py']
+
+# model settings
+voxel_size = [0.05, 0.05, 0.1]
+point_cloud_range = [0, -40, -3, 70.4, 40, 1]
+
+model = dict(
+    type='DynamicMVXFasterRCNN',
+    data_preprocessor=dict(
+        type='Det3DDataPreprocessor',
+        voxel=True,
+        voxel_type='dynamic',
+        voxel_layer=dict(
+            max_num_points=5,  
+            point_cloud_range=[-50, -50, -5, 50, 50, 3],  
+            voxel_size=[0.1, 0.1, 0.2],  
+            max_voxels=(120000, 160000)  
+        ),
+        mean=[102.9801, 115.9465, 122.7717],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False,
+        pad_size_divisor=32),
+    img_backbone=dict(
+        type='mmdet.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='caffe'),
+    img_neck=dict(
+        type='mmdet.FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        # make the image features more stable numerically to avoid loss nan
+        norm_cfg=dict(type='BN', requires_grad=False),
+        num_outs=5),
+    pts_voxel_encoder=dict(
+        type='DynamicVFE',
+        in_channels=4,
+        feat_channels=[64, 64],
+        with_distance=False,
+        voxel_size=voxel_size,
+        with_cluster_center=True,
+        with_voxel_center=True,
+        point_cloud_range=point_cloud_range,
+        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_backbone=dict(
+        type='VoxelNeXt',  # Use VoxelNeXt instead of SECOND
+        in_channels=4,
+        base_channels=64,
+        norm_cfg=dict(type='LN', eps=1e-6)
+    ),
+    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=[[0.8, 0.6, 1.73], [1.76, 0.6, 1.73], [3.9, 1.6, 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='mmdet.FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(
+            type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
+        loss_dir=dict(
+            type='mmdet.CrossEntropyLoss', use_sigmoid=False,
+            loss_weight=0.2)),
+    # model training and testing settings
+    train_cfg=dict(
+        pts=dict(
+            assigner=[
+                dict(  # for Pedestrian
+                    type='Max3DIoUAssigner',
+                    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(  # for Cyclist
+                    type='Max3DIoUAssigner',
+                    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(  # for Car
+                    type='Max3DIoUAssigner',
+                    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 settings
+dataset_type = 'KittiDataset'
+data_root = 'data/kitti/'
+class_names = ['Pedestrian', 'Cyclist', 'Car']
+metainfo = dict(classes=class_names)
+input_modality = dict(use_lidar=True, use_camera=True)
+backend_args = None
+# train_pipeline = [
+#     dict(
+#         type='LoadPointsFromFile',
+#         coord_type='LIDAR',
+#         load_dim=4,
+#         use_dim=4,
+#         backend_args=backend_args),
+#     dict(type='LoadImageFromFile', backend_args=backend_args),
+#     dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+#     dict(
+#         type='RandomResize', scale=[(640, 192), (2560, 768)], 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=point_cloud_range),
+#     dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+#     dict(type='PointShuffle'),
+#     dict(
+#         type='Pack3DDetInputs',
+#         keys=[
+#             'points', 'img', 'gt_bboxes_3d', 'gt_labels_3d', 'gt_bboxes',
+#             'gt_labels'
+#         ])
+# ]
+train_pipeline = [
+    dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
+    dict(type='DynamicVoxelization'),  
+    dict(type='Resize', img_scale=(512, 512), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1280, 384),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            # Temporary solution, fix this after refactor the augtest
+            dict(type='Resize', scale=0, keep_ratio=True),
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(type='RandomFlip3D'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points', 'img'])
+]
+modality = dict(use_lidar=True, use_camera=True)
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            modality=modality,
+            ann_file='kitti_infos_train.pkl',
+            data_prefix=dict(
+                pts='training/velodyne_reduced', img='training/image_2'),
+            pipeline=train_pipeline,
+            filter_empty_gt=False,
+            metainfo=metainfo,
+            # 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',
+            backend_args=backend_args)))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        modality=modality,
+        ann_file='kitti_infos_val.pkl',
+        data_prefix=dict(
+            pts='training/velodyne_reduced', img='training/image_2'),
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        test_mode=True,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='kitti_infos_val.pkl',
+        modality=modality,
+        data_prefix=dict(
+            pts='training/velodyne_reduced', img='training/image_2'),
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        test_mode=True,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+
+optim_wrapper = dict(
+    optimizer=dict(weight_decay=0.01),
+    clip_grad=dict(max_norm=35, norm_type=2),
+)
+val_evaluator = dict(
+    type='KittiMetric', ann_file='data/kitti/kitti_infos_val.pkl')
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+# You may need to download the model first is the network is unstable
+load_from = 'https://download.openmmlab.com/mmdetection3d/pretrain_models/mvx_faster_rcnn_detectron2-caffe_20e_coco-pretrain_gt-sample_kitti-3-class_moderate-79.3_20200207-a4a6a3c7.pth'  # noqa

configs/mvxnet/mvxnet_late_fusion.py

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+_base_ = ['./mvxnet_true_pseudolidar.py']
+
+model = dict(
+    pts_backbone=dict(
+        fusion_type='late',  # Late feature fusion
+        use_attention=True  # With attention
+    )
+)
+
+# Unique work directory
+work_dir = './work_dirs/mvxnet_late_fusion'