[Feature] Group-Free-3D head

configs/_base_/models/groupfree3d.py

@@ -0,0 +1,47 @@
+model = dict(
+ type='GroupFree3DNet',
+ backbone=dict(
+ type='PointNet2SASSG',
+ in_channels=3,
+ num_points=(2048, 1024, 512, 256),
+ radius=(0.2, 0.4, 0.8, 1.2),
+ num_samples=(64, 32, 16, 16),
+ sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
+ (128, 128, 256)),
+ fp_channels=((256, 256), (256, 288)),
+ norm_cfg=dict(type='BN2d'),
+ sa_cfg=dict(
+ type='PointSAModule',
+ pool_mod='max',
+ use_xyz=True,
+ normalize_xyz=True)),
+ bbox_head=dict(
+ type='GroupFree3DHead',
+ in_channels=288,
+ num_decoder_layers=6,
+ num_proposal=256,
+ transformerlayers=dict(
+ type='BaseTransformerLayer',
+ attn_cfgs=dict(
+ type='GroupFree3DMultiheadAttention',
+ embed_dims=288,
+ num_heads=8,
+ attn_drop=0.1,
+ dropout_layer=dict(type='DropOut', drop_prob=0.1)),
+ ffn_cfgs=dict(
+ embed_dims=288,
+ feedforward_channels=2048,
+ ffn_drop=0.1,
+ act_cfg=dict(type='ReLU', inplace=True)),
+ operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn',
+ 'norm')),
+ pred_layer_cfg=dict(
+ in_channels=288, shared_conv_channels=(288, 288), bias=True)),
+ # model training and testing settings
+ train_cfg=dict(sample_mod='kps'),
+ test_cfg=dict(
+ sample_mod='kps',
+ nms_thr=0.25,
+ score_thr=0.05,
+ per_class_proposal=True,
+ prediction_stages=('_3', '_4', '_5')))

configs/groupfree3d/groupfree3d_16x8_sunrgbd-3d-10class.py

@@ -0,0 +1,164 @@
+_base_ = [
+ '../_base_/datasets/sunrgbd-3d-10class.py',
+ '../_base_/models/groupfree3d.py', '../_base_/schedules/schedule_3x.py',
+ '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+ bbox_head=dict(
+ num_classes=10,
+ size_cls_agnostic=True,
+ bbox_coder=dict(
+ type='GroupFree3DBBoxCoder',
+ num_sizes=10,
+ num_dir_bins=12,
+ with_rot=True,
+ size_cls_agnostic=True,
+ mean_sizes=[
+ [2.114256, 1.620300, 0.927272], [0.791118, 1.279516, 0.718182],
+ [0.923508, 1.867419, 0.845495], [0.591958, 0.552978, 0.827272],
+ [0.699104, 0.454178, 0.75625], [0.69519, 1.346299, 0.736364],
+ [0.528526, 1.002642, 1.172878], [0.500618, 0.632163, 0.683424],
+ [0.404671, 1.071108, 1.688889], [0.76584, 1.398258, 0.472728]
+ ]),
+ sampling_objectness_loss=dict(
+ type='FocalLoss',
+ use_sigmoid=True,
+ gamma=2.0,
+ alpha=0.25,
+ loss_weight=2.0),
+ objectness_loss=dict(
+ type='FocalLoss',
+ use_sigmoid=True,
+ gamma=2.0,
+ alpha=0.25,
+ loss_weight=4.0),
+ center_loss=dict(
+ type='SmoothL1Loss',
+ beta=1.0 / 9.0,
+ reduction='sum',
+ loss_weight=10.0),
+ dir_class_loss=dict(
+ type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
+ dir_res_loss=dict(
+ type='SmoothL1Loss',
+ beta=0.04,
+ reduction='sum',
+ loss_weight=10.0 * 0.04),
+ size_reg_loss=dict(
+ type='SmoothL1Loss',
+ beta=0.0625,
+ reduction='sum',
+ loss_weight=10.0 * 0.0625),
+ semantic_loss=dict(
+ type='CrossEntropyLoss', reduction='sum', loss_weight=1.0)))
+
+dataset_type = 'SUNRGBDDataset'
+data_root = 'data/sunrgbd/'
+class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
+ 'night_stand', 'bookshelf', 'bathtub')
+train_pipeline = [
+ dict(
+ type='LoadPointsFromFile',
+ coord_type='DEPTH',
+ load_dim=6,
+ use_dim=[0, 1, 2]),
+ dict(type='LoadAnnotations3D'),
+ dict(
+ type='RandomFlip3D',
+ sync_2d=False,
+ flip_ratio_bev_horizontal=0.5,
+ ),
+ dict(
+ type='GlobalRotScaleTrans',
+ rot_range=[-0.523599, 0.523599],
+ scale_ratio_range=[0.85, 1.15]),
+ dict(type='IndoorPointSample', num_points=20000),
+ dict(type='DefaultFormatBundle3D', class_names=class_names),
+ dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+ dict(
+ type='LoadPointsFromFile',
+ coord_type='DEPTH',
+ load_dim=6,
+ use_dim=[0, 1, 2]),
+ dict(
+ type='MultiScaleFlipAug3D',
+ img_scale=(1333, 800),
+ pts_scale_ratio=1,
+ flip=False,
+ transforms=[
+ dict(
+ type='GlobalRotScaleTrans',
+ rot_range=[0, 0],
+ scale_ratio_range=[1., 1.],
+ translation_std=[0, 0, 0]),
+ dict(
+ type='RandomFlip3D',
+ sync_2d=False,
+ flip_ratio_bev_horizontal=0.5,
+ ),
+ dict(type='IndoorPointSample', num_points=20000),
+ dict(
+ type='DefaultFormatBundle3D',
+ class_names=class_names,
+ with_label=False),
+ dict(type='Collect3D', keys=['points'])
+ ])
+]
+
+data = dict(
+ samples_per_gpu=16,
+ workers_per_gpu=4,
+ train=dict(
+ type='RepeatDataset',
+ times=5,
+ dataset=dict(
+ type=dataset_type,
+ data_root=data_root,
+ ann_file=data_root + 'sunrgbd_infos_train.pkl',
+ pipeline=train_pipeline,
+ classes=class_names,
+ filter_empty_gt=False,
+ # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+ # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+ box_type_3d='Depth')),
+ val=dict(
+ type=dataset_type,
+ data_root=data_root,
+ ann_file=data_root + 'sunrgbd_infos_val.pkl',
+ pipeline=test_pipeline,
+ classes=class_names,
+ test_mode=True,
+ box_type_3d='Depth'),
+ test=dict(
+ type=dataset_type,
+ data_root=data_root,
+ ann_file=data_root + 'sunrgbd_infos_val.pkl',
+ pipeline=test_pipeline,
+ classes=class_names,
+ test_mode=True,
+ box_type_3d='Depth'))
+
+# optimizer
+lr = 0.004
+optimizer = dict(
+ lr=lr,
+ weight_decay=0.00000001,
+ paramwise_cfg=dict(
+ custom_keys={
+ 'bbox_head.decoder_layers': dict(lr_mult=0.05, decay_mult=1.0),
+ 'bbox_head.decoder_self_posembeds': dict(
+ lr_mult=0.05, decay_mult=1.0),
+ 'bbox_head.decoder_cross_posembeds': dict(
+ lr_mult=0.05, decay_mult=1.0),
+ 'bbox_head.decoder_query_proj': dict(lr_mult=0.05, decay_mult=1.0),
+ 'bbox_head.decoder_key_proj': dict(lr_mult=0.05, decay_mult=1.0)
+ }))
+
+optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
+lr_config = dict(policy='step', warmup=None, step=[84, 96, 108])
+
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=120)