[Feature] Support FCOS3D head (open-mmlab#442)

* Support base mono3d dense head and anchor free mono3d head

* Support FCOS3D head

* Support FCOS3D baseline on nuScenes

* Fix an import error caused by update of mmcv/mmdet

* Change img_scale to scale_factor in the MultiScaleFlipAug in the config

* Add pred_bbox2d in the params of anchor_free_mono3d_head

* Add unit test for fcos3d head

* Fix a minor bug when setting img_metas in the unit test

* Add unit test for fcos3d detector

* Simplify the logic of weights initialization

* Add comments to specify the reason of cloning features

* Update head config

Author: Tai-Wang

configs/_base_/datasets/nus-mono3d.py

@@ -0,0 +1,89 @@
+dataset_type = 'NuScenesMonoDataset'
+data_root = 'data/nuscenes/'
+class_names = [
+    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
+]
+# Input modality for nuScenes dataset, this is consistent with the submission
+# format which requires the information in input_modality.
+input_modality = dict(
+    use_lidar=False,
+    use_camera=True,
+    use_radar=False,
+    use_map=False,
+    use_external=False)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFileMono3D'),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox=True,
+        with_label=True,
+        with_attr_label=True,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    dict(type='Resize', img_scale=(1600, 900), keep_ratio=True),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(
+        type='Collect3D',
+        keys=[
+            'img', 'gt_bboxes', 'gt_labels', 'attr_labels', 'gt_bboxes_3d',
+            'gt_labels_3d', 'centers2d', 'depths'
+        ]),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFileMono3D'),
+    dict(
+        type='MultiScaleFlipAug',
+        scale_factor=1.0,
+        flip=False,
+        transforms=[
+            dict(type='RandomFlip3D'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_train_mono3d.coco.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=train_pipeline,
+        modality=input_modality,
+        test_mode=False,
+        box_type_3d='Camera'),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_val_mono3d.coco.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        box_type_3d='Camera'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_val_mono3d.coco.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        box_type_3d='Camera'))
+evaluation = dict(interval=2)

configs/_base_/models/fcos3d.py

@@ -0,0 +1,75 @@
+model = dict(
+    type='FCOSMono3D',
+    pretrained='open-mmlab://detectron2/resnet101_caffe',
+    backbone=dict(
+        type='ResNet',
+        depth=101,
+        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'),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs=True,
+        extra_convs_on_inputs=False,  # use P5
+        num_outs=5,
+        relu_before_extra_convs=True),
+    bbox_head=dict(
+        type='FCOSMono3DHead',
+        num_classes=10,
+        in_channels=256,
+        stacked_convs=2,
+        feat_channels=256,
+        use_direction_classifier=True,
+        diff_rad_by_sin=True,
+        pred_attrs=True,
+        pred_velo=True,
+        dir_offset=0.7854,  # pi/4
+        strides=[8, 16, 32, 64, 128],
+        group_reg_dims=(2, 1, 3, 1, 2),  # offset, depth, size, rot, velo
+        cls_branch=(256, ),
+        reg_branch=(
+            (256, ),  # offset
+            (256, ),  # depth
+            (256, ),  # size
+            (256, ),  # rot
+            ()  # velo
+        ),
+        dir_branch=(256, ),
+        attr_branch=(256, ),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
+        loss_dir=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+        loss_attr=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        norm_on_bbox=True,
+        centerness_on_reg=True,
+        center_sampling=True,
+        conv_bias=True,
+        dcn_on_last_conv=True),
+    train_cfg=dict(
+        allowed_border=0,
+        code_weight=[1.0, 1.0, 0.2, 1.0, 1.0, 1.0, 1.0, 0.05, 0.05],
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        use_rotate_nms=True,
+        nms_across_levels=False,
+        nms_pre=1000,
+        nms_thr=0.8,
+        score_thr=0.05,
+        min_bbox_size=0,
+        max_per_img=200))

configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py

@@ -0,0 +1,75 @@
+_base_ = [
+    '../_base_/datasets/nus-mono3d.py', '../_base_/models/fcos3d.py',
+    '../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, False, True, True)))
+
+class_names = [
+    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
+]
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFileMono3D'),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox=True,
+        with_label=True,
+        with_attr_label=True,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    dict(type='Resize', img_scale=(1600, 900), keep_ratio=True),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(
+        type='Collect3D',
+        keys=[
+            'img', 'gt_bboxes', 'gt_labels', 'attr_labels', 'gt_bboxes_3d',
+            'gt_labels_3d', 'centers2d', 'depths'
+        ]),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFileMono3D'),
+    dict(
+        type='MultiScaleFlipAug',
+        scale_factor=1.0,
+        flip=False,
+        transforms=[
+            dict(type='RandomFlip3D'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    lr=0.002, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[8, 11])
+total_epochs = 12
+evaluation = dict(interval=2)

mmdet3d/models/dense_heads/__init__.py

@@ -1,6 +1,9 @@
 from .anchor3d_head import Anchor3DHead
+from .anchor_free_mono3d_head import AnchorFreeMono3DHead
 from .base_conv_bbox_head import BaseConvBboxHead
+from .base_mono3d_dense_head import BaseMono3DDenseHead
 from .centerpoint_head import CenterHead
+from .fcos_mono3d_head import FCOSMono3DHead
 from .free_anchor3d_head import FreeAnchor3DHead
 from .parta2_rpn_head import PartA2RPNHead
 from .shape_aware_head import ShapeAwareHead
@@ -9,5 +12,6 @@
 
 __all__ = [
     'Anchor3DHead', 'FreeAnchor3DHead', 'PartA2RPNHead', 'VoteHead',
-    'SSD3DHead', 'BaseConvBboxHead', 'CenterHead', 'ShapeAwareHead'
+    'SSD3DHead', 'BaseConvBboxHead', 'CenterHead', 'ShapeAwareHead',
+    'BaseMono3DDenseHead', 'AnchorFreeMono3DHead', 'FCOSMono3DHead'
 ]