Fix configs

configs/kitti/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py

@@ -34,7 +34,7 @@ model = dict(
         type='SECONDFPN',
         in_channels=[64, 128, 256],
         upsample_strides=[1, 2, 4],
-        num_upsample_filters=[128, 128, 128],
+        out_channels=[128, 128, 128],
     ),
     bbox_head=dict(
         type='SECONDHead',

configs/kitti/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py

@@ -32,7 +32,7 @@ model = dict(
         type='SECONDFPN',
         in_channels=[128, 256],
         upsample_strides=[1, 2],
-        num_upsample_filters=[256, 256],
+        out_channels=[256, 256],
     ),
     bbox_head=dict(
         type='SECONDHead',

configs/kitti/dv_second_secfpn_6x8_80e_kitti-3d-car.py

@@ -32,7 +32,7 @@ model = dict(
         type='SECONDFPN',
         in_channels=[128, 256],
         upsample_strides=[1, 2],
-        num_upsample_filters=[256, 256],
+        out_channels=[256, 256],
     ),
     bbox_head=dict(
         type='SECONDHead',

configs/kitti/hv_PartA2_secfpn_4x8_cosine_80e_kitti-3d-3class.py

@@ -27,7 +27,7 @@ model = dict(
         type='SECONDFPN',
         in_channels=[128, 256],
         upsample_strides=[1, 2],
-        num_upsample_filters=[256, 256]),
+        out_channels=[256, 256]),
     rpn_head=dict(
         type='PartA2RPNHead',
         class_name=['Pedestrian', 'Cyclist', 'Car'],

configs/kitti/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py

@@ -33,7 +33,7 @@ model = dict(
         type='SECONDFPN',
         in_channels=[64, 128, 256],
         upsample_strides=[1, 2, 4],
-        num_upsample_filters=[128, 128, 128],
+        out_channels=[128, 128, 128],
     ),
     bbox_head=dict(
         type='SECONDHead',

configs/kitti/hv_second_secfpn_6x8_80e_kitti-3d-car.py

@@ -32,7 +32,7 @@ model = dict(
         type='SECONDFPN',
         in_channels=[128, 256],
         upsample_strides=[1, 2],
-        num_upsample_filters=[256, 256],
+        out_channels=[256, 256],
     ),
     bbox_head=dict(
         type='SECONDHead',

configs/nus/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py

+# model settings
+voxel_size = [0.25, 0.25, 8]
+point_cloud_range = [-50, -50, -5, 50, 50, 3]
+class_names = [
+    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
+]
+model = dict(
+    type='MVXFasterRCNNV2',
+    pts_voxel_layer=dict(
+        max_num_points=64,  # max_points_per_voxel
+        point_cloud_range=point_cloud_range,  # velodyne coordinates, x, y, z
+        voxel_size=voxel_size,
+        max_voxels=(30000, 40000),  # (training, testing) max_coxels
+    ),
+    pts_voxel_encoder=dict(
+        type='HardVFE',
+        num_input_features=4,
+        num_filters=[64, 64],
+        with_distance=False,
+        voxel_size=voxel_size,
+        with_cluster_center=True,
+        with_voxel_center=True,
+        point_cloud_range=point_cloud_range,
+        norm_cfg=dict(type='naiveSyncBN1d', eps=1e-3, momentum=0.01)),
+    pts_middle_encoder=dict(
+        type='PointPillarsScatter',
+        in_channels=64,
+        output_shape=[400, 400],  # checked from PointCloud3D
+    ),
+    pts_backbone=dict(
+        type='SECOND',
+        in_channels=64,
+        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
+        layer_nums=[3, 5, 5],
+        layer_strides=[2, 2, 2],
+        num_filters=[64, 128, 256],
+    ),
+    pts_neck=dict(
+        type='FPN',
+        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
+        act_cfg=dict(type='ReLU'),
+        in_channels=[64, 128, 256],
+        out_channels=256,
+        start_level=0,
+        num_outs=3,
+    ),
+    pts_bbox_head=dict(
+        type='Anchor3DVeloHead',
+        class_names=class_names,
+        num_classes=10,
+        in_channels=256,
+        feat_channels=256,
+        use_direction_classifier=True,
+        encode_bg_as_zeros=True,
+        anchor_generator=dict(
+            type='Anchor3DRangeGenerator',
+            ranges=[[-50, -50, -1.80, 50, 50, -1.80]],
+            strides=[1, 2, 4],
+            sizes=[
+                [0.577, 1.732, 1.],
+                [1., 1., 1.],
+                [0.4, 0.4, 1],
+            ],
+            custom_values=[0, 0],
+            rotations=[0, 1.57],
+            reshape_out=True),
+        assigner_per_size=False,
+        diff_rad_by_sin=True,
+        dir_offset=0.7854,  # pi/4
+        dir_limit_offset=0,
+        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', ),
+        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=0.2),
+    ),
+)
+# model training and testing settings
+train_cfg = dict(
+    pts=dict(
+        assigner=dict(  # for Car
+            type='MaxIoUAssigner',
+            iou_calculator=dict(type='BboxOverlapsNearest3D'),
+            pos_iou_thr=0.6,
+            neg_iou_thr=0.3,
+            min_pos_iou=0.3,
+            ignore_iof_thr=-1),
+        allowed_border=0,
+        code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2],
+        pos_weight=-1,
+        debug=False))
+test_cfg = dict(
+    pts=dict(
+        use_rotate_nms=True,
+        nms_across_levels=False,
+        nms_pre=1000,
+        nms_thr=0.2,
+        score_thr=0.05,
+        min_bbox_size=0,
+        max_per_img=500,
+        post_center_limit_range=point_cloud_range,
+        # TODO: check whether need to change this
+        # post_center_limit_range=[-59.6, -59.6, -6, 59.6, 59.6, 4],
+        # soft-nms is also supported for rcnn testing
+        # e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
+    ))
+
+# dataset settings
+dataset_type = 'NuScenesDataset'
+data_root = 'data/nuscenes/'
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+input_modality = dict(
+    use_lidar=True,
+    use_depth=False,
+    use_lidar_intensity=True,
+    use_camera=False,
+)
+db_sampler = dict(
+    root_path=data_root,
+    info_path=data_root + 'nuscenes_dbinfos_train.pkl',
+    rate=1.0,
+    use_road_plane=False,
+    object_rot_range=[0.0, 0.0],
+    prepare=dict(),
+    sample_groups=dict(
+        bus=4,
+        trailer=4,
+        truck=4,
+    ),
+)
+
+train_pipeline = [
+    dict(
+        type='GlobalRotScale',
+        rot_uniform_noise=[-0.3925, 0.3925],
+        scaling_uniform_noise=[0.95, 1.05],
+        trans_normal_noise=[0, 0, 0]),
+    dict(type='RandomFlip3D', flip_ratio=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='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']),
+]
+test_pipeline = [
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='RandomFlip3D', flip_ratio=0),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points']),
+]
+
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        root_path=data_root,
+        ann_file=data_root + 'nuscenes_infos_train.pkl',
+        pipeline=train_pipeline,
+        modality=input_modality,
+        class_names=class_names,
+        with_label=True),
+    val=dict(
+        type=dataset_type,
+        root_path=data_root,
+        ann_file=data_root + 'nuscenes_infos_val.pkl',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        class_names=class_names,
+        with_label=True),
+    test=dict(
+        type=dataset_type,
+        root_path=data_root,
+        ann_file=data_root + 'nuscenes_infos_val.pkl',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        class_names=class_names,
+        with_label=False))
+# optimizer
+optimizer = dict(type='AdamW', lr=0.001, weight_decay=0.01)
+# max_norm=10 is better for SECOND
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[20, 23])
+momentum_config = None
+checkpoint_config = dict(interval=1)
+# yapf:disable
+evaluation = dict(interval=20)
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+# runtime settings
+total_epochs = 24
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]

mmdet3d/models/necks/second_fpn.py

-import copy
-
 import torch
 import torch.nn as nn
 from mmcv.cnn import (build_norm_layer, build_upsample_layer, constant_init,
@@ -36,19 +34,15 @@ class SECONDFPN(nn.Module):
 
         deblocks = []
         for i, out_channel in enumerate(out_channels):
-            norm_layer = build_norm_layer(norm_cfg, out_channel)[1]
-            upsample_cfg_ = copy.deepcopy(upsample_cfg)
-            upsample_cfg_.update(
+            upsample_layer = build_upsample_layer(
+                upsample_cfg,
                 in_channels=in_channels[i],
                 out_channels=out_channel,
-                padding=upsample_strides[i],
+                kernel_size=upsample_strides[i],
                 stride=upsample_strides[i])
-            upsample_layer = build_upsample_layer(upsample_cfg_)
-            deblock = nn.Sequential(
-                upsample_layer,
-                norm_layer,
-                nn.ReLU(inplace=True),
-            )
+            deblock = nn.Sequential(upsample_layer,
+                                    build_norm_layer(norm_cfg, out_channel)[1],
+                                    nn.ReLU(inplace=True))
             deblocks.append(deblock)
         self.deblocks = nn.ModuleList(deblocks)
 

requirements/build.txt

 # These must be installed before building mmdetection
 numpy
-torch>=1.1
+torch>=1.3

requirements/runtime.txt

 matplotlib
-mmcv>=0.5.0
+mmcv>=0.5.1
 numba==0.45.1
 numpy
 # need older pillow until torchvision is fixed