[Fix] Fix #460 and simplify configs (#462)

* fix #460 and simplify configs

* fix duplicate key error

* delete unused _delete_

* add cosine docstring and fixed a bug

* revert config files under benchmark folder

* add type to runner in benchmark configs

* remove irrelevant change

configs/3dssd/3dssd_kitti-3d-car.py

@@ -108,7 +108,7 @@ optimizer = dict(type='AdamW', lr=lr, weight_decay=0)
 optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
 lr_config = dict(policy='step', warmup=None, step=[80, 120])
 # runtime settings
-runner = dict(max_epochs=150)
+runner = dict(type='EpochBasedRunner', max_epochs=150)
 
 # yapf:disable
 log_config = dict(

configs/_base_/models/3dssd.py

@@ -75,13 +75,3 @@ model = dict(
         score_thr=0.0,
         per_class_proposal=True,
         max_output_num=100))
-
-# optimizer
-# This schedule is mainly used by models on indoor dataset,
-# e.g., VoteNet on SUNRGBD and ScanNet
-lr = 0.002  # max learning rate
-optimizer = dict(type='AdamW', lr=lr, weight_decay=0)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-lr_config = dict(policy='step', warmup=None, step=[80, 120])
-# runtime settings
-runner = dict(max_epochs=150)

configs/_base_/models/hv_pointpillars_secfpn_kitti.py

 voxel_size = [0.16, 0.16, 4]
+
 model = dict(
     type='VoxelNet',
     voxel_layer=dict(
-        max_num_points=32,
+        max_num_points=32,  # max_points_per_voxel
         point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1],
         voxel_size=voxel_size,
-        max_voxels=(16000, 40000)),
+        max_voxels=(16000, 40000)  # (training, testing) max_voxels
+    ),
     voxel_encoder=dict(
         type='PillarFeatureNet',
         in_channels=4,

configs/_base_/models/hv_second_secfpn_kitti.py

+voxel_size = [0.05, 0.05, 0.1]
+
 model = dict(
     type='VoxelNet',
     voxel_layer=dict(
         max_num_points=5,
         point_cloud_range=[0, -40, -3, 70.4, 40, 1],
-        voxel_size=[0.05, 0.05, 0.1],
+        voxel_size=voxel_size,
         max_voxels=(16000, 40000)),
     voxel_encoder=dict(type='HardSimpleVFE'),
     middle_encoder=dict(

configs/_base_/models/parta2.py

+# model settings
+voxel_size = [0.05, 0.05, 0.1]
+point_cloud_range = [0, -40, -3, 70.4, 40, 1]
+
+model = dict(
+    type='PartA2',
+    voxel_layer=dict(
+        max_num_points=5,  # max_points_per_voxel
+        point_cloud_range=point_cloud_range,
+        voxel_size=voxel_size,
+        max_voxels=(16000, 40000)  # (training, testing) max_voxels
+    ),
+    voxel_encoder=dict(type='HardSimpleVFE'),
+    middle_encoder=dict(
+        type='SparseUNet',
+        in_channels=4,
+        sparse_shape=[41, 1600, 1408],
+        order=('conv', 'norm', 'act')),
+    backbone=dict(
+        type='SECOND',
+        in_channels=256,
+        layer_nums=[5, 5],
+        layer_strides=[1, 2],
+        out_channels=[128, 256]),
+    neck=dict(
+        type='SECONDFPN',
+        in_channels=[128, 256],
+        upsample_strides=[1, 2],
+        out_channels=[256, 256]),
+    rpn_head=dict(
+        type='PartA2RPNHead',
+        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=[[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]],
+            rotations=[0, 1.57],
+            reshape_out=False),
+        diff_rad_by_sin=True,
+        assigner_per_size=True,
+        assign_per_class=True,
+        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=2.0),
+        loss_dir=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
+    roi_head=dict(
+        type='PartAggregationROIHead',
+        num_classes=3,
+        semantic_head=dict(
+            type='PointwiseSemanticHead',
+            in_channels=16,
+            extra_width=0.2,
+            seg_score_thr=0.3,
+            num_classes=3,
+            loss_seg=dict(
+                type='FocalLoss',
+                use_sigmoid=True,
+                reduction='sum',
+                gamma=2.0,
+                alpha=0.25,
+                loss_weight=1.0),
+            loss_part=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+        seg_roi_extractor=dict(
+            type='Single3DRoIAwareExtractor',
+            roi_layer=dict(
+                type='RoIAwarePool3d',
+                out_size=14,
+                max_pts_per_voxel=128,
+                mode='max')),
+        part_roi_extractor=dict(
+            type='Single3DRoIAwareExtractor',
+            roi_layer=dict(
+                type='RoIAwarePool3d',
+                out_size=14,
+                max_pts_per_voxel=128,
+                mode='avg')),
+        bbox_head=dict(
+            type='PartA2BboxHead',
+            num_classes=3,
+            seg_in_channels=16,
+            part_in_channels=4,
+            seg_conv_channels=[64, 64],
+            part_conv_channels=[64, 64],
+            merge_conv_channels=[128, 128],
+            down_conv_channels=[128, 256],
+            bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
+            shared_fc_channels=[256, 512, 512, 512],
+            cls_channels=[256, 256],
+            reg_channels=[256, 256],
+            dropout_ratio=0.1,
+            roi_feat_size=14,
+            with_corner_loss=True,
+            loss_bbox=dict(
+                type='SmoothL1Loss',
+                beta=1.0 / 9.0,
+                reduction='sum',
+                loss_weight=1.0),
+            loss_cls=dict(
+                type='CrossEntropyLoss',
+                use_sigmoid=True,
+                reduction='sum',
+                loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=[
+                dict(  # for Pedestrian
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.35,
+                    min_pos_iou=0.35,
+                    ignore_iof_thr=-1),
+                dict(  # for Cyclist
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.35,
+                    min_pos_iou=0.35,
+                    ignore_iof_thr=-1),
+                dict(  # for Car
+                    type='MaxIoUAssigner',
+                    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),
+        rpn_proposal=dict(
+            nms_pre=9000,
+            nms_post=512,
+            max_num=512,
+            nms_thr=0.8,
+            score_thr=0,
+            use_rotate_nms=False),
+        rcnn=dict(
+            assigner=[
+                dict(  # for Pedestrian
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(
+                        type='BboxOverlaps3D', coordinate='lidar'),
+                    pos_iou_thr=0.55,
+                    neg_iou_thr=0.55,
+                    min_pos_iou=0.55,
+                    ignore_iof_thr=-1),
+                dict(  # for Cyclist
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(
+                        type='BboxOverlaps3D', coordinate='lidar'),
+                    pos_iou_thr=0.55,
+                    neg_iou_thr=0.55,
+                    min_pos_iou=0.55,
+                    ignore_iof_thr=-1),
+                dict(  # for Car
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(
+                        type='BboxOverlaps3D', coordinate='lidar'),
+                    pos_iou_thr=0.55,
+                    neg_iou_thr=0.55,
+                    min_pos_iou=0.55,
+                    ignore_iof_thr=-1)
+            ],
+            sampler=dict(
+                type='IoUNegPiecewiseSampler',
+                num=128,
+                pos_fraction=0.55,
+                neg_piece_fractions=[0.8, 0.2],
+                neg_iou_piece_thrs=[0.55, 0.1],
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False,
+                return_iou=True),
+            cls_pos_thr=0.75,
+            cls_neg_thr=0.25)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1024,
+            nms_post=100,
+            max_num=100,
+            nms_thr=0.7,
+            score_thr=0,
+            use_rotate_nms=True),
+        rcnn=dict(
+            use_rotate_nms=True,
+            use_raw_score=True,
+            nms_thr=0.01,
+            score_thr=0.1)))

configs/_base_/schedules/cosine.py

+# This schedule is mainly used by models with dynamic voxelization
+# optimizer
+lr = 0.003  # max learning rate
+optimizer = dict(
+    type='AdamW',
+    lr=lr,
+    betas=(0.95, 0.99),  # the momentum is change during training
+    weight_decay=0.001)
+optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
+
+lr_config = dict(
+    policy='CosineAnnealing',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 10,
+    min_lr_ratio=1e-5)
+
+momentum_config = None
+
+runner = dict(type='EpochBasedRunner', max_epochs=40)

configs/benchmark/hv_PartA2_secfpn_4x8_cyclic_80e_pcdet_kitti-3d-3class.py

@@ -322,7 +322,7 @@ log_config = dict(
     ])
 # yapf:enable
 # runtime settings
-runner = dict(max_epochs=80)
+runner = dict(type='EpochBasedRunner', max_epochs=80)
 dist_params = dict(backend='nccl', port=29506)
 log_level = 'INFO'
 find_unused_parameters = True

configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py

@@ -192,7 +192,7 @@ log_config = dict(
     ])
 # yapf:enable
 # runtime settings
-runner = dict(max_epochs=50)
+runner = dict(type='EpochBasedRunner', max_epochs=50)
 dist_params = dict(backend='nccl')
 log_level = 'INFO'
 work_dir = './work_dirs/pp_secfpn_100e'

configs/benchmark/hv_pointpillars_secfpn_4x8_80e_pcdet_kitti-3d-3class.py

@@ -235,7 +235,7 @@ log_config = dict(
     ])
 # yapf:enable
 # runtime settings
-runner = dict(max_epochs=80)
+runner = dict(type='EpochBasedRunner', max_epochs=80)
 dist_params = dict(backend='nccl')
 log_level = 'INFO'
 work_dir = './work_dirs/pp_secfpn_80e'

configs/benchmark/hv_second_secfpn_4x8_80e_pcdet_kitti-3d-3class.py

@@ -242,7 +242,7 @@ log_config = dict(
     ])
 # yapf:enable
 # runtime settings
-runner = dict(max_epochs=80)
+runner = dict(type='EpochBasedRunner', max_epochs=80)
 dist_params = dict(backend='nccl')
 log_level = 'INFO'
 work_dir = './work_dirs/sec_secfpn_80e'

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

-_base_ = '../second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py'
+_base_ = [
+    '../_base_/models/hv_second_secfpn_kitti.py',
+    '../_base_/datasets/kitti-3d-3class.py', '../_base_/schedules/cosine.py',
+    '../_base_/default_runtime.py'
+]
 
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]
 voxel_size = [0.05, 0.05, 0.1]
@@ -16,20 +20,3 @@ model = dict(
         type='DynamicSimpleVFE',
         voxel_size=voxel_size,
         point_cloud_range=point_cloud_range))
-
-# optimizer
-lr = 0.003  # max learning rate
-optimizer = dict(
-    _delete_=True,
-    type='AdamW',
-    lr=lr,
-    betas=(0.95, 0.99),  # the momentum is change during training
-    weight_decay=0.001)
-lr_config = dict(
-    _delete_=True,
-    policy='CosineAnnealing',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=1.0 / 10,
-    min_lr_ratio=1e-5)
-momentum_config = None

configs/h3dnet/h3dnet_3x8_scannet-3d-18class.py

@@ -59,12 +59,6 @@ model = dict(
 
 data = dict(samples_per_gpu=3, workers_per_gpu=2)
 
-# optimizer
 # yapf:disable
-log_config = dict(
-    interval=30,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        dict(type='TensorboardLoggerHook')
-    ])
+log_config = dict(interval=30)
 # yapf:enable

configs/imvotenet/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class.py

@@ -53,6 +53,6 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=0.001,
     step=[6])
-runner = dict(max_epochs=8)
+runner = dict(type='EpochBasedRunner', max_epochs=8)
 
 load_from = 'http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'  # noqa

configs/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py

+_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]
@@ -237,33 +239,13 @@ data = dict(
         classes=class_names,
         test_mode=True,
         box_type_3d='LiDAR'))
+
 # Training settings
-optimizer = dict(type='AdamW', lr=0.003, betas=(0.95, 0.99), weight_decay=0.01)
+optimizer = dict(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='CosineAnnealing',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=1.0 / 10,
-    min_lr_ratio=1e-5)
-momentum_config = None
-checkpoint_config = dict(interval=1)
-# yapf:disable
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        dict(type='TensorboardLoggerHook')
-    ])
-# yapf:enable
+
 evaluation = dict(interval=1, pipeline=eval_pipeline)
-# runtime settings
-runner = dict(max_epochs=40)
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-work_dir = None
+
 # 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
-resume_from = None
-workflow = [('train', 1)]

configs/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py

-_base_ = ['../_base_/schedules/cyclic_40e.py', '../_base_/default_runtime.py']
+_base_ = [
+    '../_base_/schedules/cyclic_40e.py', '../_base_/default_runtime.py',
+    '../_base_/models/parta2.py'
+]
 
-# model settings
-voxel_size = [0.05, 0.05, 0.1]
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]
 
-model = dict(
-    type='PartA2',
-    voxel_layer=dict(
-        max_num_points=5,
-        point_cloud_range=point_cloud_range,
-        voxel_size=voxel_size,
-        max_voxels=(16000, 40000)),
-    voxel_encoder=dict(type='HardSimpleVFE'),
-    middle_encoder=dict(
-        type='SparseUNet',
-        in_channels=4,
-        sparse_shape=[41, 1600, 1408],
-        order=('conv', 'norm', 'act')),
-    backbone=dict(
-        type='SECOND',
-        in_channels=256,
-        layer_nums=[5, 5],
-        layer_strides=[1, 2],
-        out_channels=[128, 256]),
-    neck=dict(
-        type='SECONDFPN',
-        in_channels=[128, 256],
-        upsample_strides=[1, 2],
-        out_channels=[256, 256]),
-    rpn_head=dict(
-        type='PartA2RPNHead',
-        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=[[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]],
-            rotations=[0, 1.57],
-            reshape_out=False),
-        diff_rad_by_sin=True,
-        assigner_per_size=True,
-        assign_per_class=True,
-        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=2.0),
-        loss_dir=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
-    roi_head=dict(
-        type='PartAggregationROIHead',
-        num_classes=3,
-        semantic_head=dict(
-            type='PointwiseSemanticHead',
-            in_channels=16,
-            extra_width=0.2,
-            seg_score_thr=0.3,
-            num_classes=3,
-            loss_seg=dict(
-                type='FocalLoss',
-                use_sigmoid=True,
-                reduction='sum',
-                gamma=2.0,
-                alpha=0.25,
-                loss_weight=1.0),
-            loss_part=dict(
-                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
-        seg_roi_extractor=dict(
-            type='Single3DRoIAwareExtractor',
-            roi_layer=dict(
-                type='RoIAwarePool3d',
-                out_size=14,
-                max_pts_per_voxel=128,
-                mode='max')),
-        part_roi_extractor=dict(
-            type='Single3DRoIAwareExtractor',
-            roi_layer=dict(
-                type='RoIAwarePool3d',
-                out_size=14,
-                max_pts_per_voxel=128,
-                mode='avg')),
-        bbox_head=dict(
-            type='PartA2BboxHead',
-            num_classes=3,
-            seg_in_channels=16,
-            part_in_channels=4,
-            seg_conv_channels=[64, 64],
-            part_conv_channels=[64, 64],
-            merge_conv_channels=[128, 128],
-            down_conv_channels=[128, 256],
-            bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
-            shared_fc_channels=[256, 512, 512, 512],
-            cls_channels=[256, 256],
-            reg_channels=[256, 256],
-            dropout_ratio=0.1,
-            roi_feat_size=14,
-            with_corner_loss=True,
-            loss_bbox=dict(
-                type='SmoothL1Loss',
-                beta=1.0 / 9.0,
-                reduction='sum',
-                loss_weight=1.0),
-            loss_cls=dict(
-                type='CrossEntropyLoss',
-                use_sigmoid=True,
-                reduction='sum',
-                loss_weight=1.0))),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            assigner=[
-                dict(  # for Pedestrian
-                    type='MaxIoUAssigner',
-                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                    pos_iou_thr=0.5,
-                    neg_iou_thr=0.35,
-                    min_pos_iou=0.35,
-                    ignore_iof_thr=-1),
-                dict(  # for Cyclist
-                    type='MaxIoUAssigner',
-                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                    pos_iou_thr=0.5,
-                    neg_iou_thr=0.35,
-                    min_pos_iou=0.35,
-                    ignore_iof_thr=-1),
-                dict(  # for Car
-                    type='MaxIoUAssigner',
-                    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),
-        rpn_proposal=dict(
-            nms_pre=9000,
-            nms_post=512,
-            max_num=512,
-            nms_thr=0.8,
-            score_thr=0,
-            use_rotate_nms=False),
-        rcnn=dict(
-            assigner=[
-                dict(  # for Pedestrian
-                    type='MaxIoUAssigner',
-                    iou_calculator=dict(
-                        type='BboxOverlaps3D', coordinate='lidar'),
-                    pos_iou_thr=0.55,
-                    neg_iou_thr=0.55,
-                    min_pos_iou=0.55,
-                    ignore_iof_thr=-1),
-                dict(  # for Cyclist
-                    type='MaxIoUAssigner',
-                    iou_calculator=dict(
-                        type='BboxOverlaps3D', coordinate='lidar'),
-                    pos_iou_thr=0.55,
-                    neg_iou_thr=0.55,
-                    min_pos_iou=0.55,
-                    ignore_iof_thr=-1),
-                dict(  # for Car
-                    type='MaxIoUAssigner',
-                    iou_calculator=dict(
-                        type='BboxOverlaps3D', coordinate='lidar'),
-                    pos_iou_thr=0.55,
-                    neg_iou_thr=0.55,
-                    min_pos_iou=0.55,
-                    ignore_iof_thr=-1)
-            ],
-            sampler=dict(
-                type='IoUNegPiecewiseSampler',
-                num=128,
-                pos_fraction=0.55,
-                neg_piece_fractions=[0.8, 0.2],
-                neg_iou_piece_thrs=[0.55, 0.1],
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False,
-                return_iou=True),
-            cls_pos_thr=0.75,
-            cls_neg_thr=0.25)),
-    test_cfg=dict(
-        rpn=dict(
-            nms_pre=1024,
-            nms_post=100,
-            max_num=100,
-            nms_thr=0.7,
-            score_thr=0,
-            use_rotate_nms=True),
-        rcnn=dict(
-            use_rotate_nms=True,
-            use_raw_score=True,
-            nms_thr=0.01,
-            score_thr=0.1)))
-
 # dataset settings
 dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'

configs/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car.py

 _base_ = './hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py'
 
-voxel_size = [0.05, 0.05, 0.1]
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]  # velodyne coordinates, x, y, z
 
 model = dict(

configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py

@@ -78,9 +78,10 @@ optimizer = dict(lr=lr)
 # development of the codebase thus we keep the setting. But we does not
 # specifically tune this parameter.
 optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# Use evaluation interval=2 reduce the number of evaluation timese
-evaluation = dict(interval=2)
 # PointPillars usually need longer schedule than second, we simply double
 # the training schedule. Do remind that since we use RepeatDataset and
 # repeat factor is 2, so we actually train 160 epochs.
 runner = dict(max_epochs=80)
+
+# Use evaluation interval=2 reduce the number of evaluation timese
+evaluation = dict(interval=2)

configs/votenet/votenet_8x8_scannet-3d-18class.py

@@ -31,12 +31,6 @@ model = dict(
                         [1.1511526, 1.0546296, 0.49706793],
                         [0.47535285, 0.49249494, 0.5802117]])))
 
-# optimizer
 # yapf:disable
-log_config = dict(
-    interval=30,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        dict(type='TensorboardLoggerHook')
-    ])
+log_config = dict(interval=30)
 # yapf:enable

docs/tutorials/config.md

@@ -413,7 +413,7 @@ gpu_ids = range(0, 1)  # ids of gpus
 ### Ignore some fields in the base configs
 
 Sometimes, you may set `_delete_=True` to ignore some of fields in base configs.
-You may refer to [mmcv](https://mmcv.readthedocs.io/en/latest/utils.html#inherit-from-base-config-with-ignored-fields) for simple inllustration.
+You may refer to [mmcv](https://mmcv.readthedocs.io/en/latest/utils.html#inherit-from-base-config-with-ignored-fields) for simple illustration.
 
 In MMDetection or MMDetection3D, for example, to change the FPN neck of PointPillars with the following config.