Release v1.1.0rc1

Release v1.1.0rc1

configs/h3dnet/h3dnet_8xb3_scannet-seg.py

@@ -67,3 +67,8 @@ default_hooks = dict(
     logger=dict(type='LoggerHook', interval=30)
 )
 # yapf:enable
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (3 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=24)

configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py

@@ -217,3 +217,8 @@ test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
 
 # may also use your own pre-trained image branch
 load_from = 'https://download.openmmlab.com/mmdetection3d/v0.1.0_models/imvotenet/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class_20210323_173222-cad62aeb.pth'  # noqa
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (16 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=128)

configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py

+_base_ = [
+    '../_base_/schedules/mmdet-schedule-1x.py', '../_base_/default_runtime.py'
+]
+
 model = dict(
     type='ImVoxelNet',
     data_preprocessor=dict(
@@ -151,7 +155,8 @@ test_evaluator = val_evaluator
 # optimizer
 optim_wrapper = dict(
     type='OptimWrapper',
-    optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001),
+    optimizer=dict(
+        _delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001),
     paramwise_cfg=dict(
         custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}),
     clip_grad=dict(max_norm=35., norm_type=2))
@@ -166,30 +171,7 @@ param_scheduler = [
 ]
 
 # hooks
-default_hooks = dict(
-    timer=dict(type='IterTimerHook'),
-    logger=dict(type='LoggerHook', interval=50),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=1),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-)
-
-# training schedule for 2x
-train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
-val_cfg = dict(type='ValLoop')
-test_cfg = dict(type='TestLoop')
+default_hooks = dict(checkpoint=dict(type='CheckpointHook', max_keep_ckpts=1))
 
 # runtime
-default_scope = 'mmdet3d'
-
-env_cfg = dict(
-    cudnn_benchmark=False,
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
-    dist_cfg=dict(backend='nccl'),
-)
-
-log_level = 'INFO'
-load_from = None
-resume = False
-dist_params = dict(backend='nccl')
 find_unused_parameters = True  # only 1 of 4 FPN outputs is used

configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py

@@ -127,3 +127,9 @@ test_evaluator = val_evaluator
 # Part-A2 uses a different learning rate from what SECOND uses.
 optim_wrapper = dict(optimizer=dict(lr=0.001))
 find_unused_parameters = True
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

configs/pgd/pgd_r101-caffe_fpn_head-gn_4xb3-4x_kitti-mono3d.py

@@ -113,7 +113,7 @@ val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
 
 # optimizer
 optim_wrapper = dict(
-    optimizer=dict(lr=0.01),
+    optimizer=dict(lr=0.001),
     paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.),
     clip_grad=dict(max_norm=35, norm_type=2))
 
@@ -134,4 +134,4 @@ param_scheduler = [
         gamma=0.1)
 ]
 
-train_cfg = dict(max_epochs=48)
+train_cfg = dict(max_epochs=48, val_interval=2)

configs/point_rcnn/point-rcnn_8xb2_kitti-3d-3class.py

@@ -6,7 +6,8 @@ _base_ = [
 # dataset settings
 dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
-class_names = ['Car', 'Pedestrian', 'Cyclist']
+class_names = ['Pedestrian', 'Cyclist', 'Car']
+metainfo = dict(CLASSES=class_names)
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]
 input_modality = dict(use_lidar=True, use_camera=False)
 
@@ -42,8 +43,9 @@ train_pipeline = [
     dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='PointSample', num_points=16384, sample_range=40.0),
     dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
 ]
 test_pipeline = [
     dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
@@ -61,36 +63,67 @@ test_pipeline = [
             dict(type='RandomFlip3D'),
             dict(
                 type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(type='PointSample', num_points=16384, sample_range=40.0),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
+            dict(type='PointSample', num_points=16384, sample_range=40.0)
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
-
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    dataset=dict(
         type='RepeatDataset',
         times=2,
-        dataset=dict(pipeline=train_pipeline, classes=class_names)),
-    val=dict(pipeline=test_pipeline, classes=class_names),
-    test=dict(pipeline=test_pipeline, classes=class_names))
+        dataset=dict(pipeline=train_pipeline, metainfo=metainfo)))
+test_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
 
-# optimizer
 lr = 0.001  # max learning rate
-optimizer = dict(lr=lr, betas=(0.95, 0.85))
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=80)
-evaluation = dict(interval=2)
-# yapf:disable
-log_config = dict(
-    interval=30,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        dict(type='TensorboardLoggerHook')
-    ])
-# yapf:enable
+optim_wrapper = dict(optimizer=dict(lr=lr, betas=(0.95, 0.85)))
+train_cfg = dict(by_epoch=True, max_epochs=80, val_interval=2)
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+param_scheduler = [
+    # learning rate scheduler
+    # During the first 35 epochs, learning rate increases from 0 to lr * 10
+    # during the next 45 epochs, learning rate decreases from lr * 10 to
+    # lr * 1e-4
+    dict(
+        type='CosineAnnealingLR',
+        T_max=35,
+        eta_min=lr * 10,
+        begin=0,
+        end=35,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    dict(
+        type='CosineAnnealingLR',
+        T_max=45,
+        eta_min=lr * 1e-4,
+        begin=35,
+        end=80,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    # momentum scheduler
+    # During the first 35 epochs, momentum increases from 0 to 0.85 / 0.95
+    # during the next 45 epochs, momentum increases from 0.85 / 0.95 to 1
+    dict(
+        type='CosineAnnealingMomentum',
+        T_max=35,
+        eta_min=0.85 / 0.95,
+        begin=0,
+        end=35,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    dict(
+        type='CosineAnnealingMomentum',
+        T_max=45,
+        eta_min=1,
+        begin=35,
+        end=80,
+        by_epoch=True,
+        convert_to_iter_based=True)
+]

configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d-range100.py

@@ -3,3 +3,8 @@ _base_ = [
     '../_base_/datasets/lyft-3d-range100.py',
     '../_base_/schedules/schedule-2x.py', '../_base_/default_runtime.py'
 ]
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py

@@ -3,3 +3,8 @@ _base_ = [
     '../_base_/datasets/lyft-3d.py', '../_base_/schedules/schedule-2x.py',
     '../_base_/default_runtime.py'
 ]
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymo-3d-3class.py

@@ -6,4 +6,9 @@ _base_ = [
 ]
 
 # data settings
-data = dict(train=dict(dataset=dict(load_interval=1)))
+train_dataloader = dict(dataset=dict(dataset=dict(load_interval=1)))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (16 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=32)

configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymo-3d-car.py

@@ -6,7 +6,7 @@ _base_ = [
 ]
 
 # data settings
-data = dict(train=dict(dataset=dict(load_interval=1)))
+train_dataloader = dict(dataset=dict(dataset=dict(load_interval=1)))
 
 # model settings
 model = dict(
@@ -35,3 +35,8 @@ model = dict(
             code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
             pos_weight=-1,
             debug=False)))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (16 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=32)

configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-car.py

@@ -32,3 +32,8 @@ model = dict(
             code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
             pos_weight=-1,
             debug=False)))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (16 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=32)

configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb2-2x_lyft-3d-range100.py

@@ -40,3 +40,8 @@ model = dict(
             ],
             rotations=[0, 1.57],
             reshape_out=True)))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb2-2x_lyft-3d.py

@@ -41,3 +41,8 @@ model = dict(
             ],
             rotations=[0, 1.57],
             reshape_out=True)))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

configs/regnet/pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb2-2x_lyft-3d.py

@@ -22,3 +22,8 @@ model = dict(
         norm_eval=False,
         style='pytorch'),
     pts_neck=dict(in_channels=[64, 160, 384]))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

configs/regnet/pointpillars_hv_regnet-400mf_fpn_sbn-all_range100_8xb2-2x_lyft-3d.py

@@ -22,3 +22,8 @@ model = dict(
         norm_eval=False,
         style='pytorch'),
     pts_neck=dict(in_channels=[64, 160, 384]))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

configs/second/second_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-3class.py

@@ -140,3 +140,8 @@ test_dataloader = dict(
         test_mode=True,
         metainfo=metainfo,
         box_type_3d='LiDAR'))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (16 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=32)

configs/smoke/smoke_dla34_dlaneck_gn-all_4xb8-6x_kitti-mono3d.py

@@ -47,8 +47,10 @@ train_dataloader = dict(
 test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
 val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
 
-# training schedule for 1x
-train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
+# training schedule for 6x
+max_epochs = 72
+train_cfg = dict(
+    type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=5)
 val_cfg = dict(type='ValLoop')
 test_cfg = dict(type='TestLoop')
 
@@ -57,9 +59,9 @@ param_scheduler = [
     dict(
         type='MultiStepLR',
         begin=0,
-        end=12,
+        end=max_epochs,
         by_epoch=True,
-        milestones=[8, 11],
+        milestones=[50],
         gamma=0.1)
 ]
 
@@ -68,3 +70,5 @@ optim_wrapper = dict(
     type='OptimWrapper',
     optimizer=dict(type='Adam', lr=2.5e-4),
     clip_grad=None)
+
+find_unused_parameters = True

configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py

@@ -220,3 +220,8 @@ model = dict(
             code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
             pos_weight=-1,
             debug=False)))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (16 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=32)

configs/votenet/votenet_8xb16_sunrgbd-3d.py

@@ -20,3 +20,8 @@ model = dict(
                 [0.404671, 1.071108, 1.688889], [0.76584, 1.398258, 0.472728]
             ]),
     ))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (16 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=128)

configs/votenet/votenet_8xb8_scannet-3d.py

@@ -32,3 +32,8 @@ model = dict(
                         [0.47535285, 0.49249494, 0.5802117]])))
 
 default_hooks = dict(logger=dict(type='LoggerHook', interval=30))
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (8 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=64)