[Refactor] Add auto lr in cfg (open-mmlab#1807)

* add deploy.yaml

* add auto_scale_lr in cfg

* fix cfg

Author: VVsssssk

configs/_base_/schedules/cosine.py

@@ -22,3 +22,9 @@
 train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=40, val_interval=1)
 val_cfg = dict(type='ValLoop')
 test_cfg = dict(type='TestLoop')
+
+# 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/_base_/schedules/cyclic-20e.py

@@ -57,3 +57,9 @@
 train_cfg = dict(by_epoch=True, max_epochs=20, val_interval=20)
 val_cfg = dict()
 test_cfg = dict()
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (4 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=32)

configs/_base_/schedules/cyclic-40e.py

@@ -59,3 +59,9 @@
 train_cfg = dict(by_epoch=True, max_epochs=40, val_interval=1)
 val_cfg = dict()
 test_cfg = dict()
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (6 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=48)

configs/_base_/schedules/mmdet-schedule-1x.py

@@ -20,3 +20,9 @@
 optim_wrapper = dict(
     type='OptimWrapper',
     optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
+
+# 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/_base_/schedules/schedule-2x.py

@@ -28,3 +28,9 @@
         milestones=[20, 23],
         gamma=0.1)
 ]
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (4 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=32)

configs/_base_/schedules/schedule-3x.py

@@ -23,3 +23,9 @@
         milestones=[24, 32],
         gamma=0.1)
 ]
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (4 GPUs) x (8 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=32)

configs/_base_/schedules/seg-cosine-100e.py

@@ -19,3 +19,9 @@
 train_cfg = dict(by_epoch=True, max_epochs=100)
 val_cfg = dict(interval=1)
 test_cfg = dict()
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (4 GPUs) x (32 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=128)

configs/_base_/schedules/seg-cosine-150e.py

@@ -19,3 +19,9 @@
 train_cfg = dict(by_epoch=True, max_epochs=150)
 val_cfg = dict(interval=1)
 test_cfg = dict()
+
+# 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)

configs/_base_/schedules/seg-cosine-200e.py

@@ -19,3 +19,9 @@
 train_cfg = dict(by_epoch=True, max_epochs=200)
 val_cfg = dict(interval=1)
 test_cfg = dict()
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (2 GPUs) x (16 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=32)

configs/_base_/schedules/seg-cosine-50e.py

@@ -19,3 +19,9 @@
 train_cfg = dict(by_epoch=True, max_epochs=50)
 val_cfg = dict(interval=1)
 test_cfg = dict()
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (2 GPUs) x (16 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=32)

configs/h3dnet/h3dnet_8xb3_scannet-seg.py

@@ -67,3 +67,8 @@
     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 @@
 
 # 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/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py

@@ -127,3 +127,9 @@
 # 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/point_rcnn/point-rcnn_8xb2_kitti-3d-3class.py

@@ -94,3 +94,8 @@
         dict(type='TensorboardLoggerHook')
     ])
 # yapf:enable
+# 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-range100.py

@@ -3,3 +3,8 @@
     '../_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_/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

@@ -7,3 +7,8 @@
 
 # data settings
 data = dict(train=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

@@ -35,3 +35,8 @@
             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 @@
             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 @@
             ],
             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 @@
             ],
             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 @@
         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 @@
         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_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/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py

@@ -220,3 +220,8 @@
             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 @@
                 [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 @@
                         [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)