[Feature]: support to customize type of runner (#4570)

* sup customize runner

* fix typo

* use build runner

* minor fix

* modify according to comments

* add an assertion

* swap order

* fix tutorial

configs/_base_/schedules/schedule_1x.py

@@ -8,4 +8,4 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=0.001,
     step=[8, 11])
-total_epochs = 12
+runner = dict(type='EpochBasedRunner', max_epochs=12)

configs/_base_/schedules/schedule_20e.py

@@ -8,4 +8,4 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=0.001,
     step=[16, 19])
-total_epochs = 20
+runner = dict(type='EpochBasedRunner', max_epochs=20)

configs/_base_/schedules/schedule_2x.py

@@ -8,4 +8,4 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=0.001,
     step=[16, 22])
-total_epochs = 24
+runner = dict(type='EpochBasedRunner', max_epochs=24)

configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py

@@ -1,4 +1,4 @@
 _base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py

@@ -102,4 +102,4 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=1.0 / 3,
     step=[190])
-total_epochs = 210
+runner = dict(max_epochs=210)

configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py

@@ -32,7 +32,7 @@ lr_config = dict(
     warmup_ratio=0.001,
     # [7] yields higher performance than [6]
     step=[7])
-total_epochs = 8  # actual epoch = 8 * 8 = 64
+runner = dict(max_epochs=8)  # actual epoch = 8 * 8 = 64
 log_config = dict(interval=100)
 # For better, more stable performance initialize from COCO
 load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'  # noqa

configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py

@@ -39,7 +39,7 @@ lr_config = dict(
     warmup_ratio=0.001,
     # [7] yields higher performance than [6]
     step=[7])
-total_epochs = 8  # actual epoch = 8 * 8 = 64
+runner = dict(max_epochs=8)  # actual epoch = 8 * 8 = 64
 log_config = dict(interval=100)
 # For better, more stable performance initialize from COCO
 load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth'  # noqa

configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py

@@ -102,4 +102,4 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=1.0 / 3,
     step=[180])
-total_epochs = 210
+runner = dict(max_epochs=210)

configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py

@@ -102,4 +102,4 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=1.0 / 3,
     step=[180])
-total_epochs = 210
+runner = dict(max_epochs=210)

configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py

@@ -102,4 +102,4 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=1.0 / 3,
     step=[180])
-total_epochs = 210
+runner = dict(max_epochs=210)

configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py

@@ -7,4 +7,4 @@ model = dict(
     roi_head=dict(
         bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15)))
 # runtime settings
-total_epochs = 15
+runner = dict(max_epochs=15)

configs/detr/detr_r50_8x2_150e_coco.py

@@ -128,4 +128,4 @@ optimizer = dict(
 optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
 # learning policy
 lr_config = dict(policy='step', step=[100])
-total_epochs = 150
+runner = dict(max_epochs=150)

configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py

@@ -2,4 +2,4 @@ _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
 
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py'
 # learning policy
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 23])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
 # learning policy
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py

@@ -0,0 +1,15 @@
+_base_ = 'faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[60000, 80000])
+
+# Runner type
+runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
+
+checkpoint_config = dict(interval=10000)
+evaluation = dict(interval=10000, metric='bbox')

configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py

@@ -41,4 +41,4 @@ data = dict(
     test=dict(pipeline=test_pipeline))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py

@@ -102,4 +102,4 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=1.0 / 3,
     step=[8, 11])
-total_epochs = 12
+runner = dict(max_epochs=12)

configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py

@@ -36,4 +36,4 @@ data = dict(
     test=dict(pipeline=test_pipeline))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py

@@ -56,4 +56,4 @@ optimizer_config = dict(
     _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py

@@ -7,4 +7,4 @@ model = dict(
         norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py

@@ -24,4 +24,4 @@ train_pipeline = [
 data = dict(train=dict(pipeline=train_pipeline))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py

@@ -5,6 +5,6 @@ model = dict(
         norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)
 optimizer_config = dict(
     _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py

@@ -22,4 +22,4 @@ train_pipeline = [
 data = dict(train=dict(pipeline=train_pipeline))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './fovea_r50_fpn_4x4_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py

@@ -1,7 +1,7 @@
 _base_ = './gfl_r50_fpn_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)
 # multi-scale training
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py

@@ -1,4 +1,4 @@
 _base_ = './mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py'
 # learning policy
 lr_config = dict(step=[20, 23])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py

@@ -1,4 +1,4 @@
 _base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
 # learning policy
 lr_config = dict(step=[20, 23])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py

@@ -14,4 +14,4 @@ model = dict(
         mask_head=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg)))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py

@@ -1,4 +1,4 @@
 _base_ = './mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py'
 # learning policy
 lr_config = dict(step=[20, 23])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py

@@ -1,4 +1,4 @@
 _base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py'
 # learning policy
 lr_config = dict(step=[20, 23])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py

@@ -2,4 +2,4 @@ _base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py'
 
 # learning policy
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py

@@ -43,4 +43,4 @@ data = dict(
     test=dict(pipeline=test_pipeline))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py

@@ -2,4 +2,4 @@ _base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py'
 
 # learning policy
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py

@@ -12,4 +12,4 @@ model = dict(
         mask_head=dict(norm_cfg=norm_cfg)))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py

@@ -2,4 +2,4 @@ _base_ = './mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py'
 
 # learning policy
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py

@@ -8,4 +8,4 @@ lr_config = dict(
     step=[8, 11])
 checkpoint_config = dict(interval=1)
 # runtime settings
-total_epochs = 12
+runner = dict(max_epochs=12)

configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py

@@ -132,4 +132,4 @@ lr_config = dict(
     warmup_iters=3665,
     warmup_ratio=1.0 / 80,
     step=[17, 23])
-total_epochs = 25
+runner = dict(max_epochs=25)

configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py

@@ -20,4 +20,4 @@ lr_config = dict(
     warmup_iters=3665,
     warmup_ratio=1.0 / 80,
     step=[17, 23])
-total_epochs = 25
+runner = dict(max_epochs=25)

configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py

@@ -1,3 +1,5 @@
+_base_ = '../_base_/default_runtime.py'
+
 # model settings
 model = dict(
     type='RetinaNet',
@@ -162,10 +164,4 @@ log_config = dict(
     ])
 # yapf:enable
 # runtime settings
-total_epochs = 24
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-work_dir = './work_dirs/ga_retinanet_r101_caffe_fpn_mstrain_2x'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
+runner = dict(max_epochs=24)

configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py

@@ -36,4 +36,4 @@ model = dict(
         out_channels=256))
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py

@@ -36,4 +36,4 @@ model = dict(
         out_channels=256))
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py

@@ -2,4 +2,4 @@ _base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py'
 
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py

@@ -36,4 +36,4 @@ data = dict(
     test=dict(pipeline=test_pipeline))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/hrnet/htc_hrnetv2p_w40_28e_coco.py

@@ -1,4 +1,4 @@
 _base_ = './htc_hrnetv2p_w40_20e_coco.py'
 # learning policy
 lr_config = dict(step=[24, 27])
-total_epochs = 28
+runner = dict(max_epochs=28)

configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py

@@ -1,4 +1,4 @@
 _base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py'
 # learning policy
 lr_config = dict(step=[24, 27])
-total_epochs = 28
+runner = dict(max_epochs=28)

configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/htc/htc_r101_fpn_20e_coco.py

@@ -2,4 +2,4 @@ _base_ = './htc_r50_fpn_1x_coco.py'
 model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/htc/htc_r50_fpn_20e_coco.py

@@ -1,4 +1,4 @@
 _base_ = './htc_r50_fpn_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py

@@ -15,4 +15,4 @@ model = dict(
 data = dict(samples_per_gpu=1, workers_per_gpu=1)
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py

@@ -15,4 +15,4 @@ model = dict(
 data = dict(samples_per_gpu=1, workers_per_gpu=1)
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py

@@ -39,4 +39,4 @@ data = dict(
     samples_per_gpu=1, workers_per_gpu=1, train=dict(pipeline=train_pipeline))
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py

@@ -25,4 +25,4 @@ train_pipeline = [
 data = dict(train=dict(pipeline=train_pipeline))
 # learning policy
 lr_config = dict(step=[32, 44])
-total_epochs = 48
+runner = dict(max_epochs=48)

configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py

@@ -25,4 +25,4 @@ train_pipeline = [
 data = dict(train=dict(pipeline=train_pipeline))
 # learning policy
 lr_config = dict(step=[32, 44])
-total_epochs = 48
+runner = dict(max_epochs=48)

configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 23])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
 # learning policy
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py

@@ -58,4 +58,4 @@ data = dict(
     test=dict(pipeline=test_pipeline))
 
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py

@@ -77,4 +77,4 @@ lr_config = dict(
     warmup_ratio=0.1,
     step=[30, 40])
 # runtime settings
-total_epochs = 50
+runner = dict(max_epochs=50)

configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py

@@ -76,4 +76,4 @@ lr_config = dict(
     warmup_ratio=0.1,
     step=[30, 40])
 # runtime settings
-total_epochs = 50
+runner = dict(max_epochs=50)

configs/paa/paa_r101_fpn_1x_coco.py

@@ -1,4 +1,2 @@
 _base_ = './paa_r50_fpn_1x_coco.py'
 model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
-lr_config = dict(step=[16, 22])
-total_epochs = 24

configs/paa/paa_r101_fpn_2x_coco.py

@@ -1,3 +1,3 @@
 _base_ = './paa_r101_fpn_1x_coco.py'
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/paa/paa_r50_fpn_1.5x_coco.py

@@ -1,3 +1,3 @@
 _base_ = './paa_r50_fpn_1x_coco.py'
 lr_config = dict(step=[12, 16])
-total_epochs = 18
+runner = dict(max_epochs=18)

configs/paa/paa_r50_fpn_2x_coco.py

@@ -1,3 +1,3 @@
 _base_ = './paa_r50_fpn_1x_coco.py'
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/paa/paa_r50_fpn_mstrain_3x_coco.py

@@ -17,4 +17,4 @@ train_pipeline = [
 ]
 data = dict(train=dict(pipeline=train_pipeline))
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py

@@ -10,4 +10,4 @@ optimizer_config = dict(grad_clip=None)
 # actual epoch = 3 * 3 = 9
 lr_config = dict(policy='step', step=[3])
 # runtime settings
-total_epochs = 4  # actual epoch = 4 * 3 = 12
+runner = dict(max_epochs=4)  # actual epoch = 4 * 3 = 12

configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py

@@ -10,4 +10,4 @@ optimizer_config = dict(grad_clip=None)
 # actual epoch = 3 * 3 = 9
 lr_config = dict(policy='step', step=[3])
 # runtime settings
-total_epochs = 4  # actual epoch = 4 * 3 = 12
+runner = dict(max_epochs=4)  # actual epoch = 4 * 3 = 12

configs/pascal_voc/ssd300_voc0712.py

@@ -66,4 +66,4 @@ lr_config = dict(
     step=[16, 20])
 checkpoint_config = dict(interval=1)
 # runtime settings
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py'
 # learning policy
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py

@@ -1,3 +1,3 @@
 _base_ = './faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py

@@ -60,4 +60,4 @@ data = dict(
     test=dict(pipeline=test_pipeline))
 optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py

@@ -60,6 +60,6 @@ data = dict(
     test=dict(pipeline=test_pipeline))
 optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)
 optimizer_config = dict(
     _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py

@@ -1,3 +1,3 @@
 _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/res2net/htc_r2_101_fpn_20e_coco.py

@@ -4,4 +4,4 @@ model = dict(
     backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26))
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 23])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
 # learning policy
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/retinanet/retinanet_r50_fpn_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './retinanet_r50_fpn_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/rpn/rpn_r50_fpn_2x_coco.py

@@ -2,4 +2,4 @@ _base_ = './rpn_r50_fpn_1x_coco.py'
 
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/scnet/scnet_r50_fpn_20e_coco.py

@@ -1,4 +1,4 @@
 _base_ = './scnet_r50_fpn_1x_coco.py'
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py

@@ -19,4 +19,4 @@ optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0))
 optimizer_config = dict(_delete_=True, grad_clip=None)
 # learning policy
 lr_config = dict(warmup_ratio=0.1, step=[65, 71])
-total_epochs = 73
+runner = dict(max_epochs=73)

configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py

@@ -20,4 +20,4 @@ optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0))
 optimizer_config = dict(_delete_=True, grad_clip=None)
 # learning policy
 lr_config = dict(warmup_ratio=0.1, step=[65, 71])
-total_epochs = 73
+runner = dict(max_epochs=73)

configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py

@@ -92,4 +92,4 @@ optimizer = dict(_delete_=True, type='AdamW', lr=0.000025, weight_decay=0.0001)
 optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=1, norm_type=2))
 # learning policy
 lr_config = dict(policy='step', step=[8, 11])
-total_epochs = 12
+runner = dict(max_epochs=12)

configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py

@@ -20,4 +20,4 @@ train_pipeline = [
 
 data = dict(train=dict(pipeline=train_pipeline))
 lr_config = dict(policy='step', step=[27, 33])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py

@@ -1,4 +1,4 @@
 _base_ = 'tridentnet_r50_caffe_mstrain_1x_coco.py'
 
 lr_config = dict(step=[28, 34])
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/vfnet/vfnet_r101_fpn_2x_coco.py

@@ -1,4 +1,4 @@
 _base_ = './vfnet_r50_fpn_1x_coco.py'
 model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/vfnet/vfnet_r50_fpn_1x_coco.py

@@ -105,9 +105,4 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=0.1,
     step=[8, 11])
-total_epochs = 12
-
-# runtime
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
+runner = dict(max_epochs=12)

configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py

@@ -36,4 +36,4 @@ data = dict(
     test=dict(pipeline=test_pipeline))
 # learning policy
 lr_config = dict(step=[16, 22])
-total_epochs = 24
+runner = dict(max_epochs=24)

configs/wider_face/ssd300_wider_face.py

@@ -14,5 +14,5 @@ lr_config = dict(
     warmup_ratio=0.001,
     step=[16, 20])
 # runtime settings
-total_epochs = 24
+runner = dict(max_epochs=24)
 log_config = dict(interval=1)

configs/yolact/yolact_r50_1x8_coco.py

@@ -155,6 +155,6 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=0.1,
     step=[20, 42, 49, 52])
-total_epochs = 55
+runner = dict(max_epochs=55)
 cudnn_benchmark = True
 evaluation = dict(metric=['bbox', 'segm'])

configs/yolo/yolov3_d53_mstrain-608_273e_coco.py

@@ -120,5 +120,5 @@ lr_config = dict(
     warmup_ratio=0.1,
     step=[218, 246])
 # runtime settings
-total_epochs = 273
+runner = dict(max_epochs=273)
 evaluation = dict(interval=1, metric=['bbox'])

docs/tutorials/config.md

@@ -394,7 +394,7 @@ lr_config = dict(  # Learning rate scheduler config used to register LrUpdater h
     warmup_ratio=
     0.001,  # The ratio of the starting learning rate used for warmup
     step=[8, 11])  # Steps to decay the learning rate
-total_epochs = 12  # Total epochs to train the model
+runner = dict(type='EpochBasedRunner', max_epochs=12) # Runner that runs the workflow in total max_epochs
 checkpoint_config = dict(  # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
     interval=1)  # The save interval is 1
 log_config = dict(  # config to register logger hook
@@ -409,7 +409,6 @@ load_from = None  # load models as a pre-trained model from a given path. This w
 resume_from = None  # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
 workflow = [('train', 1)]  # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 12 epochs according to the total_epochs.
 work_dir = 'work_dir'  # Directory to save the model checkpoints and logs for the current experiments.
-
 ```
 
 ## FAQ