[Refactor] Customize runner type

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

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

configs/_base_/models/3dssd.py

@@ -84,4 +84,4 @@
 optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
 lr_config = dict(policy='step', warmup=None, step=[80, 120])
 # runtime settings
-total_epochs = 150
+runner = dict(max_epochs=150)

configs/_base_/schedules/cyclic_20e.py

@@ -21,4 +21,4 @@
 )
 
 # runtime settings
-total_epochs = 20
+runner = dict(type='EpochBasedRunner', max_epochs=20)

configs/_base_/schedules/cyclic_40e.py

@@ -25,7 +25,7 @@
  cyclic_times=1,
  step_ratio_up=0.4,
 )
-# Although the total_epochs is 40, this schedule is usually used we
-# RepeatDataset with repeat ratio N, thus the actual total epoch
+# Although the max_epochs is 40, this schedule is usually used we
+# RepeatDataset with repeat ratio N, thus the actual max epoch
 # number could be Nx40
-total_epochs = 40
+runner = dict(type='EpochBasedRunner', max_epochs=40)

configs/_base_/schedules/mmdet_schedule_1x.py

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

configs/_base_/schedules/schedule_2x.py

@@ -11,4 +11,4 @@
  step=[20, 23])
 momentum_config = None
 # runtime settings
-total_epochs = 24
+runner = dict(type='EpochBasedRunner', max_epochs=24)

configs/_base_/schedules/schedule_3x.py

@@ -6,4 +6,4 @@
 optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
 lr_config = dict(policy='step', warmup=None, step=[24, 32])
 # runtime settings
-total_epochs = 36
+runner = dict(type='EpochBasedRunner', max_epochs=36)

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

@@ -312,7 +312,7 @@
  ])
 # yapf:enable
 # runtime settings
-total_epochs = 80
+runner = dict(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

@@ -182,7 +182,7 @@
  ])
 # yapf:enable
 # runtime settings
-total_epochs = 50
+runner = dict(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

@@ -225,7 +225,7 @@
  ])
 # yapf:enable
 # runtime settings
-total_epochs = 80
+runner = dict(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

@@ -227,7 +227,7 @@
  ])
 # yapf:enable
 # runtime settings
-total_epochs = 80
+runner = dict(max_epochs=80)
 dist_params = dict(backend='nccl')
 log_level = 'INFO'
 work_dir = './work_dirs/sec_secfpn_80e'

configs/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py

@@ -66,4 +66,4 @@
 
 lr_config = dict(step=[28, 34])
 evaluation = dict(interval=36)
-total_epochs = 36
+runner = dict(max_epochs=36)

configs/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py

@@ -66,4 +66,4 @@
 data = dict(train=dict(pipeline=train_pipeline))
 lr_config = dict(step=[28, 34])
 evaluation = dict(interval=36)
-total_epochs = 36
+runner = dict(max_epochs=36)

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

@@ -53,6 +53,6 @@
  warmup_iters=500,
  warmup_ratio=0.001,
  step=[6])
-total_epochs = 8
+runner = dict(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

@@ -248,7 +248,7 @@
 # yapf:enable
 evaluation = dict(interval=1)
 # runtime settings
-total_epochs = 40
+runner = dict(max_epochs=40)
 dist_params = dict(backend='nccl')
 log_level = 'INFO'
 work_dir = None

configs/nuimages/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim.py

@@ -2,6 +2,6 @@
 
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)
 
 load_from = 'http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth' # noqa

configs/nuimages/htc_r50_fpn_coco-20e_20e_nuim.py

@@ -1,4 +1,4 @@
 _base_ = './htc_r50_fpn_coco-20e_1x_nuim.py'
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)

configs/nuimages/htc_x101_64x4d_fpn_dconv_c3-c5_coco-20e_16x1_20e_nuim.py

@@ -18,6 +18,6 @@
 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)
 
 load_from = 'http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth' # noqa

configs/nuimages/mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim.py

@@ -47,6 +47,6 @@
 
 # learning policy
 lr_config = dict(step=[16, 19])
-total_epochs = 20
+runner = dict(max_epochs=20)
 
 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/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py

@@ -83,4 +83,4 @@
 # 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.
-total_epochs = 80
+runner = dict(max_epochs=80)

docs/getting_started.md

@@ -12,7 +12,7 @@ The required versions of MMCV and MMDetection for different versions of MMDetect
 
 | MMDetection3D version | MMDetection version | MMCV version |
 |:-------------------:|:-------------------:|:-------------------:|
-| master | mmdet>=2.5.0  | mmcv-full>=1.2.4, <=1.4|
+| master | mmdet>=2.10.0 | mmcv-full>=1.2.4, <=1.4|
 | 0.12.0 | mmdet>=2.5.0 | mmcv-full>=1.2.4, <=1.4|
 | 0.11.0 | mmdet>=2.5.0 | mmcv-full>=1.2.4, <=1.4|
 | 0.10.0 | mmdet>=2.5.0 | mmcv-full>=1.2.4, <=1.4|

docs/tutorials/config.md

@@ -365,14 +365,14 @@ log_config = dict( # config to register logger hook
  interval=50, # Interval to print the log
  hooks=[dict(type='TextLoggerHook'),
  dict(type='TensorboardLoggerHook')]) # The logger used to record the training process.
-total_epochs = 36 # Total epochs to train the model
+runner = dict(type='EpochBasedRunner', max_epochs=36) # Runner that runs the workflow in total max_epochs
 dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set.
 log_level = 'INFO' # The level of logging.
 find_unused_parameters = True # Whether to find unused parameters
 work_dir = None # Directory to save the model checkpoints and logs for the current experiments.
 load_from = None # load models as a pre-trained model from a given path. This will not resume training.
 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 36 epochs according to the total_epochs.
+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 36 epochs according to the max_epochs.
 gpu_ids = range(0, 1) # ids of gpus
 ```
 

docs/tutorials/customize_runtime.md

@@ -179,7 +179,7 @@ so that 1 epoch for training and 1 epoch for validation will be run iteratively.
 **Note**:
 
 1. The parameters of model will not be updated during val epoch.
-2. Keyword `total_epochs` in the config only controls the number of training epochs and will not affect the validation workflow.
+2. Keyword `max_epochs` in `runner` in the config only controls the number of training epochs and will not affect the validation workflow.
 3. Workflows `[('train', 1), ('val', 1)]` and `[('train', 1)]` will not change the behavior of `EvalHook` because `EvalHook` is called by `after_train_epoch` and validation workflow only affect hooks that are called through `after_val_epoch`. Therefore, the only difference between `[('train', 1), ('val', 1)]` and `[('train', 1)]` is that the runner will calculate losses on validation set after each training epoch.
 
 ## Customize hooks

mmdet3d/__init__.py

@@ -26,7 +26,7 @@ def digit_version(version_str):
  f'MMCV=={mmcv.__version__} is used but incompatible. ' \
  f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.'
 
-mmdet_minimum_version = '2.5.0'
+mmdet_minimum_version = '2.10.0'
 mmdet_maximum_version = '3.0.0'
 mmdet_version = digit_version(mmdet.__version__)
 assert (mmdet_version >= digit_version(mmdet_minimum_version)