[Refactor] Customize runner type (#437)

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 @@ assert (mmcv_version >= digit_version(mmcv_minimum_version)
     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)