# Tutorial 1: Learn about Configs

We use python files as configs, incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments.
You can find all the provided configs under `$MMAction2/configs`. If you wish to inspect the config file,
you may run `python tools/analysis/print_config.py /PATH/TO/CONFIG` to see the complete config.

<!-- TOC -->

- [Modify config through script arguments](#modify-config-through-script-arguments)
- [Config File Structure](#config-file-structure)
- [Config File Naming Convention](#config-file-naming-convention)
  - [Config System for Action localization](#config-system-for-action-localization)
  - [Config System for Action Recognition](#config-system-for-action-recognition)
  - [Config System for Spatio-Temporal Action Detection](#config-system-for-spatio-temporal-action-detection)
- [FAQ](#faq)
  - [Use intermediate variables in configs](#use-intermediate-variables-in-configs)

<!-- TOC -->

## Modify config through script arguments

When submitting jobs using "tools/train.py" or "tools/test.py", you may specify `--cfg-options` to in-place modify the config.

- Update config keys of dict.

  The config options can be specified following the order of the dict keys in the original config.
  For example, `--cfg-options model.backbone.norm_eval=False` changes the all BN modules in model backbones to `train` mode.

- Update keys inside a list of configs.

  Some config dicts are composed as a list in your config. For example, the training pipeline `data.train.pipeline` is normally a list
  e.g. `[dict(type='SampleFrames'), ...]`. If you want to change `'SampleFrames'` to `'DenseSampleFrames'` in the pipeline,
  you may specify `--cfg-options data.train.pipeline.0.type=DenseSampleFrames`.

- Update values of list/tuples.

  If the value to be updated is a list or a tuple. For example, the config file normally sets `workflow=[('train', 1)]`. If you want to
  change this key, you may specify `--cfg-options workflow="[(train,1),(val,1)]"`. Note that the quotation mark " is necessary to
  support list/tuple data types, and that **NO** white space is allowed inside the quotation marks in the specified value.

## Config File Structure

There are 3 basic component types under `config/_base_`, model, schedule, default_runtime.
Many methods could be easily constructed with one of each like TSN, I3D, SlowOnly, etc.
The configs that are composed by components from `_base_` are called _primitive_.

For all configs under the same folder, it is recommended to have only **one** _primitive_ config. All other configs should inherit from the _primitive_ config. In this way, the maximum of inheritance level is 3.

For easy understanding, we recommend contributors to inherit from exiting methods.
For example, if some modification is made base on TSN, users may first inherit the basic TSN structure by specifying `_base_ = ../tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py`, then modify the necessary fields in the config files.

If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder under `configs/TASK`.

Please refer to [mmcv](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html) for detailed documentation.

## Config File Naming Convention

We follow the style below to name config files. Contributors are advised to follow the same style.

```
{model}_[model setting]_{backbone}_[misc]_{data setting}_[gpu x batch_per_gpu]_{schedule}_{dataset}_{modality}
```

`{xxx}` is required field and `[yyy]` is optional.

- `{model}`: model type, e.g. `tsn`, `i3d`, etc.
- `[model setting]`: specific setting for some models.
- `{backbone}`: backbone type, e.g. `r50` (ResNet-50), etc.
- `[misc]`: miscellaneous setting/plugins of model, e.g. `dense`, `320p`, `video`, etc.
- `{data setting}`: frame sample setting in `{clip_len}x{frame_interval}x{num_clips}` format.
- `[gpu x batch_per_gpu]`: GPUs and samples per GPU.
- `{schedule}`: training schedule, e.g. `20e` means 20 epochs.
- `{dataset}`: dataset name, e.g. `kinetics400`, `mmit`, etc.
- `{modality}`: frame modality, e.g. `rgb`, `flow`, etc.

### Config System for Action localization

We incorporate modular design into our config system,
which is convenient to conduct various experiments.

- An Example of BMN

  To help the users have a basic idea of a complete config structure and the modules in an action localization system,
  we make brief comments on the config of BMN as the following.
  For more detailed usage and alternative for per parameter in each module, please refer to the [API documentation](https://mmaction2.readthedocs.io/en/latest/api.html).

  ```python
  # model settings
  model = dict(  # Config of the model
      type='BMN',  # Type of the localizer
      temporal_dim=100,  # Total frames selected for each video
      boundary_ratio=0.5,  # Ratio for determining video boundaries
      num_samples=32,  # Number of samples for each proposal
      num_samples_per_bin=3,  # Number of bin samples for each sample
      feat_dim=400,  # Dimension of feature
      soft_nms_alpha=0.4,  # Soft NMS alpha
      soft_nms_low_threshold=0.5,  # Soft NMS low threshold
      soft_nms_high_threshold=0.9,  # Soft NMS high threshold
      post_process_top_k=100)  # Top k proposals in post process
  # model training and testing settings
  train_cfg = None  # Config of training hyperparameters for BMN
  test_cfg = dict(average_clips='score')  # Config for testing hyperparameters for BMN

  # dataset settings
  dataset_type = 'ActivityNetDataset'  # Type of dataset for training, validation and testing
  data_root = 'data/activitynet_feature_cuhk/csv_mean_100/'  # Root path to data for training
  data_root_val = 'data/activitynet_feature_cuhk/csv_mean_100/'  # Root path to data for validation and testing
  ann_file_train = 'data/ActivityNet/anet_anno_train.json'  # Path to the annotation file for training
  ann_file_val = 'data/ActivityNet/anet_anno_val.json'  # Path to the annotation file for validation
  ann_file_test = 'data/ActivityNet/anet_anno_test.json'  # Path to the annotation file for testing

  train_pipeline = [  # List of training pipeline steps
      dict(type='LoadLocalizationFeature'),  # Load localization feature pipeline
      dict(type='GenerateLocalizationLabels'),  # Generate localization labels pipeline
      dict(  # Config of Collect
          type='Collect',  # Collect pipeline that decides which keys in the data should be passed to the localizer
          keys=['raw_feature', 'gt_bbox'],  # Keys of input
          meta_name='video_meta',  # Meta name
          meta_keys=['video_name']),  # Meta keys of input
      dict(  # Config of ToTensor
          type='ToTensor',  # Convert other types to tensor type pipeline
          keys=['raw_feature']),  # Keys to be converted from image to tensor
      dict(  # Config of ToDataContainer
          type='ToDataContainer',  # Pipeline to convert the data to DataContainer
          fields=[dict(key='gt_bbox', stack=False, cpu_only=True)])  # Required fields to be converted with keys and attributes
  ]
  val_pipeline = [  # List of validation pipeline steps
      dict(type='LoadLocalizationFeature'),  # Load localization feature pipeline
      dict(type='GenerateLocalizationLabels'),  # Generate localization labels pipeline
      dict(  # Config of Collect
          type='Collect',  # Collect pipeline that decides which keys in the data should be passed to the localizer
          keys=['raw_feature', 'gt_bbox'],  # Keys of input
          meta_name='video_meta',  # Meta name
          meta_keys=[
              'video_name', 'duration_second', 'duration_frame', 'annotations',
              'feature_frame'
          ]),  # Meta keys of input
      dict(  # Config of ToTensor
          type='ToTensor',  # Convert other types to tensor type pipeline
          keys=['raw_feature']),  # Keys to be converted from image to tensor
      dict(  # Config of ToDataContainer
          type='ToDataContainer',  # Pipeline to convert the data to DataContainer
          fields=[dict(key='gt_bbox', stack=False, cpu_only=True)])  # Required fields to be converted with keys and attributes
  ]
  test_pipeline = [  # List of testing pipeline steps
      dict(type='LoadLocalizationFeature'),  # Load localization feature pipeline
      dict(  # Config of Collect
          type='Collect',  # Collect pipeline that decides which keys in the data should be passed to the localizer
          keys=['raw_feature'],  # Keys of input
          meta_name='video_meta',  # Meta name
          meta_keys=[
              'video_name', 'duration_second', 'duration_frame', 'annotations',
              'feature_frame'
          ]),  # Meta keys of input
      dict(  # Config of ToTensor
          type='ToTensor',  # Convert other types to tensor type pipeline
          keys=['raw_feature']),  # Keys to be converted from image to tensor
  ]
  data = dict(  # Config of data
      videos_per_gpu=8,  # Batch size of each single GPU
      workers_per_gpu=8,  # Workers to pre-fetch data for each single GPU
      train_dataloader=dict(  # Additional config of train dataloader
          drop_last=True),  # Whether to drop out the last batch of data in training
      val_dataloader=dict(  # Additional config of validation dataloader
          videos_per_gpu=1),  # Batch size of each single GPU during evaluation
      test_dataloader=dict(  # Additional config of test dataloader
          videos_per_gpu=2),  # Batch size of each single GPU during testing
      test=dict(  # Testing dataset config
          type=dataset_type,
          ann_file=ann_file_test,
          pipeline=test_pipeline,
          data_prefix=data_root_val),
      val=dict(  # Validation dataset config
          type=dataset_type,
          ann_file=ann_file_val,
          pipeline=val_pipeline,
          data_prefix=data_root_val),
      train=dict(  # Training dataset config
          type=dataset_type,
          ann_file=ann_file_train,
          pipeline=train_pipeline,
          data_prefix=data_root))

  # optimizer
  optimizer = dict(
      # Config used to build optimizer, support (1). All the optimizers in PyTorch
      # whose arguments are also the same as those in PyTorch. (2). Custom optimizers
      # which are built on `constructor`, referring to "tutorials/5_new_modules.md"
      # for implementation.
      type='Adam',  # Type of optimizer, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details
      lr=0.001,  # Learning rate, see detail usages of the parameters in the documentation of PyTorch
      weight_decay=0.0001)  # Weight decay of Adam
  optimizer_config = dict(  # Config used to build the optimizer hook
      grad_clip=None)  # Most of the methods do not use gradient clip
  # learning policy
  lr_config = dict(  # Learning rate scheduler config used to register LrUpdater hook
      policy='step',  # Policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9
      step=7)  # Steps to decay the learning rate

  total_epochs = 9  # Total epochs to train the model
  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)  # Interval to save checkpoint
  evaluation = dict(  # Config of evaluation during training
      interval=1,  # Interval to perform evaluation
      metrics=['AR@AN'])  # Metrics to be performed
  log_config = dict(  # Config to register logger hook
      interval=50,  # Interval to print the log
      hooks=[  # Hooks to be implemented during training
          dict(type='TextLoggerHook'),  # The logger used to record the training process
          # dict(type='TensorboardLoggerHook'),  # The Tensorboard logger is also supported
      ])

  # runtime settings
  dist_params = dict(backend='nccl')  # Parameters to setup distributed training, the port can also be set
  log_level = 'INFO'  # The level of logging
  work_dir = './work_dirs/bmn_400x100_2x8_9e_activitynet_feature/'  # 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
  output_config = dict(  # Config of localization output
      out=f'{work_dir}/results.json',  # Path to output file
      output_format='json')  # File format of output file
  ```

### Config System for Action Recognition

We incorporate modular design into our config system,
which is convenient to conduct various experiments.

- An Example of TSN

  To help the users have a basic idea of a complete config structure and the modules in an action recognition system,
  we make brief comments on the config of TSN as the following.
  For more detailed usage and alternative for per parameter in each module, please refer to the API documentation.

  ```python
  # model settings
  model = dict(  # Config of the model
      type='Recognizer2D',  # Type of the recognizer
      backbone=dict(  # Dict for backbone
          type='ResNet',  # Name of the backbone
          pretrained='torchvision://resnet50',  # The url/site of the pretrained model
          depth=50,  # Depth of ResNet model
          norm_eval=False),  # Whether to set BN layers to eval mode when training
      cls_head=dict(  # Dict for classification head
          type='TSNHead',  # Name of classification head
          num_classes=400,  # Number of classes to be classified.
          in_channels=2048,  # The input channels of classification head.
          spatial_type='avg',  # Type of pooling in spatial dimension
          consensus=dict(type='AvgConsensus', dim=1),  # Config of consensus module
          dropout_ratio=0.4,  # Probability in dropout layer
          init_std=0.01), # Std value for linear layer initiation
          # model training and testing settings
          train_cfg=None,  # Config of training hyperparameters for TSN
          test_cfg=dict(average_clips=None))  # Config for testing hyperparameters for TSN.

  # dataset settings
  dataset_type = 'RawframeDataset'  # Type of dataset for training, validation and testing
  data_root = 'data/kinetics400/rawframes_train/'  # Root path to data for training
  data_root_val = 'data/kinetics400/rawframes_val/'  # Root path to data for validation and testing
  ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'  # Path to the annotation file for training
  ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'  # Path to the annotation file for validation
  ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'  # Path to the annotation file for testing
  img_norm_cfg = dict(  # Config of image normalization used in data pipeline
      mean=[123.675, 116.28, 103.53],  # Mean values of different channels to normalize
      std=[58.395, 57.12, 57.375],  # Std values of different channels to normalize
      to_bgr=False)  # Whether to convert channels from RGB to BGR

  train_pipeline = [  # List of training pipeline steps
      dict(  # Config of SampleFrames
          type='SampleFrames',  # Sample frames pipeline, sampling frames from video
          clip_len=1,  # Frames of each sampled output clip
          frame_interval=1,  # Temporal interval of adjacent sampled frames
          num_clips=3),  # Number of clips to be sampled
      dict(  # Config of RawFrameDecode
          type='RawFrameDecode'),  # Load and decode Frames pipeline, picking raw frames with given indices
      dict(  # Config of Resize
          type='Resize',  # Resize pipeline
          scale=(-1, 256)),  # The scale to resize images
      dict(  # Config of MultiScaleCrop
          type='MultiScaleCrop',  # Multi scale crop pipeline, cropping images with a list of randomly selected scales
          input_size=224,  # Input size of the network
          scales=(1, 0.875, 0.75, 0.66),  # Scales of width and height to be selected
          random_crop=False,  # Whether to randomly sample cropping bbox
          max_wh_scale_gap=1),  # Maximum gap of w and h scale levels
      dict(  # Config of Resize
          type='Resize',  # Resize pipeline
          scale=(224, 224),  # The scale to resize images
          keep_ratio=False),  # Whether to resize with changing the aspect ratio
      dict(  # Config of Flip
          type='Flip',  # Flip Pipeline
          flip_ratio=0.5),  # Probability of implementing flip
      dict(  # Config of Normalize
          type='Normalize',  # Normalize pipeline
          **img_norm_cfg),  # Config of image normalization
      dict(  # Config of FormatShape
          type='FormatShape',  # Format shape pipeline, Format final image shape to the given input_format
          input_format='NCHW'),  # Final image shape format
      dict(  # Config of Collect
          type='Collect',  # Collect pipeline that decides which keys in the data should be passed to the recognizer
          keys=['imgs', 'label'],  # Keys of input
          meta_keys=[]),  # Meta keys of input
      dict(  # Config of ToTensor
          type='ToTensor',  # Convert other types to tensor type pipeline
          keys=['imgs', 'label'])  # Keys to be converted from image to tensor
  ]
  val_pipeline = [  # List of validation pipeline steps
      dict(  # Config of SampleFrames
          type='SampleFrames',  # Sample frames pipeline, sampling frames from video
          clip_len=1,  # Frames of each sampled output clip
          frame_interval=1,  # Temporal interval of adjacent sampled frames
          num_clips=3,  # Number of clips to be sampled
          test_mode=True),  # Whether to set test mode in sampling
      dict(  # Config of RawFrameDecode
          type='RawFrameDecode'),  # Load and decode Frames pipeline, picking raw frames with given indices
      dict(  # Config of Resize
          type='Resize',  # Resize pipeline
          scale=(-1, 256)),  # The scale to resize images
      dict(  # Config of CenterCrop
          type='CenterCrop',  # Center crop pipeline, cropping the center area from images
          crop_size=224),  # The size to crop images
      dict(  # Config of Flip
          type='Flip',  # Flip pipeline
          flip_ratio=0),  # Probability of implementing flip
      dict(  # Config of Normalize
          type='Normalize',  # Normalize pipeline
          **img_norm_cfg),  # Config of image normalization
      dict(  # Config of FormatShape
          type='FormatShape',  # Format shape pipeline, Format final image shape to the given input_format
          input_format='NCHW'),  # Final image shape format
      dict(  # Config of Collect
          type='Collect',  # Collect pipeline that decides which keys in the data should be passed to the recognizer
          keys=['imgs', 'label'],  # Keys of input
          meta_keys=[]),  # Meta keys of input
      dict(  # Config of ToTensor
          type='ToTensor',  # Convert other types to tensor type pipeline
          keys=['imgs'])  # Keys to be converted from image to tensor
  ]
  test_pipeline = [  # List of testing pipeline steps
      dict(  # Config of SampleFrames
          type='SampleFrames',  # Sample frames pipeline, sampling frames from video
          clip_len=1,  # Frames of each sampled output clip
          frame_interval=1,  # Temporal interval of adjacent sampled frames
          num_clips=25,  # Number of clips to be sampled
          test_mode=True),  # Whether to set test mode in sampling
      dict(  # Config of RawFrameDecode
          type='RawFrameDecode'),  # Load and decode Frames pipeline, picking raw frames with given indices
      dict(  # Config of Resize
          type='Resize',  # Resize pipeline
          scale=(-1, 256)),  # The scale to resize images
      dict(  # Config of TenCrop
          type='TenCrop',  # Ten crop pipeline, cropping ten area from images
          crop_size=224),  # The size to crop images
      dict(  # Config of Flip
          type='Flip',  # Flip pipeline
          flip_ratio=0),  # Probability of implementing flip
      dict(  # Config of Normalize
          type='Normalize',  # Normalize pipeline
          **img_norm_cfg),  # Config of image normalization
      dict(  # Config of FormatShape
          type='FormatShape',  # Format shape pipeline, Format final image shape to the given input_format
          input_format='NCHW'),  # Final image shape format
      dict(  # Config of Collect
          type='Collect',  # Collect pipeline that decides which keys in the data should be passed to the recognizer
          keys=['imgs', 'label'],  # Keys of input
          meta_keys=[]),  # Meta keys of input
      dict(  # Config of ToTensor
          type='ToTensor',  # Convert other types to tensor type pipeline
          keys=['imgs'])  # Keys to be converted from image to tensor
  ]
  data = dict(  # Config of data
      videos_per_gpu=32,  # Batch size of each single GPU
      workers_per_gpu=2,  # Workers to pre-fetch data for each single GPU
      train_dataloader=dict(  # Additional config of train dataloader
          drop_last=True),  # Whether to drop out the last batch of data in training
      val_dataloader=dict(  # Additional config of validation dataloader
          videos_per_gpu=1),  # Batch size of each single GPU during evaluation
      test_dataloader=dict(  # Additional config of test dataloader
          videos_per_gpu=2),  # Batch size of each single GPU during testing
      train=dict(  # Training dataset config
          type=dataset_type,
          ann_file=ann_file_train,
          data_prefix=data_root,
          pipeline=train_pipeline),
      val=dict(  # Validation dataset config
          type=dataset_type,
          ann_file=ann_file_val,
          data_prefix=data_root_val,
          pipeline=val_pipeline),
      test=dict(  # Testing dataset config
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix=data_root_val,
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      # Config used to build optimizer, support (1). All the optimizers in PyTorch
      # whose arguments are also the same as those in PyTorch. (2). Custom optimizers
      # which are built on `constructor`, referring to "tutorials/5_new_modules.md"
      # for implementation.
      type='SGD',  # Type of optimizer, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details
      lr=0.01,  # Learning rate, see detail usages of the parameters in the documentation of PyTorch
      momentum=0.9,  # Momentum,
      weight_decay=0.0001)  # Weight decay of SGD
  optimizer_config = dict(  # Config used to build the optimizer hook
      grad_clip=dict(max_norm=40, norm_type=2))  # Use gradient clip
  # learning policy
  lr_config = dict(  # Learning rate scheduler config used to register LrUpdater hook
      policy='step',  # Policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9
      step=[40, 80])  # Steps to decay the learning rate
  total_epochs = 100  # Total epochs to train the model
  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=5)  # Interval to save checkpoint
  evaluation = dict(  # Config of evaluation during training
      interval=5,  # Interval to perform evaluation
      metrics=['top_k_accuracy', 'mean_class_accuracy'],  # Metrics to be performed
      metric_options=dict(top_k_accuracy=dict(topk=(1, 3))), # Set top-k accuracy to 1 and 3 during validation
      save_best='top_k_accuracy')  # set `top_k_accuracy` as key indicator to save best checkpoint
  eval_config = dict(
      metric_options=dict(top_k_accuracy=dict(topk=(1, 3)))) # Set top-k accuracy to 1 and 3 during testing. You can also use `--eval top_k_accuracy` to assign evaluation metrics
  log_config = dict(  # Config to register logger hook
      interval=20,  # Interval to print the log
      hooks=[  # Hooks to be implemented during training
          dict(type='TextLoggerHook'),  # The logger used to record the training process
          # dict(type='TensorboardLoggerHook'),  # The Tensorboard logger is also supported
      ])

  # runtime settings
  dist_params = dict(backend='nccl')  # Parameters to setup distributed training, the port can also be set
  log_level = 'INFO'  # The level of logging
  work_dir = './work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb/'  # 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

  ```

### Config System for Spatio-Temporal Action Detection

We incorporate modular design into our config system, which is convenient to conduct various experiments.

- An Example of FastRCNN

  To help the users have a basic idea of a complete config structure and the modules in a spatio-temporal action detection system,
  we make brief comments on the config of FastRCNN as the following.
  For more detailed usage and alternative for per parameter in each module, please refer to the API documentation.

  ```python
  # model setting
  model = dict(  # Config of the model
      type='FastRCNN',  # Type of the detector
      backbone=dict(  # Dict for backbone
          type='ResNet3dSlowOnly',  # Name of the backbone
          depth=50, # Depth of ResNet model
          pretrained=None,   # The url/site of the pretrained model
          pretrained2d=False, # If the pretrained model is 2D
          lateral=False,  # If the backbone is with lateral connections
          num_stages=4, # Stages of ResNet model
          conv1_kernel=(1, 7, 7), # Conv1 kernel size
          conv1_stride_t=1, # Conv1 temporal stride
          pool1_stride_t=1, # Pool1 temporal stride
          spatial_strides=(1, 2, 2, 1)),  # The spatial stride for each ResNet stage
      roi_head=dict(  # Dict for roi_head
          type='AVARoIHead',  # Name of the roi_head
          bbox_roi_extractor=dict(  # Dict for bbox_roi_extractor
              type='SingleRoIExtractor3D',  # Name of the bbox_roi_extractor
              roi_layer_type='RoIAlign',  # Type of the RoI op
              output_size=8,  # Output feature size of the RoI op
              with_temporal_pool=True), # If temporal dim is pooled
          bbox_head=dict( # Dict for bbox_head
              type='BBoxHeadAVA', # Name of the bbox_head
              in_channels=2048, # Number of channels of the input feature
              num_classes=81, # Number of action classes + 1
              multilabel=True,  # If the dataset is multilabel
              dropout_ratio=0.5)),  # The dropout ratio used
      # model training and testing settings
      train_cfg=dict(  # Training config of FastRCNN
          rcnn=dict(  # Dict for rcnn training config
              assigner=dict(  # Dict for assigner
                  type='MaxIoUAssignerAVA', # Name of the assigner
                  pos_iou_thr=0.9,  # IoU threshold for positive examples, > pos_iou_thr -> positive
                  neg_iou_thr=0.9,  # IoU threshold for negative examples, < neg_iou_thr -> negative
                  min_pos_iou=0.9), # Minimum acceptable IoU for positive examples
              sampler=dict( # Dict for sample
                  type='RandomSampler', # Name of the sampler
                  num=32, # Batch Size of the sampler
                  pos_fraction=1, # Positive bbox fraction of the sampler
                  neg_pos_ub=-1,  # Upper bound of the ratio of num negative to num positive
                  add_gt_as_proposals=True), # Add gt bboxes as proposals
              pos_weight=1.0, # Loss weight of positive examples
              debug=False)), # Debug mode
      test_cfg=dict( # Testing config of FastRCNN
          rcnn=dict(  # Dict for rcnn testing config
              action_thr=0.002))) # The threshold of an action

  # dataset settings
  dataset_type = 'AVADataset' # Type of dataset for training, validation and testing
  data_root = 'data/ava/rawframes'  # Root path to data
  anno_root = 'data/ava/annotations'  # Root path to annotations

  ann_file_train = f'{anno_root}/ava_train_v2.1.csv'  # Path to the annotation file for training
  ann_file_val = f'{anno_root}/ava_val_v2.1.csv'  # Path to the annotation file for validation

  exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'  # Path to the exclude annotation file for training
  exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'  # Path to the exclude annotation file for validation

  label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'  # Path to the label file

  proposal_file_train = f'{anno_root}/ava_dense_proposals_train.FAIR.recall_93.9.pkl'  # Path to the human detection proposals for training examples
  proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'  # Path to the human detection proposals for validation examples

  img_norm_cfg = dict(  # Config of image normalization used in data pipeline
      mean=[123.675, 116.28, 103.53], # Mean values of different channels to normalize
      std=[58.395, 57.12, 57.375],   # Std values of different channels to normalize
      to_bgr=False) # Whether to convert channels from RGB to BGR

  train_pipeline = [  # List of training pipeline steps
      dict(  # Config of SampleFrames
          type='AVASampleFrames',  # Sample frames pipeline, sampling frames from video
          clip_len=4,  # Frames of each sampled output clip
          frame_interval=16),  # Temporal interval of adjacent sampled frames
      dict(  # Config of RawFrameDecode
          type='RawFrameDecode'),  # Load and decode Frames pipeline, picking raw frames with given indices
      dict(  # Config of RandomRescale
          type='RandomRescale',   # Randomly rescale the shortedge by a given range
          scale_range=(256, 320)),   # The shortedge size range of RandomRescale
      dict(  # Config of RandomCrop
          type='RandomCrop',   # Randomly crop a patch with the given size
          size=256),   # The size of the cropped patch
      dict(  # Config of Flip
          type='Flip',  # Flip Pipeline
          flip_ratio=0.5),  # Probability of implementing flip
      dict(  # Config of Normalize
          type='Normalize',  # Normalize pipeline
          **img_norm_cfg),  # Config of image normalization
      dict(  # Config of FormatShape
          type='FormatShape',  # Format shape pipeline, Format final image shape to the given input_format
          input_format='NCTHW',  # Final image shape format
          collapse=True),   # Collapse the dim N if N == 1
      dict(  # Config of Rename
          type='Rename',  # Rename keys
          mapping=dict(imgs='img')),  # The old name to new name mapping
      dict(  # Config of ToTensor
          type='ToTensor',  # Convert other types to tensor type pipeline
          keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),  # Keys to be converted from image to tensor
      dict(  # Config of ToDataContainer
          type='ToDataContainer',  # Convert other types to DataContainer type pipeline
          fields=[   # Fields to convert to DataContainer
              dict(   # Dict of fields
                  key=['proposals', 'gt_bboxes', 'gt_labels'],  # Keys to Convert to DataContainer
                  stack=False)]),  # Whether to stack these tensor
      dict(  # Config of Collect
          type='Collect',  # Collect pipeline that decides which keys in the data should be passed to the detector
          keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],  # Keys of input
          meta_keys=['scores', 'entity_ids']),  # Meta keys of input
  ]

  val_pipeline = [  # List of validation pipeline steps
      dict(  # Config of SampleFrames
          type='AVASampleFrames',  # Sample frames pipeline, sampling frames from video
          clip_len=4,  # Frames of each sampled output clip
          frame_interval=16)  # Temporal interval of adjacent sampled frames
      dict(  # Config of RawFrameDecode
          type='RawFrameDecode'),  # Load and decode Frames pipeline, picking raw frames with given indices
      dict(  # Config of Resize
          type='Resize',  # Resize pipeline
          scale=(-1, 256)),  # The scale to resize images
      dict(  # Config of Normalize
          type='Normalize',  # Normalize pipeline
          **img_norm_cfg),  # Config of image normalization
      dict(  # Config of FormatShape
          type='FormatShape',  # Format shape pipeline, Format final image shape to the given input_format
          input_format='NCTHW',  # Final image shape format
          collapse=True),   # Collapse the dim N if N == 1
      dict(  # Config of Rename
          type='Rename',  # Rename keys
          mapping=dict(imgs='img')),  # The old name to new name mapping
      dict(  # Config of ToTensor
          type='ToTensor',  # Convert other types to tensor type pipeline
          keys=['img', 'proposals']),  # Keys to be converted from image to tensor
      dict(  # Config of ToDataContainer
          type='ToDataContainer',  # Convert other types to DataContainer type pipeline
          fields=[   # Fields to convert to DataContainer
              dict(   # Dict of fields
                  key=['proposals'],  # Keys to Convert to DataContainer
                  stack=False)]),  # Whether to stack these tensor
      dict(  # Config of Collect
          type='Collect',  # Collect pipeline that decides which keys in the data should be passed to the detector
          keys=['img', 'proposals'],  # Keys of input
          meta_keys=['scores', 'entity_ids'],  # Meta keys of input
          nested=True)  # Whether to wrap the data in a nested list
  ]

  data = dict(  # Config of data
      videos_per_gpu=16,  # Batch size of each single GPU
      workers_per_gpu=2,  # Workers to pre-fetch data for each single GPU
      val_dataloader=dict(   # Additional config of validation dataloader
          videos_per_gpu=1),  # Batch size of each single GPU during evaluation
      train=dict(   # Training dataset config
          type=dataset_type,
          ann_file=ann_file_train,
          exclude_file=exclude_file_train,
          pipeline=train_pipeline,
          label_file=label_file,
          proposal_file=proposal_file_train,
          person_det_score_thr=0.9,
          data_prefix=data_root),
      val=dict(     # Validation dataset config
          type=dataset_type,
          ann_file=ann_file_val,
          exclude_file=exclude_file_val,
          pipeline=val_pipeline,
          label_file=label_file,
          proposal_file=proposal_file_val,
          person_det_score_thr=0.9,
          data_prefix=data_root))
  data['test'] = data['val']    # Set test_dataset as val_dataset

  # optimizer
  optimizer = dict(
      # Config used to build optimizer, support (1). All the optimizers in PyTorch
      # whose arguments are also the same as those in PyTorch. (2). Custom optimizers
      # which are built on `constructor`, referring to "tutorials/5_new_modules.md"
      # for implementation.
      type='SGD',  # Type of optimizer, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details
      lr=0.2,  # Learning rate, see detail usages of the parameters in the documentation of PyTorch (for 8gpu)
      momentum=0.9,  # Momentum,
      weight_decay=0.00001)  # Weight decay of SGD

  optimizer_config = dict(  # Config used to build the optimizer hook
      grad_clip=dict(max_norm=40, norm_type=2))   # Use gradient clip

  lr_config = dict(  # Learning rate scheduler config used to register LrUpdater hook
      policy='step',  # Policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9
      step=[40, 80],  # Steps to decay the learning rate
      warmup='linear',  # Warmup strategy
      warmup_by_epoch=True,  # Warmup_iters indicates iter num or epoch num
      warmup_iters=5,   # Number of iters or epochs for warmup
      warmup_ratio=0.1)   # The initial learning rate is warmup_ratio * lr

  total_epochs = 20  # Total epochs to train the model
  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)   # Interval to save checkpoint
  workflow = [('train', 1)]   # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once
  evaluation = dict(  # Config of evaluation during training
      interval=1, save_best='mAP@0.5IOU')  # Interval to perform evaluation and the key for saving best checkpoint
  log_config = dict(  # Config to register logger hook
      interval=20,  # Interval to print the log
      hooks=[  # Hooks to be implemented during training
          dict(type='TextLoggerHook'),  # The logger used to record the training process
      ])

  # runtime settings
  dist_params = dict(backend='nccl')  # Parameters to setup distributed training, the port can also be set
  log_level = 'INFO'  # The level of logging
  work_dir = ('./work_dirs/ava/'  # Directory to save the model checkpoints and logs for the current experiments
              'slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb')
  load_from = ('https://download.openmmlab.com/mmaction/recognition/slowonly/'  # load models as a pre-trained model from a given path. This will not resume training
               'slowonly_r50_4x16x1_256e_kinetics400_rgb/'
               'slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth')
  resume_from = None  # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved
  ```

## FAQ

### Use intermediate variables in configs

Some intermediate variables are used in the config files, like `train_pipeline`/`val_pipeline`/`test_pipeline`,
`ann_file_train`/`ann_file_val`/`ann_file_test`, `img_norm_cfg` etc.

For Example, we would like to first define `train_pipeline`/`val_pipeline`/`test_pipeline` and pass them into `data`.
Thus, `train_pipeline`/`val_pipeline`/`test_pipeline` are intermediate variable.

we also define `ann_file_train`/`ann_file_val`/`ann_file_test` and `data_root`/`data_root_val` to provide data pipeline some
basic information.

In addition, we use `img_norm_cfg` as intermediate variables to construct data augmentation components.

```python
...
dataset_type = 'RawframeDataset'
data_root = 'data/kinetics400/rawframes_train'
data_root_val = 'data/kinetics400/rawframes_val'
ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)

train_pipeline = [
    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
    dict(type='RawFrameDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(
        type='MultiScaleCrop',
        input_size=224,
        scales=(1, 0.8),
        random_crop=False,
        max_wh_scale_gap=0),
    dict(type='Resize', scale=(224, 224), keep_ratio=False),
    dict(type='Flip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs', 'label'])
]
val_pipeline = [
    dict(
        type='SampleFrames',
        clip_len=32,
        frame_interval=2,
        num_clips=1,
        test_mode=True),
    dict(type='RawFrameDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='CenterCrop', crop_size=224),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]
test_pipeline = [
    dict(
        type='SampleFrames',
        clip_len=32,
        frame_interval=2,
        num_clips=10,
        test_mode=True),
    dict(type='RawFrameDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='ThreeCrop', crop_size=256),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]

data = dict(
    videos_per_gpu=8,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=ann_file_train,
        data_prefix=data_root,
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=ann_file_val,
        data_prefix=data_root_val,
        pipeline=val_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=ann_file_val,
        data_prefix=data_root_val,
        pipeline=test_pipeline))
```
