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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.

Modify config through script arguments
Config File Structure
Config File Naming Convention
Config System for Action localization
Config System for Action Recognition
Config System for Spatio-Temporal Action Detection
FAQ
Use intermediate variables in configs

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 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.

```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.

...
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))

758 lines (686 with data), 43.3 kB

Tutorial 1: Learn about Configs

Modify config through script arguments

Config File Structure

Config File Naming Convention

Config System for Action localization

model settings

model training and testing settings

dataset settings

optimizer

learning policy

runtime settings

Config System for Action Recognition

model settings

dataset settings

optimizer

learning policy

runtime settings

Config System for Spatio-Temporal Action Detection

model setting

dataset settings

optimizer

runtime settings

FAQ

Use intermediate variables in configs