We use python files as our config system. You can find all the provided configs under $MMAction2/configs.
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.20emeans 20 epochs.{dataset}: dataset name, e.g.kinetics400,mmit, etc.{modality}: frame modality, e.g.rgb,flow, etc.
Please refer to the corresponding pages for config file structure for different tasks.
We incorporate modular design into our config system, which is convenient to conduct various experiments.
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.
# 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, valiation 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 builed on `constructor`, referring to "tutorials/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 documentaion 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 ouput
out=f'{work_dir}/results.json', # Path to output file
output_format='json') # File format of output fileWe incorporate modular design into our config system, which is convenient to conduct various experiments.
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.
# 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. Here we define clip averaging method in it
# dataset settings
dataset_type = 'RawframeDataset' # Type of dataset for training, valiation 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 normalition 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 weight 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 CenterCrop
type='TenCrop', # 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
]
data = dict( # Config of data
videos_per_gpu=32, # Batch size of each single GPU
workers_per_gpu=4, # 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 builed on `constructor`, referring to "tutorials/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 documentaion 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
topk=(1, 5)) # K value for `top_k_accuracy` metric
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 onceSome 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='Flip', flip_ratio=0),
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='Flip', flip_ratio=0),
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=4,
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))