mmaction2 指南 (1) 配置文件_mmaction2 配置文件命名含义

mmaction2 指南

1. 配置文件

https://github.com/open-mmlab/mmaction2/blob/master/docs/tutorials/1_config.md

配置文件命名规则

{model}_[model setting]_{backbone}_[misc]_{data setting}_[gpu x batch_per_gpu]_{schedule}_{dataset}_{modality}
模型名称_模型specific配置_bakcbone_【e.g. dense, 320p, video, etc】_【帧采样设置({clip_len}x{frame_interval}x{num_clips}】_[gpu x batch_per_gpu]_【epoch，e.g. 20e】_【数据集名称】_【模态】
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配置文件structure

Action localizartion 配置

• BMN例子

# model settings 模型配置
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 数据集配置（预处理管道，gpu分布配置）
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 流程
    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
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