attention_lstm.yaml

MODEL: #MODEL field
    framework: "Recognizer1D" #Mandatory, indicate the type of network, associate to the 'paddlevideo/modeling/framework/' .
    head:
        name: "AttentionLstmHead" #Mandatory, indicate the type of head, associate to the 'paddlevideo/modeling/heads'
        num_classes: 3862 #Optional, the number of classes to be classified.
        feature_num: 2
        embedding_size: 512
        lstm_size: 1024

DATASET: #DATASET field
    batch_size: 128  #Mandatory, bacth size
    num_workers: 0  #Mandatory, the number of subprocess on each GPU.
    shuffle_valid: True
    train:
        format: "FeatureDataset" #Mandatory, indicate the type of dataset, associate to the 'paddlevidel/loader/dateset'
        file_path:   "data/dataset/youtube-8m/train.list" #Mandatory, train data index file path
    valid:
        format: "FeatureDataset" #Mandatory, indicate the type of dataset, associate to the 'paddlevidel/loader/dateset'
        file_path:   "data/dataset/youtube-8m/val.list" #Mandatory, train data index file path
    test:
        format: "FeatureDataset" #Mandatory, indicate the type of dataset, associate to the 'paddlevidel/loader/dateset'
        data_prefix: ""  #Mandatory, valid data root path
        file_path:   "data/dataset/youtube-8m/val.list" #Mandatory, train data index file path


PIPELINE: #PIPELINE field
    train: #Mandotary, indicate the pipeline to deal with the training data, associate to the 'paddlevideo/loader/pipelines/'
        decode:
            name: "FeatureDecoder" #"VideoDecoder"
            num_classes: 3862
    valid: #Mandatory, indicate the pipeline to deal with the validing data. associate to the 'paddlevideo/loader/pipelines/'
        decode:
            name: "FeatureDecoder" #"VideoDecoder"
            num_classes: 3862
    test:
        decode:
            name: "FeatureDecoder" #"VideoDecoder"
            num_classes: 3862

OPTIMIZER: #OPTIMIZER field
    name: 'RMSProp' #Mandatory, the type of optimizer, associate to the 'paddlevideo/solver/'
    centered: True
    learning_rate: #Mandatory, the type of learning rate scheduler, associate to the 'paddlevideo/solver/'
        name: 'PiecewiseDecay'
        boundaries: [5, 8]
        values: [0.00025, 0.000025, 0.0000025]
    weight_decay:
        name: 'L2'
        value: 4e-4

model_name: "AttentionLstm"
log_interval: 1 #Optional, the interal of logger, default:10
epochs: 10 #Mandatory, total epoch
save_interval: 5
log_level: "INFO"