example.yaml

# PaddleVieo Example Configuration, please refer to "docs/en/config.md" for more information.
MODEL: #MODEL field
    framework: "Recognizer2D" #Mandatory ["Recognizer1D", "Recognizer2D", "Recognizer3D", "BMNLocalizer"], indicate the type of network, please refer to the 'paddlevideo/modeling/framework/'.
    backbone:
        name: "ResNet" #Optional, indicate the type of backbone, please refer to the 'paddlevideo/modeling/backbones/'.
        pretrained: "data/ResNet50_vd_ssld_v2_pretrained.pdparams" #Optional, pretrained backbone params path. pass "" or " " without loading from files.
        depth: 50 #Optional, the depth of backbone architecture.
    head:
        name: "TSNHead" #Mandatory, indicate the type of head, please refer to the 'paddlevideo/modeling/heads'
        num_classes: 101 #Optional, the number of classes to be classified.
        in_channels: 2048 #Optional, input channels of the extracted feature.
        drop_ratio: 0.4 #Optional, the ratio of dropout.
        std: 0.01 #Optional, std value in params initialization.

DATASET: #DATASET field
    batch_size: 4 #Mandatory, batch size per gpu.
    valid_batch_size: 4 #Optional, valid batch size per gpu.
    test_batch_size: 4 #Optional, test batch size per gpu.
    num_workers: 2 #Mandatory, the number of subprocess on each GPU.
    train:
        format: "VideoDataset" #Mandatory, indicate the type of train dataset, please refer to the 'paddlevidel/loader/dateset'.
        data_prefix: "" #Optional, train data root path.
        file_path: "data/ucf101/ucf101_train_split_1_videos.txt" #Mandatory, train data index file path
        suffix: ".avi"
    valid:
        format: "VideoDataset" #Mandatory, indicate the type of valid dataset, please refer to the 'paddlevidel/loader/dateset'
        data_prefix: "" #Optional, valid data root path
        file_path: "data/ucf101/ucf101_val_split_1_videos.txt" #Mandatory, valid data index file path
        suffix: ".avi"
    test:
        format: "VideoDataset" #Mandotary, indicate the type of test dataset, please refer to the 'paddlevideo/loader/dataset'.
        data_prefix: "" #Optional, test data root path.
        file_path: "data/ucf101/ucf101_val_split_1_videos.txt" #Mandotary, test data index file path.
        suffix: ".avi"

PIPELINE: #PIPELINE field
    train: #Mandotary, indicate the pipeline to deal with the training data, please refer to the 'paddlevideo/loader/pipelines/'
        decode:
            name: "VideoDecoder" #Decoder type.
        sample:
            name: "Sampler" #Sampler type.
            num_seg: 8
            seg_len: 1
            valid_mode: False
        transform: #Mandotary, image transform operator
            - Scale:
                short_size: 256
            - RandomCrop:
                target_size: 224
            - RandomFlip:
            - Image2Array:
            - Normalization:
                mean: [0.485, 0.456, 0.406]
                std: [0.229, 0.224, 0.225]

    valid: #Mandatory, indicate the pipeline to deal with the validing data. please refer to the 'paddlevideo/loader/pipelines/'
        decode:
            name: "VideoDecoder" #Decoder type.
        sample:
            name: "Sampler" #Sampler type.
            valid_mode: True
            num_seg: 8
            seg_len: 1
        transform: #Mandatory, image transform operator.
            - Scale:
                short_size: 256
            - CenterCrop:
                target_size: 224
            - Image2Array:
            - Normalization:
                mean: [0.485, 0.456, 0.406]
                std: [0.229, 0.224, 0.225]

    test: #Mandatory, indicate the pipeline to deal with the validing data. please refer to the 'paddlevideo/loader/pipelines/'
        decode:
            name: "VideoDecoder" #Decoder type.
        sample:
            name: "Sampler" #Sampler type.
            valid_mode: True
            num_seg: 8
            seg_len: 1
        transform: #Mandatory, image transform operator.
            - Scale:
                short_size: 256
            - CenterCrop:
                target_size: 224
            - Image2Array:
            - Normalization:
                mean: [0.485, 0.456, 0.406]
                std: [0.229, 0.224, 0.225]

OPTIMIZER: #OPTIMIZER field
    name: 'Momentum' #Mandatory, the type of optimizer, please to the 'paddlevideo/solver/'
    momentum: 0.9
    learning_rate: #Mandatory, the type of learning rate scheduler, please refer to the 'paddlevideo/solver/'
        name: 'PiecewiseDecay'
        boundaries: [30, 60]
        values: [0.00025, 0.000025, 0.0000025]
    weight_decay:
        name: 'L2'
        value: 1e-4

METRIC:
    name: 'CenterCropMetric'

INFERENCE:
    name: 'ppTSM_Inference_helper'
    num_seg: 8
    target_size: 224

model_name: "TSN" #Mandatory, model name.
log_interval: 20 #Optional, the interval of logger.
epochs: 5 #Mandatory, total epoch
log_level: "DEBUG" #Optional, the logger level.