我按照Doc中的方法，基于vox_mobile.pdparams 的预训练模型，并把mode改成了both进行迁移学习，使用的是经过预处理的voxceleb1的数据集，但是无论我怎么训练重建损失都没办法收敛到0.015这么小，我只能训练到0.04以上，所以想请教下如何更近一步减小reconstruction loss，而且除了验证的指标，在训练期间的其它损失指标也都很不稳定下面是我的配置，和repository里提供的是一样的： epochs: 100 output_dir: output_dir

dataset: train: name: FirstOrderDataset batch_size: 8 num_workers: 4 use_shared_memory: False phase: train

dataroot: data/first_order/Voxceleb/

dataroot: /data/yyking/video-preprocessing/vox-png
frame_shape: [256, 256, 3]
id_sampling: True
pairs_list: None
time_flip: True
num_repeats: 75
create_frames_folder: False
transforms:
  - name: PairedRandomHorizontalFlip
    prob: 0.5
    keys: [image, image]
  - name: PairedColorJitter
    brightness: 0.1
    contrast: 0.1
    saturation: 0.1
    hue: 0.1
    keys: [image, image]

test: name: FirstOrderDataset dataroot: /data/yyking/video-preprocessing/vox-png phase: test batch_size: 1 num_workers: 1 time_flip: False id_sampling: False create_frames_folder: False frame_shape: [ 256, 256, 3 ]

model: name: FirstOrderModelMobile mode: both # should be kp_detector, generator, both kp_weight_path: /home/cmcm/jaccob/first-order-model/vox_mobile.pdparams gen_weight_path: /home/cmcm/jaccob/first-order-model/vox_mobile.pdparams common_params: num_kp: 10 num_channels: 3 estimate_jacobian: True generator: name: FirstOrderGenerator kp_detector_cfg: temperature: 0.1 block_expansion: 32 max_features: 256 scale_factor: 0.25 num_blocks: 5 mobile_net: True generator_cfg: block_expansion: 32 max_features: 256 num_down_blocks: 2 num_bottleneck_blocks: 6 estimate_occlusion_map: True dense_motion_params: block_expansion: 32 max_features: 256 num_blocks: 5 scale_factor: 0.25 mobile_net: True generator_ori: name: FirstOrderGenerator kp_detector_cfg: temperature: 0.1 block_expansion: 32 max_features: 1024 scale_factor: 0.25 num_blocks: 5 generator_cfg: block_expansion: 64 max_features: 512 num_down_blocks: 2 num_bottleneck_blocks: 6 estimate_occlusion_map: True dense_motion_params: block_expansion: 64 max_features: 1024 num_blocks: 5 scale_factor: 0.25 discriminator: name: FirstOrderDiscriminator discriminator_cfg: scales: [1] block_expansion: 32 max_features: 512 num_blocks: 4 sn: True train_params: num_epochs: 100 scales: [1, 0.5, 0.25, 0.125] checkpoint_freq: 50 transform_params: sigma_affine: 0.05 sigma_tps: 0.005 points_tps: 5 loss_weights: generator_gan: 1 discriminator_gan: 1 feature_matching: [10, 10, 10, 10] perceptual: [10, 10, 10, 10, 10] equivariance_value: 10 equivariance_jacobian: 10

lr_scheduler: name: MultiStepDecay epoch_milestones: [237360, 356040] lr_generator: 2.0e-4 lr_discriminator: 2.0e-4 lr_kp_detector: 2.0e-4

reconstruction_params: num_videos: 1000 format: '.mp4'

animate_params: num_pairs: 50 format: '.mp4' normalization_params: adapt_movement_scale: False use_relative_movement: True use_relative_jacobian: True

visualizer_params: kp_size: 5 draw_border: True colormap: 'gist_rainbow'

log_config: interval: 10 visiual_interval: 10

validate: interval: 30000 save_img: true

snapshot_config: interval: 1

optimizer: name: Adam

export_model:

{}

PaddlePaddle / PaddleGAN

关于压缩版First Order Motion 模型的重建损失 #473

dataroot: data/first_order/Voxceleb/