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- batch_size: 16 # total batch size: 16
- iters: 100000
- train_dataset:
- type: MattingDataset
- dataset_root: data/PPM-100
- train_file: train.txt
- transforms:
- - type: LoadImages
- - type: LimitShort
- max_short: 512
- - type: RandomCrop
- crop_size: [512, 512]
- - type: Padding
- target_size: [512, 512]
- - type: RandomDistort
- - type: RandomBlur
- prob: 0.1
- - type: RandomSharpen
- prob: 0.2
- - type: RandomNoise
- prob: 0.5
- - type: RandomReJpeg
- prob: 0.2
- - type: RandomHorizontalFlip
- - type: Normalize
- mode: train
- val_dataset:
- type: MattingDataset
- dataset_root: data/PPM-100
- val_file: val.txt
- transforms:
- - type: LoadImages
- - type: LimitShort
- max_short: 512
- - type: ResizeToIntMult
- mult_int: 32
- - type: Normalize
- mode: val
- get_trimap: False
- model:
- type: PPMattingV2
- backbone:
- type: STDC1
- pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet1.tar.gz
- decoder_channels: [128, 96, 64, 32, 16]
- head_channel: 8
- dpp_output_channel: 256
- dpp_merge_type: add
-
- optimizer:
- type: sgd
- momentum: 0.9
- weight_decay: 5.0e-4
- lr_scheduler:
- type: PolynomialDecay
- learning_rate: 0.01
- end_lr: 0
- power: 0.9
- warmup_iters: 1000
- warmup_start_lr: 1.0e-5
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