114 lines
2.5 KiB
YAML
114 lines
2.5 KiB
YAML
# global configs
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Global:
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checkpoints: null
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pretrained_model: null
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output_dir: "./output/"
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device: "gpu"
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save_interval: 5
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eval_during_train: True
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eval_interval: 1
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epochs: 30
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print_batch_step: 20
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use_visualdl: False
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# used for static mode and model export
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image_shape: [3, 256, 192]
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save_inference_dir: "./inference"
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use_multilabel: True
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# model architecture
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Arch:
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name: "ResNet50"
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pretrained: True
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class_num: 26
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infer_add_softmax: False
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# loss function config for traing/eval process
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Loss:
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Train:
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- MultiLabelLoss:
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weight: 1.0
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weight_ratio: True
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size_sum: True
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Eval:
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- MultiLabelLoss:
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weight: 1.0
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weight_ratio: True
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size_sum: True
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Optimizer:
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name: Adam
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lr:
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name: Piecewise
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decay_epochs: [12, 18, 24, 28]
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values: [0.0001, 0.00001, 0.000001, 0.0000001]
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regularizer:
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name: 'L2'
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coeff: 0.0005
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clip_norm: 10
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# data loader for train and eval
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DataLoader:
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Train:
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dataset:
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name: MultiLabelDataset
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image_root: "dataset/attribute/data/"
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cls_label_path: "dataset/attribute/trainval.txt"
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label_ratio: True
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transform_ops:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- ResizeImage:
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size: [192, 256]
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- Padv2:
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size: [212, 276]
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pad_mode: 1
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fill_value: 0
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- RandomCropImage:
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size: [192, 256]
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- RandFlipImage:
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flip_code: 1
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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sampler:
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name: DistributedBatchSampler
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batch_size: 64
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drop_last: True
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shuffle: True
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loader:
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num_workers: 4
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use_shared_memory: True
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Eval:
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dataset:
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name: MultiLabelDataset
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image_root: "dataset/attribute/data/"
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cls_label_path: "dataset/attribute/test.txt"
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label_ratio: True
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transform_ops:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- ResizeImage:
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size: [192, 256]
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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sampler:
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name: DistributedBatchSampler
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batch_size: 64
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drop_last: False
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shuffle: False
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loader:
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num_workers: 4
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use_shared_memory: True
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Metric:
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Eval:
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- ATTRMetric:
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