145 lines
3.1 KiB
YAML
145 lines
3.1 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: 1
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eval_during_train: True
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eval_interval: 1
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epochs: 350
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print_batch_step: 20
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use_visualdl: False
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train_mode: progressive # progressive training
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# used for static mode and model export
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image_shape: [3, 384, 384]
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save_inference_dir: ./inference
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AMP:
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scale_loss: 65536
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use_dynamic_loss_scaling: True
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# O1: mixed fp16, O2: pure fp16
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level: O1
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EMA:
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decay: 0.9999
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# model architecture
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Arch:
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name: EfficientNetV2_S
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class_num: 1000
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use_sync_bn: True
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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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- CELoss:
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weight: 1.0
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epsilon: 0.1
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Eval:
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- CELoss:
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weight: 1.0
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Optimizer:
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name: Momentum
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momentum: 0.9
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lr:
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name: Cosine
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learning_rate: 0.65 # 8gpux128bs
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warmup_epoch: 5
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regularizer:
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name: L2
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coeff: 0.00001
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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: ImageNetDataset
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image_root: ./dataset/ILSVRC2012/
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cls_label_path: ./dataset/ILSVRC2012/train_list.txt
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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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- RandCropImage:
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size: 171
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progress_size: [171, 214, 257, 300]
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scale: [0.05, 1.0]
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- RandFlipImage:
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flip_code: 1
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- RandAugmentV2:
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num_layers: 2
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magnitude: 5.0
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progress_magnitude: [5.0, 8.3333333333, 11.66666666667, 15.0]
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- NormalizeImage:
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scale: 1.0
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mean: [128.0, 128.0, 128.0]
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std: [128.0, 128.0, 128.0]
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order: ""
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sampler:
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name: DistributedBatchSampler
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batch_size: 128
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drop_last: True
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shuffle: True
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loader:
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num_workers: 8
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use_shared_memory: True
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Eval:
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dataset:
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name: ImageNetDataset
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image_root: ./dataset/ILSVRC2012/
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cls_label_path: ./dataset/ILSVRC2012/val_list.txt
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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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- CropImageAtRatio:
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size: 384
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pad: 32
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interpolation: bilinear
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- NormalizeImage:
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scale: 1.0
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mean: [128.0, 128.0, 128.0]
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std: [128.0, 128.0, 128.0]
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order: ""
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sampler:
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name: DistributedBatchSampler
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batch_size: 128
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drop_last: False
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shuffle: False
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loader:
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num_workers: 8
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use_shared_memory: True
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Infer:
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infer_imgs: docs/images/inference_deployment/whl_demo.jpg
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batch_size: 10
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transforms:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- CropImageAtRatio:
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size: 384
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pad: 32
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interpolation: bilinear
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- NormalizeImage:
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scale: 1.0
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mean: [128.0, 128.0, 128.0]
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std: [128.0, 128.0, 128.0]
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order: ""
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PostProcess:
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name: Topk
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topk: 5
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class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
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Metric:
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Train:
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- TopkAcc:
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topk: [1, 5]
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Eval:
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- TopkAcc:
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topk: [1, 5]
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