158 lines
3.4 KiB
YAML
158 lines
3.4 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_r50_vd_distill
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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: 100
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print_batch_step: 10
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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, 224, 224]
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save_inference_dir: ./inference
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to_static: True
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AMP:
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scale_loss: 128.0
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use_dynamic_loss_scaling: True
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# O1: mixed fp16
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level: O1
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# model architecture
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Arch:
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name: "DistillationModel"
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class_num: &class_num 1000
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# if not null, its lengths should be same as models
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pretrained_list:
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# if not null, its lengths should be same as models
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freeze_params_list:
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- True
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- False
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infer_model_name: "Student"
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models:
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- Teacher:
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name: ResNet50_vd
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class_num: *class_num
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pretrained: True
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use_ssld: True
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- Student:
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name: PPLCNet_x2_5
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class_num: *class_num
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pretrained: 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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- DistillationDMLLoss:
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weight: 1.0
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model_name_pairs:
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- ["Student", "Teacher"]
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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: 0.2
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warmup_epoch: 5
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regularizer:
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name: 'L2'
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coeff: 0.00004
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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: 224
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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: 128
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drop_last: False
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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/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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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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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: 256
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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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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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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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- ToCHWImage:
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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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- DistillationTopkAcc:
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model_key: "Student"
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topk: [1, 5]
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Eval:
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- DistillationTopkAcc:
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model_key: "Student"
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topk: [1, 5]
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