206 lines
4.7 KiB
YAML
206 lines
4.7 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: 100
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print_batch_step: 20
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use_visualdl: False
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eval_mode: retrieval
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retrieval_feature_from: features # 'backbone' or 'features'
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re_ranking: False
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use_dali: 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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AMP:
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scale_loss: 65536
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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: RecModel
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infer_output_key: features
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infer_add_softmax: False
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Backbone:
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name: PPLCNetV2_base_ShiTu
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pretrained: True
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use_ssld: True
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class_expand: &feat_dim 512
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BackboneStopLayer:
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name: flatten
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Neck:
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name: BNNeck
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num_features: *feat_dim
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weight_attr:
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initializer:
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name: Constant
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value: 1.0
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bias_attr:
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initializer:
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name: Constant
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value: 0.0
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learning_rate: 1.0e-20 # NOTE: Temporarily set lr small enough to freeze the bias to zero
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Head:
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name: FC
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embedding_size: *feat_dim
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class_num: 192612
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weight_attr:
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initializer:
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name: Normal
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std: 0.001
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bias_attr: 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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- CELoss:
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weight: 1.0
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epsilon: 0.1
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- TripletAngularMarginLoss:
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weight: 1.0
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feature_from: features
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margin: 0.5
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reduction: mean
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add_absolute: True
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absolute_loss_weight: 0.1
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normalize_feature: True
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ap_value: 0.8
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an_value: 0.4
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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: 0.06 # for 8gpu x 256bs
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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/
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cls_label_path: ./dataset/train_reg_all_data_v2.txt
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relabel: 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: [224, 224]
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return_numpy: False
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interpolation: bilinear
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backend: cv2
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- RandFlipImage:
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flip_code: 1
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- Pad:
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padding: 10
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backend: cv2
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- RandCropImageV2:
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size: [224, 224]
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- RandomRotation:
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prob: 0.5
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degrees: 90
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interpolation: bilinear
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- ResizeImage:
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size: [224, 224]
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return_numpy: False
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interpolation: bilinear
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backend: cv2
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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: hwc
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sampler:
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name: PKSampler
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batch_size: 256
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sample_per_id: 4
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drop_last: False
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shuffle: True
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sample_method: "id_avg_prob"
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id_list: [50030, 80700, 92019, 96015] # be careful when set relabel=True
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ratio: [4, 4]
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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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Query:
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dataset:
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name: VeriWild
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image_root: ./dataset/Aliproduct/
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cls_label_path: ./dataset/Aliproduct/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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- ResizeImage:
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size: [224, 224]
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return_numpy: False
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interpolation: bilinear
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backend: cv2
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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: hwc
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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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Gallery:
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dataset:
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name: VeriWild
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image_root: ./dataset/Aliproduct/
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cls_label_path: ./dataset/Aliproduct/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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- ResizeImage:
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size: [224, 224]
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return_numpy: False
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interpolation: bilinear
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backend: cv2
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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: hwc
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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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- Recallk:
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topk: [1, 5]
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- mAP: {}
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