更换文档检测模型
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_BASE_: [
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'../../yolov3/yolov3_r34_270e_coco.yml',
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_r34_270e_coco.pdparams
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slim: DistillPrune
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distill_loss: DistillYOLOv3Loss
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DistillYOLOv3Loss:
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weight: 1000
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pruner: Pruner
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Pruner:
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criterion: l1_norm
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pruned_params: ['conv2d_27.w_0', 'conv2d_28.w_0', 'conv2d_29.w_0',
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'conv2d_30.w_0', 'conv2d_31.w_0', 'conv2d_32.w_0',
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'conv2d_34.w_0', 'conv2d_35.w_0', 'conv2d_36.w_0',
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'conv2d_37.w_0', 'conv2d_38.w_0', 'conv2d_39.w_0',
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'conv2d_41.w_0', 'conv2d_42.w_0', 'conv2d_43.w_0',
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'conv2d_44.w_0', 'conv2d_45.w_0', 'conv2d_46.w_0']
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pruned_ratios: [0.5,0.5,0.5,0.5,0.5,0.5,0.7,0.7,0.7,0.7,0.7,0.7,0.8,0.8,0.8,0.8,0.8,0.8]
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# Weights of yolov3_mobilenet_v1_voc
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pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams
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slim: PrunerQAT
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PrunerQAT:
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criterion: fpgm
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pruned_params: ['conv2d_27.w_0', 'conv2d_28.w_0', 'conv2d_29.w_0',
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'conv2d_30.w_0', 'conv2d_31.w_0', 'conv2d_32.w_0',
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'conv2d_34.w_0', 'conv2d_35.w_0', 'conv2d_36.w_0',
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'conv2d_37.w_0', 'conv2d_38.w_0', 'conv2d_39.w_0',
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'conv2d_41.w_0', 'conv2d_42.w_0', 'conv2d_43.w_0',
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'conv2d_44.w_0', 'conv2d_45.w_0', 'conv2d_46.w_0']
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pruned_ratios: [0.1,0.2,0.2,0.2,0.2,0.1,0.2,0.3,0.3,0.3,0.2,0.1,0.3,0.4,0.4,0.4,0.4,0.3]
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print_prune_params: False
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quant_config: {
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'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max',
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'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.9,
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'quantizable_layer_type': ['Conv2D', 'Linear']}
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print_qat_model: True
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