o #j,@sddgdggddgdggddgdggddgdggddgdggddgdggdZidddgdggdddgdggd ddgdggd dgdggd ddgdggd dgdggd dgdggddgdggddgdggdddgdggddgdggdddgdggdddgdggdddgdggdddgdggddgdggdddgdggiddgdggddgdggddgdggddgdggddgdggddgdggddgdggdddgdggd dgdggd!dgdggd"dgdggd#dd$gdggd%dgdggd&dgdggd'dgdggd(dgdggd)dgdggid*dgdggd+ddgdggd,ddgdggd-dgdggd.dgdggd/dd0gd1ggd2dd0gd1ggd3dgdggd4dgdggd5dgdggd6dgdggd7dgdggd8dgdggd9dgdggd:dgdggd;dgdggddgdggd?dgdggd@dgdggdAdgdggdBdgdggdCdgdggdDdEgdFggdGdgdggdHdgdggdIdgdggdJdgdggdKgdggdLdgdggdMdgdggdNdOdPgdggdQdOdPgdggidRdgdSggdTdgdggdUdgdggdVdgdggdWdgdggdXdgddYggdZddgdggd[dgdggd\dgdggd]dd^gdggd_dgdggd`gdadggdbddcgdggdddgdggdedggdfgdgdgdggdhdgdggdgdggdgdggdgdggdgdggdiZeZeeGdjdkdkZGdldmdmeZGdndodoeZGdpdqdqeZdrS)sZInputFilterOutputXYZOut)Zconv2dZdepthwise_conv2dZconv2d_transposemulmatmul matmul_v2rrrpool2delementwise_addconcatsoftmaxargmaxZ transposeequalZgatherZ greater_equal greater_thanZ less_equal less_thanmean not_equalZreshapeZreshape2Z transpose2nearest_interptrilinear_interpsliceZsqueezeelementwise_subrelurelu6 leaky_reluZpreluAlphatanhswishZdropoutZ batch_norm layer_normsigmoidelementwise_mulelementwise_pow hard_swish hard_sigmoidZgruZWeightZHiddenZlstmZpad2dZpad3dflattenZflatten2Z unsqueeze2Zflatten_contiguous_rangesplitZsqueeze2nearest_interp_v2bilinear_interpbilinear_interp_v2Zfill_constant_batch_size_likeZarg_maxabsZassigncastclipZ box_coderZPriorBoxZ OutputBoxcropZcumsumZ expand_v2Z fill_any_likeZ fill_constantgeluZ instance_normZ lookup_tableWZIdsZlookup_table_v2ZnormZNormZp_normpow reduce_meanstackZtop_k_v2ZIndices logical_andZ logical_notZmeshgridZ roi_alignZROIsZ strided_slicewhere) ConditionrrZ grid_samplerZGridZtileZ group_norm)rZMeanZVariance reduce_sumZsquare)softplusZshuffle_channel reduce_maxscalec@sBeZdZdZgdfddZeddZeddZed d Zd S) BaseQuantizera Basic quantization configuration class, which configures some hyperparameters required for quantization, including the list of op types to be quantized, quantization bit number for weight and activation and the range of quantization values. Args: quantizable_op_type(list[str], optional): List the type of ops that will be quantized. Default is []. If quantizable_op_type is [], it will use the default quantization op type of the qunat config in the current Quantizer. quant_bits(int, optional): Quantization bit number for weight and activation. Default is 8. cCs||_||_d|_d|_dSNi)_quantizable_op_type _quant_bits _quant_min _quant_maxselfZquantizable_op_typeZ quant_bitsrAh/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/static/quantization/quant_config.py__init__s zBaseQuantizer.__init__cCs<tt}|jrg}|jD] }||vr||q|S|S)z Operation type list which should support weight quantization. And before these ops, quant dequant nodes will be inserted. )list#SUPPORT_WEIGHT_QUANTIZATION_OP_DICTkeysr;append)r@Zbase_weight_op_type_listZ weight_list_op_typerArArBweight_quant_operation_typess  z*BaseQuantizer.weight_quant_operation_typescCsDtt}g}|jr|jD] }||vr||q|Sgd}|S) Operation type list which should support activation quantization. And before these ops, quant dequant nodes will be inserted. )rrr)rD SUPPORT_ACT_QUANTIZATION_OP_DICTrFr;rG)r@Zbase_act_op_type_listZact_quant_op_listrHrArArB activation_quant_operation_typess   z.BaseQuantizer.activation_quant_operation_typescCs ttS)aU Operation type list for observer in quantization. These nodes only count the calibration boundary scale and do not participate in the fake quantization. In order to facilitate the deployment of the prediction engine, quant and dequant nodes will be inserted after these ops when exporting the model. )rDrKrFr@rArArBobserver_operation_typess z&BaseQuantizer.observer_operation_typesN) __name__ __module__ __qualname____doc__rCpropertyrIrLrNrArArArBr7s   r7c2eZdZdZgdffdd ZeddZZS)TensorRTQuantizera TensorRT quantization configuration class. Args: quantizable_op_type(list[str], optional): List the type of ops that will be quantized. Default is []. If quantizable_op_type is [], it will use the default quantization op type of the qunat config in the current Quantizer. quant_bits(int, optional): Quantization bit number for weight and activation. Default is 8. r8c&t||_||_d|_d|_dSr9superrCr;r<r=r>r? __class__rArBrC  zTensorRTQuantizer.__init__cCgdS)rJ)!rr rrrr r r rrrrrrr4r+r!r rrrr#r%rrr$r%r&r)r-r.r3r5rArMrArArBrLz2TensorRTQuantizer.activation_quant_operation_typesrOrPrQrRrCrSrL __classcell__rArArYrBrU  rUcrT)MKLDNNQuantizera MKLDNN quantization configuration class. Args: quantizable_op_type(list[str], optional): List the type of ops that will be quantized. Default is []. If quantizable_op_type is [], it will use the default quantization op type of the qunat config in the current Quantizer. quant_bits(int, optional): Quantization bit number for weight and activation. Default is 8. r8crVr9rWr?rYrArBrCr[zMKLDNNQuantizer.__init__cCr\)rJ)rr rr rr$r#rArMrArArBrL!r]z0MKLDNNQuantizer.activation_quant_operation_typesr^rArArYrBra r`racs&eZdZdZgdffdd ZZS)ARMCPUQuantizera ARM CPU with Paddle Lite quantization configuration class. Args: quantizable_op_type(list[str], optional): List the type of ops that will be quantized. Default is []. If quantizable_op_type is [], it will use the default quantization op type of the qunat config in the current Quantizer. quant_bits(int, optional): Quantization bit number for weight and activation. Default is 8. r8crV)Nir:rWr?rYrArBrC>r[zARMCPUQuantizer.__init__)rOrPrQrRrCr_rArArYrBrb2s  rbN) rErKcopyZSUPPORT_QUANTIZATION_OP_DICTupdater7rUrarbrArArArBsz                               ! " #$% & '() * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A BCD E F G H IJK L MN OPQ RS T U V   ^ IB(