>|h'ddlZddlmZddlZddlZddlZddlm Z m Z ddl m Z ddl mZedZe j Zedk(r-ddlZdd lmZdd lmZdd lmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#n.dd lmZdd l$mZdd l%mZdd l&mZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#d Z'Gdde Z(dZ)defdZ*dZ+ddZ,ddZ-defdZ.y)N)List) package_utils folder_utils)FacialRecognition)Loggerzbasemodels.VGGFace)module)backend)Model) ActivationAddBatchNormalization ConcatenateConv2DDepthwiseConv2DGlobalAveragePooling2DInputReshapeMultiplyReLUPReLU)kerasz_https://github.com/HamadYA/GhostFaceNets/releases/download/v1.2/GhostFaceNet_W1.3_S1_ArcFace.h5c@eZdZdZdZdej deefdZ y)GhostFaceNetClientaN GhostFaceNet model (GhostFaceNetV1 backbone) Repo: https://github.com/HamadYA/GhostFaceNets Pre-trained weights: https://github.com/HamadYA/GhostFaceNets/releases/tag/v1.2 GhostFaceNet_W1.3_S1_ArcFace.h5 ~ 16.5MB Author declared that this backbone and pre-trained weights got 99.7667% accuracy on LFW cLd|_d|_d|_t|_y)N GhostFaceNet)pr) model_name input_shape output_shape load_modelmodel)selfs _/var/www/html/test/engine/venv/lib/python3.12/site-packages/deepface/basemodels/GhostFaceNet.py__init__zGhostFaceNetClient.__init__Es"(%\ imgreturncf|j|djdjS)NF)trainingr)r#numpytolist)r$r(s r%find_embeddingsz"GhostFaceNetClient.find_embeddingsKs.zz#z.446q9@@BBr'N) __name__ __module__ __qualname____doc__r&npndarrayrfloatr.r'r%rr<s*" C2::C$u+Cr'rcBt}tj}|dz}tjj |durIt jdtjt|dt jd||j||S)Nz%/.deepface/weights/ghostfacenet_v1.h5TzGPre-trained weights is downloaded from {PRETRAINED_WEIGHTS} to {output}F)quietz*Pre-trained weights is just downloaded to ) GhostFaceNetV1rget_deepface_homeospathisfileloggerinfogdowndownloadPRETRAINED_WEIGHTS load_weights)r#homeoutputs r%r"r"Qs}  E  ) ) +D ; ;F ww~~fT) ]^ )6? @IJ v Lr'r)c td}d}t|ddddtjj dd d  |}t d |}t d|}gd}gd}gd}gd}gd}|}t|||||D](\} } } } } | |k(xr| dk( }t|| | | | | |}| }*tdddddtjj dd d  |}t d |}t d|}t||d}|jd}|jd}tjj|jddd|}tjj ddd |}tjjd!dd"d#d$%|}tjjd&'|}tjj ddd"d()|}tjj d*d+d,-|}tj j|||j"'}t%|.}|S)/z Build GhostFaceNetV1 model. Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py Returns: model (Model) )rr)shape)rGrGr sameF@fan_outtruncated_normalscalemode distributionstridespaddinguse_biaskernel_initializeraxisrelu)rGrGrGr[rGrGrGrGrGrGr[r[r[r[r[)rI@\r]8ir`iphrarbrb)rI rd4rehrfrfrfrgrhrhrhrh)r r rir rir r r r r rir r r r )rrr(rrrrr^rlrr_rrcr r validr9)inputsoutputsnamerGDC_dw)rUrrgGz?gMbP? GDC_batchnorm)momentumepsilonrrrT glorot_normalGDC_conv)rUrVrr GDC_flatten)rr pre_embedding)rurvrOrrlinearfloat32 embedding)dtyperr)r#)rrr initializersVarianceScalingrr zipghost_bottleneckr rprqlayersrrHFlattenmodelsrrreplace_relu_with_prelu)rp out_channelnn dwkernelsexpsouts strides_set reductionspre_outdwkstrideexpout reductionshortcutxxr}embedding_fp32r#s r%r9r9as 'FK  --==I4F>   B %  $R (B F B B@I ZD VDBKMJG,/ ;dT^,_(VS#yw66Q;7 b#vsCH M   --==I4F>   B %  $R (B F B B fb/? @BYYq\F AB  % %bhhqkE % QRT UB  ( ($O ( \]_ `B    Q/    B   =  1" 5B //uD0 I\\,,XY[,YZcdN LL  v~BGG  DE #% 0E Lr'c tjdk(rdnd}|j|}t|}t dd|f|}t |ddt jjddd |}td |}t |ddt jjddd |}td |}t||gS) ] Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py channels_firstr rWTrKrLrMrN) kernel_sizerUrVrZ hard_sigmoid) Kimage_data_formatrHrrrrrrr r)rpr channel_axisfiltersses r% se_modulers ++-1AA1rLll<(G "  !& )B "!Q !" %B  --==I4F>    B F B B  --==I4F>    B $N #B 'B 8:vrl ##r'c |dz}t||dddtjjddd |}t d |}|rt d |}t |d ddtjjddd|}t d |}|rt d |}t||gS)rriFrnrJrKrLrMrN)rUrSrTrVrWrXrZr rTrUdepthwise_initializer)rrrrrr rr)rpr convkerneldwkerneladd_activationconv_out_channelccrs r% ghost_modulersax  --==I4F>   B %  $R (B Z  #  #00@@I4FA    B %  $R (B Z  # ;="b ""r'c Ft||d}|dkDrHt||ddtjj ddd  |}t d |}|d kDr t ||}t||d}|rt||ddtjj ddd  |}t d |}t|ddddtjj ddd |}t d |}n|}t||gS)rT)rr rJFrKrLrMrNrrWrXrrnrorR) rrrrrrrrr ) rprrSrrrrrrs r%rrsg fc$ 7B{ _  "'"4"4"D"D 8J#E#   ) R ( ,1} r9 % b#e 4B _  "'"4"4"D"D 8J#E#   ) R ( ,  V  $11AA 8J B     ) R ( ,  35"b?r'cd}tjj|jdd}tjj |||S)aA Replaces relu activation function in the built model with prelu. Refactored from github.com/HamadYA/GhostFaceNets/blob/main/backbones/ghost_model.py Args: model (Model): built model with relu activation functions Returns model (Model): built model with prelu activation functions c&t|ts7t|trp|jtj j k(rI|jjdd}tddgtjjd|S|S)N_relu_prelur rig?) shared_axesalpha_initializerrr) isinstancerr activationr activationsrZrrreplacertfrConstant)layer layer_names r% convert_reluz-replace_relu_with_prelu..convert_relu2su eT " uj )e.>.>%BSBSBXBX.X++GX>JF"$//":":4"@   r'r N) input_tensorsclone_function)rrrr r clone_model)r#rrs r%rr(sG LL&&u'8'8'<=M << # #EWc # ddr')r rGT)T)/r;typingrr@r,r3 tensorflowrdeepface.commonsrr!deepface.models.FacialRecognitionrdeepface.commons.loggerrr>get_tf_major_versiontf_majorrr r keras.modelsr keras.layersr r rrrrrrrrrrtensorflow.kerastensorflow.keras.modelstensorflow.keras.layersrBrr"r9rrrrr6r'r%rs  9?* + , -= - - / q=""    !--    "wC*C*  FFR%$P#D1heeer'