hh$W dZddlmcmZddlmZddlmZddl m Z ddl m Z ddl mZddl mZdd lmZd Zd d d ddddddZda d/dZd0dZd1dZd2dZd1dZ d3dZd4dZeddd d5dZedd  d5d!Zed"d# d5d$Zed%d&d6d'Zed(d)d7d*Z ejBjEd+ejFejH,e_ej@je _d-Z%e&ed.eje%ze&ed.eje%ze&ed.eje%zy)8zResNet models for TF-Keras. Reference: - [Deep Residual Learning for Image Recognition]( https://arxiv.org/abs/1512.03385) (CVPR 2015) N)backend)imagenet_utils)training)VersionAwareLayers) data_utils) layer_utils) keras_exportzDhttps://storage.googleapis.com/tensorflow/keras-applications/resnet/) 2cb95161c43110f7111970584f804107 4d473c1dd8becc155b73f8504c6f6626) f1aeb4b969a6efcfb50fad2f0c20cfc5 88cf7a10940856eca736dc7b7e228a21) 100835be76be38e30d865e96f2aaae62 ee4c566cf9a93f14d82f913c2dc6dd0c) 3ef43a0b657b3be2300d5770ece849e0 fac2f116257151a9d068a22e544a4917) 6343647c601c52e1368623803854d971 c0ed64b8031c3730f411d2eb4eea35b5) a49b44d1979771252814e80f8ec446f9 ed17cf2e0169df9d443503ef94b23b33) 67a5b30d522ed92f75a1f16eef299d1a 62527c363bdd9ec598bed41947b379fc) 34fb605428fcc7aa4d62f44404c11509 0f678c91647380debd923963594981b3)resnet50 resnet101 resnet152 resnet50v2 resnet101v2 resnet152v2 resnext50 resnext101c  d| vr| jdan ta| rtd| |dvs4tj j j|s td|dk(r|r| dk7r tdtj|dd tj|| }|tj| } n/tj|stj|| } n|} tjd k(rdnd} tjdd| }tjddd|d|}|s9tj!| dd|}tj#dd|}tjdd|}tj%ddd !|}||}|r9tj!| dd"|}tj#dd#|}|rOtj'd$|}tj(| |tj+| | d%&|}nA|d'k(rtj'd$|}n |d(k(rtj-d)|}|t/j0|}n| }t3j4|||}|dk(r`|t6vrX|r|d*z}t6|d+}n|d,z}t6|d}t9j:|t<|zd-|.}|j?||S||j?||S)/aInstantiates the ResNet, ResNetV2, and ResNeXt architecture. Args: stack_fn: a function that returns output tensor for the stacked residual blocks. preact: whether to use pre-activation or not (True for ResNetV2, False for ResNet and ResNeXt). use_bias: whether to use biases for convolutional layers or not (True for ResNet and ResNetV2, False for ResNeXt). model_name: string, model name. include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. input_tensor: optional TF-Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(224, 224, 3)` (with `channels_last` data format) or `(3, 224, 224)` (with `channels_first` data format). It should have exactly 3 inputs channels. pooling: optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. classifier_activation: A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. **kwargs: For backwards compatibility only. Returns: A `keras.Model` instance. layerszUnknown argument(s): >NimagenetzThe `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded.r$zWIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000 ) default_sizemin_size data_formatrequire_flattenweights)shape)tensorr- channels_last)r0r0r2 conv1_padpaddingname@ conv1_convstridesuse_biasr6>conv1_bnaxisepsilonr6relu conv1_relur6r1r1rG pool1_pad pool1_poolr<r6post_bn post_reluavg_pool predictions) activationr6avgmaxmax_poolz&_weights_tf_dim_ordering_tf_kernels.h5rz,_weights_tf_dim_ordering_tf_kernels_notop.h5models) cache_subdir file_hash) popr#r ValueErrortfiogfileexistsrobtain_input_shaperimage_data_formatInputis_keras_tensor ZeroPadding2DConv2DBatchNormalization Activation MaxPooling2DGlobalAveragePooling2Dvalidate_activationDenseGlobalMaxPooling2Drget_source_inputsrModelWEIGHTS_HASHESrget_fileBASE_WEIGHTS_PATH load_weights)stack_fnpreactr= model_name include_topr, input_tensor input_shapepoolingclassesclassifier_activationkwargs img_inputbn_axisxinputsmodel file_namerU weights_paths ^/var/www/html/dev/engine/venv/lib/python3.12/site-packages/tf_keras/src/applications/resnet.pyResNetrLsu|6H%#% 09:: ) )RUU[[-?-?-H <  *D 1  !33--/# KLL{L3 &&|4 L LI$I,,./AaqG%5KH A  b!Q MaPA   % %( &     f<  8 ;%5KHKAAq|qAA  ))z):1=A..|< NN61: 6E :J.$@ "%MMI&z215IKK 'z215I!**   )!  <( L   7# LcRtjdk(rdnd}|rEtjd|zd||dz|}tj |d|dz |}n|}tj|d||d z|}tj |d|d z |}tj d |d z|}tj||d|dz|}tj |d|dz |}tj d |dz|}tjd|zd|dz|}tj |d|dz |}tj |dz||g}tj d |dz|}|S)aA residual block. Args: x: input tensor. filters: integer, filters of the bottleneck layer. kernel_size: default 3, kernel size of the bottleneck layer. stride: default 1, stride of the first layer. conv_shortcut: default True, use convolution shortcut if True, otherwise identity shortcut. name: string, block label. Returns: Output tensor for the residual block. r/r0r1_0_convrJr>_0_bnr@_1_conv_1_bnrC_1_relurESAME_2_convr4_2_bn_2_relu_3_conv_3_bn_add_out)rr]r#rarbrcAdd)r{filters kernel_sizestride conv_shortcutr6rzshortcuts rblock1rs,,./AaqG== KF 1A!  ,,(-  gq&ti7G HKA!! hTG^ "  A &ti'78;A f4)3C   A !! hTG^ "  A &ti'78;A a'k14)+;  _preact_bnr@rC _preact_relurErrrJ)r<Frr;rrrF_2_padr4rrrrr) rr]r#rbrcrardr`r) r{rrrrr6rzrprs rblock2r=s,,./AaqG  & & hTL-@' F  vD>,A  B6 JF== KF 1A!  :@!F  6  21 5   AD94D    A !! hTG^ "  A &ti'78;A%5D8OLQOA  I      A !! hTG^ "  A &ti'78;A a'k14)+;  1gD8Oc!f$< =>q''x#f+0MNA Hrc  tjdk(rdnd}|rItjd|z|zd|d|dz|}tj |d|d z |}n|}tj|dd|d z |}tj |d|d z |}tj d|dz|}||z tj d|dz|}tj|| d|dz|}tj|dd} tj|tj| | fg}tj fd|dz|}tj|tj| |fg}tj |d|dz |}tj d|dz|}tjd|z|zdd|dz |}tj |d|dz |}tj|dz||g}tj d|dz|}|S) aA residual block. Args: x: input tensor. filters: integer, filters of the bottleneck layer. kernel_size: default 3, kernel size of the bottleneck layer. stride: default 1, stride of the first layer. groups: default 32, group size for grouped convolution. conv_shortcut: default True, use convolution shortcut if True, otherwise identity shortcut. name: string, block label. Returns: Output tensor for the residual block. r/r0r1r7Frr;r>rr@r)r=r6rrCrrErFrr4r)r<depth_multiplierr=r6Nc@tfdtDS)Nc3BK|]}dddddddd|fywN).0rr{s r z+block3....s 91a1aA &9s)sumr)r{cs`rzblock3..s#9a99r _2_reducerrrrrr) rr]r#rarbrcr`DepthwiseConv2Dr-reshape concatenateLambdar) r{rrrgroupsrr6rzrx_shapers @rblock3rs0,,./AaqG== 6\W $  ! !  ,,(-  gq5ti7G HKA!! hTG^ "  A &ti'78;A6A%5D8OLQOA I      AmmAs#G7..&!Q/HIJA 9 K    A 7..'/DEFA!! hTG^ "  A &ti'78;A v !e$:J   A !! hTG^ "  A  v &!}5A&tf}5a8A Hrc t|||||dz}td|dzD]!}t|||d|dzt|z}#|S)aA set of stacked residual blocks. Args: x: input tensor. filters: integer, filters of the bottleneck layer in a block. blocks: integer, blocks in the stacked blocks. stride1: default 2, stride of the first layer in the first block. groups: default 32, group size for grouped convolution. name: string, stack label. Returns: Output tensor for the stacked blocks. r)rrr6r9r1Fr)rrr6)rrr)r{rrrrr6rs rstack3rsb q''&ti?OPA 1fqj !   3q6)    Hrz$keras.applications.resnet50.ResNet50z"keras.applications.resnet.ResNet50zkeras.applications.ResNet50c 2d}t|ddd||||||f i|S)z'Instantiates the ResNet50 architecture.c|t|dddd}t|ddd }t|d d d }t|d dd S)Nr7r0r1conv2rr6rconv3rEconv4conv5rr{s rrozResNet50..stack_fnsE 1b!QW 5 1c17 + 1c17 +aag..rFTrrrrr,rsrtrurvrxros rResNet50rs> /       rz#keras.applications.resnet.ResNet101zkeras.applications.ResNet101c 2d}t|ddd||||||f i|S)z(Instantiates the ResNet101 architecture.c|t|dddd}t|ddd }t|d d d }t|d dd S)Nr7r0r1rrrrrrErrrrrrs rrozResNet101..stack_fn&E 1b!QW 5 1c17 + 1c2G ,aag..rFTrrrs r ResNet101r>/       rz#keras.applications.resnet.ResNet152zkeras.applications.ResNet152c 2d}t|ddd||||||f i|S)z(Instantiates the ResNet152 architecture.c|t|dddd}t|ddd }t|d d d }t|d dd S)Nr7r0r1rrrrrEr$rrrrrs rrozResNet152..stack_fnIrrFTrrrs r ResNet152r;rrz,keras.applications.resnet50.preprocess_inputz*keras.applications.resnet.preprocess_inputc2tj||dS)Ncaffe)r*mode)rpreprocess_input)r{r*s rrr^s  * * { rz.keras.applications.resnet50.decode_predictionsz,keras.applications.resnet.decode_predictionsc0tj||S)N)top)rdecode_predictions)predsrs rrrhs  , ,U <rs9 "! 4(2)*:K;!F    #fR0 f *6 r .  L ^ 4*(!       @)+I    @)+I    @20  42= =*>>EE 55  3 3F ,>>FF:x)X--34 9i//#56 9i//#56r