o i eW @sdZddlmmZddlmZddlmZddl m Z ddl m Z ddl mZddl mZdd lmZd Zd d d ddddddZda        dFddZdGddZdHd d!ZdId#d$ZdHd%d&Z   '  dJd(d)ZdKd*d+Zed,d-d.      dLd/d0Zed1d2      dLd3d4Zed5d6      dLd7d8Zed9d:dMd;d<Zed=d>dNd@dAZ ej!j"dBej#ej$dCe_ej je _dDZ%e&edEeje%e&edEeje%e&edEeje%dS)OzResNet models for 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 resnext101resnetTimagenetsoftmaxc Ksd| vr | danta| rtd| |dvs%tjj|s%td|dkr3|r3| dkr3tdtj |dd t ||d }|d urKtj |d } nt |sXtj ||d } n|} t dkrbdnd} tjddd| }tjddd|dd|}|stj| ddd|}tjddd|}tjdd d|}tjddd!d"|}||}|rtj| dd#d|}tjdd$d|}|rtjd%d|}t| |tj| | d&d'|}n|d(krtjd%d|}n |d)krtjd*d|}|d urt|}n| }tj|||d}|dkr6|tvr6|r|d+}t|d,}n |d-}t|d}tj|t|d.|d/}|||S|d ur@|||S)0aInstantiates 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 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): >Nr"zThe `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded.r"r#zWIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000 ) default_sizemin_size data_formatrequire_flattenweightsN)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_pathrL/var/www/myenv/lib/python3.10/site-packages/keras/src/applications/resnet.pyResNetLs>         rr0r1cCsJtdkrdnd}|r(tjd|d||dd|}tj|d|dd |}n|}tj|d||d d|}tj|d|d d |}tjd |d d|}tj||d|dd|}tj|d|dd |}tjd |dd|}tjd|d|dd|}tj|d|dd |}tj|dd||g}tjd |dd|}|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_shortcutr6rzshortcutrrrblock1sP     rr9cCsHt||||dd}td|dD]}t||d|dt|d}q|S)adA 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. name: string, stack label. Returns: Output tensor for the stacked blocks. _block1rr6r9r1F_blockrr6)rrangestrr{rblocksstride1r6irrrstack1(s  rFc Csftdkrdnd}tj|d|dd|}tjd|dd |}|r3tjd |d||d d |}n|dkr@tjd|d |n|}tj|ddd|dd|}tj|d|dd|}tjd|dd |}tjd|dd|}tj|||d|dd|}tj|d|dd|}tjd|dd |}tjd |d|dd |}tj|dd ||g}|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 False, use convolution shortcut if True, otherwise identity shortcut. name: string, block label. Returns: Output tensor for the residual block. r/r0r1r> _preact_bnr@rC _preact_relurErrrJ)r<Frr;rrrF_2_padr4rrrrr) rr]r%rbrcrardr`r) r{rrrrr6rzrprrrrblock2=sZ   rcCs^t||d|dd}td|D]}t|||dt|d}qt||||dt|d}|S)apA 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. name: string, stack label. Returns: Output tensor for the stacked blocks. Trrr9rrEr)rrrrrrrstack2vs  rr'c stdkrdnd}|r+tjd||d|d|dd|}tj|d|d d |}n|}tj|dd|d d |}tj|d|d d |}tjd|dd|}||tjd|dd|}tj||d|dd|}t|dd} t |t | |fg}tj fdd|dd|}t |t | |fg}tj|d|dd |}tjd|dd|}tjd||dd|dd |}tj|d|dd |}tj |dd||g}tjd|d d|}|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=r6NcstfddtDS)Nc3s2|]}dddddddd|fVqdSNr).0rr{rr s0z+block3....)sumrrcrrszblock3.. _2_reducerrrrrr) rr]r%rarbrcr`DepthwiseConv2Dr-reshape concatenateLambdar) r{rrrgroupsrr6rzrx_shaperrrblock3s~      rc CsLt|||||dd}td|dD]}t|||d|dt|d}q|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{rrrrr6rrrrstack3srz$keras.applications.resnet50.ResNet50z"keras.applications.resnet.ResNet50zkeras.applications.ResNet50c K*dd}t|ddd||||||f i|S)z'Instantiates the ResNet50 architecture.cSBt|ddddd}t|dddd }t|d d d d }t|d ddd S)Nr7r0r1conv2rr6rconv3rEconv4conv5rrrrrrozResNet50..stack_fnFTrrrrr,rsrtrurvrxrorrrResNet50s rz#keras.applications.resnet.ResNet101zkeras.applications.ResNet101c Kr)z(Instantiates the ResNet101 architecture.cSr)Nr7r0r1rrrrrrErrrrrrrrrro&rzResNet101..stack_fnFTrrrrrr ResNet101 rz#keras.applications.resnet.ResNet152zkeras.applications.ResNet152c Kr)z(Instantiates the ResNet152 architecture.cSr)Nr7r0r1rrrrrEr$rrrrrrrrroIrzResNet152..stack_fnFTrrrrrr ResNet152;rrz,keras.applications.resnet50.preprocess_inputz*keras.applications.resnet.preprocess_inputcCstj||ddS)Ncaffe)r*mode)rpreprocess_input)r{r*rrrr^srz.keras.applications.resnet50.decode_predictionsz,keras.applications.resnet.decode_predictionscCstj||dS)N)top)rdecode_predictions)predsrrrrrhsr)rreterrora9 Reference: - [Deep Residual Learning for Image Recognition]( https://arxiv.org/abs/1512.03385) (CVPR 2015) For image classification use cases, see [this page for detailed examples]( https://keras.io/api/applications/#usage-examples-for-image-classification-models). For transfer learning use cases, make sure to read the [guide to transfer learning & fine-tuning]( https://keras.io/guides/transfer_learning/). Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call `tf.keras.applications.resnet.preprocess_input` on your inputs before passing them to the model. `resnet.preprocess_input` will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. Args: 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 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, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value. 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 block. - `avg` means that global average pooling will be applied to the output of the last convolutional block, 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"`. Returns: A Keras model instance. __doc__)r!Tr"NNNr#r$)r0r1TN)r9N)r0r1FN)r0r1r'TN)r9r'N)Tr"NNNr#r)r)'rtensorflow.compat.v2compatv2rX keras.srcrkeras.src.applicationsrkeras.src.enginerkeras.src.layersrkeras.src.utilsrr tensorflow.python.util.tf_exportrrmrkr%rrrrrrrrrrrrPREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_CAFFEPREPROCESS_INPUT_ERROR_DOCDOCsetattrrrrrs       #  * 3  9 O         <