o 2hAL @sddlmZddlmZddlmZddlmZddlmZddl m Z ddl m Z dZ d d d d d ddddZ         dAddZ dBddZdCdd Z dDd"d#ZdCd$d%Zegd&        'dEd(d)Zed*d+g        ,dFd-d.Zed/d0g        1dGd2d3Zed4d5gdHd6d7Zed8d9gdId;d<Zejjd=ejejd>e_ejje_d?Zeed@ejeeed@ejeeed@ejedS)J)backend)layers) keras_export)imagenet_utils) Functional)operation_utils) file_utilszDhttps://storage.googleapis.com/tensorflow/keras-applications/resnet/) 2cb95161c43110f7111970584f804107 4d473c1dd8becc155b73f8504c6f6626) f1aeb4b969a6efcfb50fad2f0c20cfc5 88cf7a10940856eca736dc7b7e228a21) 100835be76be38e30d865e96f2aaae62 ee4c566cf9a93f14d82f913c2dc6dd0c) 3ef43a0b657b3be2300d5770ece849e0 fac2f116257151a9d068a22e544a4917) 6343647c601c52e1368623803854d971 c0ed64b8031c3730f411d2eb4eea35b5) a49b44d1979771252814e80f8ec446f9 ed17cf2e0169df9d443503ef94b23b33) 67a5b30d522ed92f75a1f16eef299d1a 62527c363bdd9ec598bed41947b379fc) 34fb605428fcc7aa4d62f44404c11509 0f678c91647380debd923963594981b3)resnet50 resnet101 resnet152 resnet50v2 resnet101v2 resnet152v2 resnext50 resnext101TimagenetNsoftmaxresnetc Cs`|dvst|std||dkr!|r!|dkr!td|tj|ddt||d}|d ur9tj|d } nt |sFtj||d } n|} td krQd } nd} tj ddd| }tj ddd|dd|}|s}tj | ddd|}tj ddd|}tj ddd|}tjd ddd |}||}|rtj | dd!d|}tj dd"d|}|rtjd#d|}t| |tj|| d$d%|}n|d&krtjd#d|}n |d'krtjd(d|}|d urt|}n| }t||| d}|dkr$| tvr$|r| d)}t| d*}n | d+}t| d}tj|t|d,|d-}|||S|d ur.|||S).aR Instantiates 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. name: Name of the model. 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"`. name: The name of the model (string). Returns: A Model instance. >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. Received: weights=r!r"zcIf using `weights='imagenet'` with `include_top=True`, `classes` should be 1000. Received classes= ) default_sizemin_size data_formatrequire_flattenweightsN)shape)tensorr, channels_last)r/r/r1 conv1_padpaddingname@ conv1_convstridesuse_biasr5>conv1_bnaxisepsilonr5relu conv1_relur5r0r0rF pool1_pad pool1_poolr;r5post_bn post_reluavg_pool predictions) activationr5avgmaxmax_poolz&_weights_tf_dim_ordering_tf_kernels.h5rz,_weights_tf_dim_ordering_tf_kernels_notop.h5models) cache_subdir file_hash)rexists ValueErrorrobtain_input_shaperimage_data_formatrInputis_keras_tensor ZeroPadding2DConv2DBatchNormalization Activation MaxPooling2DGlobalAveragePooling2Dvalidate_activationDenseGlobalMaxPooling2Drget_source_inputsrWEIGHTS_HASHESget_fileBASE_WEIGHTS_PATH load_weights)stack_fnpreactr< include_topr+ input_tensor input_shapepoolingclassesclassifier_activationr5 weights_name img_inputbn_axisxinputsmodel file_namerT weights_pathryX/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/applications/resnet.pyResNet0s>         r{r/r0cCsLtdkr d}nd}|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 for ResNet*_v1. Args: x: Input tensor. filters: No of filters in the bottleneck layer. kernel_size: Kernel size of the bottleneck layer. Defaults to `3`. stride: Stride of the first layer. Defaults to `1`. conv_shortcut: Use convolution shortcut if `True`, otherwise use identity shortcut. Defaults to `True` name(optional): Name of the block Returns: Output tensor for the residual block. r.r/r0_0_convrIr=_0_bnr?_1_conv_1_bnrB_1_relurDSAME_2_convr3_2_bn_2_relu_3_conv_3_bn_add_out)rrXrr\r]r^Add)rtfilters kernel_sizestride conv_shortcutr5rsshortcutryryrzresidual_block_v1sT      rr8cCsHt||||dd}td|dD]}t||d|dt|d}q|S)pA set of stacked residual blocks. Args: x: Input tensor. filters: Number of filters in the bottleneck layer in a block. blocks: Number of blocks in the stacked blocks. stride1: Stride of the first layer in the first block. Defaults to `2`. name: Stack label. Returns: Output tensor for the stacked blocks. _block1rr5r8r0F_blockrr5)rrangestrrtrblocksstride1r5iryryrzstack_residual_blocks_v1s rFc Cshtdkr d}nd}tj|d|dd|}tjd|dd |}|r4tjd |d||d d |}n|dkrAtjd|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 for ResNet*_v2. Args: x: Input tensor. filters: No of filters in the bottleneck layer. kernel_size: Kernel size of the bottleneck layer. Defaults to `3`. stride: Stride of the first layer. Defaults to `1`. conv_shortcut: Use convolution shortcut if `True`, otherwise use identity shortcut. Defaults to `True` name(optional): Name of the block Returns: Output tensor for the residual block. r.r/r0r= _preact_bnr?rB _preact_relurDr|r}rI)r;Frr:rrrE_2_padr3rrrrr) rrXrr]r^r\r_r[r) rtrrrrr5rsrjrryryrzresidual_block_v2*s^    rcCs^t||d|dd}td|D]}t|||dt|d}qt||||dt|d}|S)rTrrr8rrDr)rrrrryryrzstack_residual_blocks_v2isr)z$keras.applications.resnet50.ResNet50z"keras.applications.resnet.ResNet50zkeras.applications.ResNet50rc Cs(dd}t|ddd||||||||d S)z'Instantiates the ResNet50 architecture.cSBt|ddddd}t|dddd }t|d d d d }t|d ddd S)Nr6r/r0conv2rr5r|conv3rDconv4conv5rrtryryrzrizResNet50..stack_fnFTr) rjr<rqr5rkr+rlrmrnrorpr{ rkr+rlrmrnrorpr5riryryrzResNet50srz#keras.applications.resnet.ResNet101zkeras.applications.ResNet101rc C(dd}t|dd|d|||||||d S)z(Instantiates the ResNet101 architecture.cSr)Nr6r/r0rrrr|rrDrrrrrrryryrzrirzResNet101..stack_fnFTr rjr<r5rqrkr+rlrmrnrorprrryryrz ResNet101rz#keras.applications.resnet.ResNet152zkeras.applications.ResNet152rc Cr)z(Instantiates the ResNet152 architecture.cSr)Nr6r/r0rrrrrDr$rrrrrryryrzrirzResNet152..stack_fnFTrrrrryryrz ResNet152rrz,keras.applications.resnet50.preprocess_inputz*keras.applications.resnet.preprocess_inputcCstj||ddS)Ncaffe)r)mode)rpreprocess_input)rtr)ryryrzrsrz.keras.applications.resnet50.decode_predictionsz,keras.applications.resnet.decode_predictionscCstj||dS)N)top)rdecode_predictions)predsrryryrzrsr)rreterroraj 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 `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. Defaults to `1000`. 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"`. name: The name of the model (string). Returns: A Model instance. __doc__) Tr!NNNr"r#r$N)r/r0TN)r8N)r/r0FN)Tr!NNNr"r#r)Tr!NNNr"r#r)Tr!NNNr"r#r)N)r) keras.srcrrkeras.src.api_exportrkeras.src.applicationsrkeras.src.modelsr keras.src.opsrkeras.src.utilsrrgrer{rrrrrrrrrPREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_CAFFEPREPROCESS_INPUT_ERROR_DOCrDOCsetattrryryryrzs      ( - 9 ? " " "   7