sgb\BdZddlmZmZmZddlZddlmZddl m Z m Z m Z ddl mZmZmZmZmZddlmZdd lmZmZmZmZd d lmZej8eZd Zd Z gdZ!d Z"dZ#GddejHjJZ&GddejHjJZ'GddejHjJZ(GddejHjJZ)GddejHjJZ*GddejHjJZ+GddejHjJZ,GddeZ-d Z.d!Z/eGd"d#ejHjJZ0ed$e.Gd%d&e-Z1ed'e.Gd(d)e-eZ2y)*zTensorFlow ResNet model.)OptionalTupleUnionN)ACT2FN) TFBaseModelOutputWithNoAttention*TFBaseModelOutputWithPoolingAndNoAttention&TFImageClassifierOutputWithNoAttention)TFPreTrainedModelTFSequenceClassificationLosskeraskeras_serializable unpack_inputs) shape_list)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardlogging) ResNetConfigrzmicrosoft/resnet-50)rirz tiger catc eZdZ ddedededededdf fd Zd ejdejfd Zdd ejd e dejfd Z dd Z xZ S)TFResNetConvLayer in_channels out_channels kernel_sizestride activationreturnNc Tt|d i||dz|_tjj |||ddd|_tjjddd |_| t|ntjjd |_ ||_ ||_ y) NvalidF convolution)rstridespaddinguse_biasnameh㈵>? normalizationepsilonmomentumr'linear)super__init__ pad_valuer layersConv2DconvBatchNormalizationr*r Activationrrr)selfrrrrrkwargs __class__s `/var/www/html/venv/lib/python3.12/site-packages/transformers/models/resnet/modeling_tf_resnet.pyr1zTFResNetConvLayer.__init__6s "6"$)LL'' k67]biv(  #\\< -PS -  -dryy *r<rwc eZdZdZ ddedededededdf fd Zdd ejd e dejfd Z dd Z xZ S)TFResNetBottleNeckLayera% A classic ResNet's bottleneck layer composed by three `3x3` convolutions. 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Parameters: config ([`ResNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. a> Args: pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ConvNextImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. 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