&Vf>.ddlZddlZddlZddlmZddlmZddlmZddlm Z ddl m Z da dZ dZd Zd Zd Zd Zd ZdZe dddZeedee_e dddZdZdZ ddZdZdZdS)N) activations)backend)ops) keras_export) file_utilszUhttps://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.jsona: Preprocesses a tensor or Numpy array encoding a batch of images. Usage example with `applications.MobileNet`: ```python i = keras.layers.Input([None, None, 3], dtype="uint8") x = ops.cast(i, "float32") x = keras.applications.mobilenet.preprocess_input(x) core = keras.applications.MobileNet() x = core(x) model = keras.Model(inputs=[i], outputs=[x]) result = model(image) ``` Args: x: A floating point `numpy.array` or a backend-native tensor, 3D or 4D with 3 color channels, with values in the range [0, 255]. The preprocessed data are written over the input data if the data types are compatible. To avoid this behaviour, `numpy.copy(x)` can be used. data_format: Optional data format of the image tensor/array. None, means the global setting `keras.backend.image_data_format()` is used (unless you changed it, it uses "channels_last").{mode} Defaults to `None`. Returns: Preprocessed array with type `float32`. {ret} Raises: {error} a mode: One of "caffe", "tf" or "torch". - caffe: will convert the images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. - tf: will scale pixels between -1 and 1, sample-wise. - torch: will scale pixels between 0 and 1 and then will normalize each channel with respect to the ImageNet dataset. Defaults to `"caffe"`. zE ValueError: In case of unknown `mode` or `data_format` argument.z; ValueError: In case of unknown `data_format` argument.zH The inputs pixel values are scaled between -1 and 1, sample-wise.z The input pixels values are scaled between 0 and 1 and each channel is normalized with respect to the ImageNet dataset.z The images are converted from RGB to BGR, then each color channel is zero-centered with respect to the ImageNet dataset, without scaling.z2keras.applications.imagenet_utils.preprocess_inputcaffec|dvrtd||tj}n|dvrtd|t|tjrt |||St|||S)z@Preprocesses a tensor or Numpy array encoding a batch of images.>tfrtorchzDExpected mode to be one of `caffe`, `tf` or `torch`. Received: mode=N> channels_lastchannels_firstz]Expected data_format to be one of `channels_first` or `channels_last`. Received: data_format=) data_formatmode) ValueErrorrimage_data_format isinstancenpndarray_preprocess_numpy_input_preprocess_tensor_input)xrrs b/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/applications/imagenet_utils.pypreprocess_inputrVs +++ %" % %   /11 ? ? ? D6A D D   !RZ  O&qkMMMM'{NNNN)rreterrorz4keras.applications.imagenet_utils.decode_predictionsc>t|jdks|jddkrtd|jtXt jdt dd }t|5}tj |adddn #1swxYwYg}tj |}|D]c | dddd }fd |D}| d d ||d|S)aDecodes the prediction of an ImageNet model. Args: preds: NumPy array encoding a batch of predictions. top: Integer, how many top-guesses to return. Defaults to `5`. Returns: A list of lists of top class prediction tuples `(class_name, class_description, score)`. One list of tuples per sample in batch input. Raises: ValueError: In case of invalid shape of the `pred` array (must be 2D). iz{`decode_predictions` expects a batch of predictions (i.e. a 2D array of shape (samples, 1000)). Received array with shape: Nzimagenet_class_index.jsonmodels c2c37ea517e94d9795004a39431a14cb) cache_subdir file_hashcrg|]3}ttt||fz4S)tuple CLASS_INDEXstr).0ipreds r z&decode_predictions..s7OOOa% CFF+,,Qz9OOOrc|dS)Nr r()rs rz$decode_predictions..s !A$rT)keyreverse)lenshaperr*rget_fileCLASS_INDEX_PATHopenjsonloadrconvert_to_numpyargsortsortappend)predstopfpathfresults top_indicesresultr.s @rdecode_predictionsrFts|& 5;1 A$ 6 6 8+0+ 8 8   # ' !8     %[[ 'A)A,,K ' ' ' ' ' ' ' ' ' ' ' ' ' ' 'G   ' 'EllnncTUU+DDbD1 OOOO;OOO  555v Ns5BBBct|jjtjs(|t jd}|dkr |dz}|dz}|S|dkr|dz}gd}gd }nP|d kr7t|j d kr|d d d df}n|d d d d d df}n |dd d d f}gd}d }|d krt|j d kr|dd d d d fxx|dzcc<|dd d d d fxx|dzcc<|dd d d d fxx|dzcc<|W|dd d d d fxx|dzcc<|dd d d d fxx|dzcc<|dd d d d fxx|dzcc<nI|d d dd d d d fxx|dzcc<|d d dd d d d fxx|dzcc<|d d dd d d d fxx|dzcc<|`|d d dd d d d fxx|dzcc<|d d dd d d d fxx|dzcc<|d d dd d d d fxx|dzcc<n|dxx|dzcc<|dxx|dzcc<|dxx|dzcc<|B|dxx|dzcc<|dxx|dzcc<|dxx|dzcc<|S)aPreprocesses a NumPy array encoding a batch of images. Args: x: Input array, 3D or 4D. data_format: Data format of the image array. mode: One of "caffe", "tf" or "torch". - caffe: will convert the images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. - tf: will scale pixels between -1 and 1, sample-wise. - torch: will scale pixels between 0 and 1 and then will normalize each channel with respect to the ImageNet dataset. Returns: Preprocessed Numpy array. F)copyr _@?r o@g ףp= ?gv/?gCl?gZd;O?gy&1?g?r Nr&.gjtY@g`"1]@gQ^@rr!r ).r).r!).r ) issubclassdtypetyperfloatingastyperfloatxr4r5)rrrmeanstds rrrs( aglBK 0 03 HHW^%%EH 2 2 t|| U  S  U $$$### * * *17||q  dddCiLaaa2slO#ttt) A)))&&& qw<<1   aAAAgJJJ$q' !JJJ aAAAgJJJ$q' !JJJ aAAAgJJJ$q' !JJJ!QQQ' c!f$ !QQQ' c!f$ !QQQ' c!f$ aaaAAAqqqjMMMT!W $MMM aaaAAAqqqjMMMT!W $MMM aaaAAAqqqjMMMT!W $MMM!!!Q111* Q' !!!Q111* Q' !!!Q111* Q'  & T!W  & T!W  & T!W ? fIIIQ III fIIIQ III fIIIQ III Hrctj}|dkr dzdzS|dkrdzgd}gd}n|dkr^tjd kr#tjfd d Dd nEtjfdd Dd n"tjfdd Dd gd}d}tjt j| j}|dkrtj|dd|dz zz}ntj|d|dz zdz}|z |Mtjt j|j}|dkrtj|d}|zS)aPreprocesses a tensor encoding a batch of images. Args: x: Input tensor, 3D or 4D. data_format: Data format of the image tensor. mode: One of "caffe", "tf" or "torch". - caffe: will convert the images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. - tf: will scale pixels between -1 and 1, sample-wise. - torch: will scale pixels between 0 and 1 and then will normalize each channel with respect to the ImageNet dataset. Returns: Preprocessed tensor. r rIrJr rKrLrMr rNc$g|] }|df S.r(r,r-rs rr/z,_preprocess_tensor_input.. s!<<.s+===aqAqqqz===rr!c$g|] }d|f SrZr(r[s rr/z,_preprocess_tensor_input..s!8881S!V9888rr&rON)rQ)r!rN)r!r )rN)r&r!r!) r4r5rstackconvert_to_tensorrarrayrQreshape)rrrndimrVrW mean_tensor std_tensors` rrrs( qw<r!rNz]This model usually expects 1 or 3 input channels. However, it was passed an input_shape with z input channels.r ) stacklevelr&NzXWhen setting `include_top=True` and loading `imagenet` weights, `input_shape` should be z. Received: input_shape=z0`input_shape` must be a tuple of three integers.z6The input must have 3 channels; Received `input_shape=`zInput size must be at least rz; Received: input_shape=)rNNN)NNrNz[If `include_top` is True, you should specify a static `input_shape`. Received: input_shape=)r4warningswarnr) input_shape default_sizemin_sizerrequire_flattenweightscorrect_channel_axis default_shapes robtain_input_shaperu%s8*[1A1AQ1F1F * * *(+K(8(8A(=(=111 /0>> ='N=== !  )^\<HMM2f,, >'O>>> !  *<RIMM * * * l;MM)<;M*  "m++ ;/<;;.9;; .. * * *&{##q(($Jq>Q&&7j+@+@$7(3777  N.;q>H3L3L!!n0[^h5N5N$5x55$55'255 &{##q(($Jr?a''Gz,A,A$7(3777  N.;q>H3L3L!!n0[^h5N5N$5#55&.55'255  .'KK...- -  ;  7)477  rcltjdkrdnd}|j||dz}t|tr||f}|dd}nd|ddzz d|ddzz f}|ddz|ddzf}|d|dz |df|d|dz |dffS)zReturns a tuple for zero-padding for 2D convolution with downsampling. Args: inputs: Input tensor. kernel_size: An integer or tuple/list of 2 integers. Returns: A tuple. r r r!rN)r!r!)rrr5rint)inputs kernel_sizeimg_dim input_sizeadjustcorrects r correct_padr~s,..2BBBaaGg156J+s##1"K0 !}jma''Z]Q->)>?1~"KNa$78G fQi , fQi , rc|dStj|}|tjdtjdhvrtd|dS)a@validates that the classifer_activation is compatible with the weights. Args: classifier_activation: str or callable activation function weights: The pretrained weights to load. Raises: ValueError: if an activation other than `None` or `softmax` are used with pretrained weights. NsoftmaxzOnly `None` and `softmax` activations are allowed for the `classifier_activation` argument when using pretrained weights, with `include_top=True`; Received: classifier_activation=)rgetr)classifier_activationrrs rvalidate_activationrs}'O,ABB ""% =&; = =   r)Nr)rr^)r9rlnumpyr keras.srcrrrkeras.src.api_exportrkeras.src.utilsrr*r7PREPROCESS_INPUT_DOCPREPROCESS_INPUT_MODE_DOC"PREPROCESS_INPUT_DEFAULT_ERROR_DOCPREPROCESS_INPUT_ERROR_DOCPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_RET_DOC_TORCHPREPROCESS_INPUT_RET_DOC_CAFFErformat__doc__rFrrrur~rr(rrrs !!!!!!------&&&&&& % !F &H">K":"N BCCOOODCO,066 " ,7DEE)))FE)XF F F R8 8 8 B  uuuup2     r