&VfhddlmZddlmZddlmZedGddeZdS))backend) keras_export) TFDataLayerzkeras.layers.ResizingcFeZdZdZ d fd ZdZd Zfd ZxZS) Resizinga A preprocessing layer which resizes images. This layer resizes an image input to a target height and width. The input should be a 4D (batched) or 3D (unbatched) tensor in `"channels_last"` format. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`). Input shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format, or `(..., channels, height, width)`, in `"channels_first"` format. Output shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., target_height, target_width, channels)`, or `(..., channels, target_height, target_width)`, in `"channels_first"` format. **Note:** This layer is safe to use inside a `tf.data` pipeline (independently of which backend you're using). Args: height: Integer, the height of the output shape. width: Integer, the width of the output shape. interpolation: String, the interpolation method. Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"lanczos3"`, `"lanczos5"`. Defaults to `"bilinear"`. crop_to_aspect_ratio: If `True`, resize the images without aspect ratio distortion. When the original aspect ratio differs from the target aspect ratio, the output image will be cropped so as to return the largest possible window in the image (of size `(height, width)`) that matches the target aspect ratio. By default (`crop_to_aspect_ratio=False`), aspect ratio may not be preserved. pad_to_aspect_ratio: If `True`, pad the images without aspect ratio distortion. When the original aspect ratio differs from the target aspect ratio, the output image will be evenly padded on the short side. fill_mode: When using `pad_to_aspect_ratio=True`, padded areas are filled according to the given mode. Only `"constant"` is supported at this time (fill with constant value, equal to `fill_value`). fill_value: Float. Padding value to use when `pad_to_aspect_ratio=True`. data_format: string, either `"channels_last"` or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch, height, width, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be `"channels_last"`. **kwargs: Base layer keyword arguments, such as `name` and `dtype`. bilinearFconstantNc  tjdi| ||_||_||_t j||_||_||_ ||_ ||_ dS)N) super__init__heightwidth interpolationrstandardize_data_format data_formatcrop_to_aspect_ratiopad_to_aspect_ratio fill_mode fill_value) selfrrrrrrrrkwargs __class__s d/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/layers/preprocessing/resizing.pyrzResizing.__init__>sm ""6"""  *":;GG$8!#6 "$c |j|jf}|jj|||j|j|j|j|j |j S)N)sizerrrrrr) rrrimageresizerrrrrr)rinputsrs rcallz Resizing.callTsZ TZ(|!(( ,(!%!: $ 8n)   rc6t|}t|dkr5|jdkr|j|d<|j|d<nI|j|d<|j|d<n4|jdkr|j|d<|j|d<n|j|d<|j|d<t |S)N channels_lastr)listlenrrrtuple)r input_shapes rcompute_output_shapezResizing.compute_output_shapeas;'' {  q ?22!% A!% A!% A!% A?22!% A!% A!% A!% A[!!!rc t}|j|j|j|j|j|j|j|j d}i||S)N)rrrrrrrr) r get_configrrrrrrrr)r base_configconfigrs rr/zResizing.get_configssbgg((** kZ!/$($=#'#;/+   )+(((r)rFFr r N) __name__ __module__ __qualname____doc__rr"r-r/ __classcell__)rs@rrrs44t!"!%%%%%%,    """$ ) ) ) ) ) ) ) ) )rrN) keras.srcrkeras.src.api_exportr,keras.src.layers.preprocessing.tf_data_layerrrr rrr:s------DDDDDD%&&x)x)x)x)x){x)x)'&x)x)x)r