ёi&SSKJr SSKJr SSKJr SSKJr \(a SSKJ r SSK J r /r "S S \5r "S S \5r"S S\5rg)) annotations) TYPE_CHECKING) functional)Layer)Tensor) DataLayout2DcZ^\rSrSrSrSSU4SjjjrS SjrS SjrSrU=r $) PixelShuffleaX Rearranges elements in a tensor of shape :math:`[N, C, H, W]` to a tensor of shape :math:`[N, C/upscale_factor^2, H*upscale_factor, W*upscale_factor]`, or from shape :math:`[N, H, W, C]` to :math:`[N, H*upscale_factor, W*upscale_factor, C/upscale_factor^2]`. This is useful for implementing efficient sub-pixel convolution with a stride of 1/upscale_factor. Please refer to the paper: `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network `_ . by Shi et. al (2016) for more details. Parameters: upscale_factor(int): factor to increase spatial resolution. data_format (str, optional): The data format of the input and output data. An optional string from: `'NCHW'``, ``'NHWC'``. When it is ``'NCHW'``, the data is stored in the order of: [batch_size, input_channels, input_height, input_width]. Default: ``'NCHW'``. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - x: 4-D tensor with shape of :math:`(N, C, H, W)` or :math:`(N, H, W, C)`. - out: 4-D tensor with shape of :math:`(N, C/upscale_factor^2, H*upscale_factor, W*upscale_factor)` or :math:`(N, H*upscale_factor, W*upscale_factor, C/upscale_factor^2)`. Examples: .. code-block:: pycon >>> import paddle >>> import paddle.nn as nn >>> x = paddle.randn(shape=[2, 9, 4, 4]) >>> pixel_shuffle = nn.PixelShuffle(3) >>> out = pixel_shuffle(x) >>> print(out.shape) paddle.Size([2, 1, 12, 12]) c>[TU]5 [U[5(d [ S5eUS;a[ SU35eXlX lX0lg)Nzupscale factor must be int typeNCHWNHWC@Data format should be 'NCHW' or 'NHWC'.But receive data format: ) super__init__ isinstanceint TypeError ValueError_upscale_factor _data_format_name)selfupscale_factor data_formatname __class__s V/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/nn/layer/vision.pyrPixelShuffle.__init__Bsa .#..=> > . .,,7=:  .' cn[R"XRURUR5$N)r pixel_shufflerrrrxs r!forwardPixelShuffle.forwardWs,'' ##T%6%6   r#cSUR3nURS:waUSUR3- nURbUSUR3- nU$)Nzupscale_factor=r, data_format=, name=)rrrrmain_strs r! extra_reprPixelShuffle.extra_repr\s_$T%9%9$:;    & .):):(;< `_ . by Shi et. al (2016) for more details. Parameters: downscale_factor (int): Factor to decrease spatial resolution. data_format (str, optional): The data format of the input and output data. An optional string of ``'NCHW'`` or ``'NHWC'``. When it is ``'NCHW'``, the data is stored in the order of [batch_size, input_channels, input_height, input_width]. Default: ``'NCHW'``. name (str|None, optional): Name for the operation (optional, default is None). Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Shape: - **x**: 4-D tensor with shape of :math:`[N, C, H, W]` or :math:`[N, C, H, W]`. - **out**: 4-D tensor with shape of :math:`[N, r^2C, H/r, W/r]` or :math:`[N, H/r, W/r, r^2C]`, where :math:`r` is :attr:`downscale_factor`. Examples: .. code-block:: pycon >>> import paddle >>> import paddle.nn as nn >>> x = paddle.randn([2, 1, 12, 12]) >>> pixel_unshuffle = nn.PixelUnshuffle(3) >>> out = pixel_unshuffle(x) >>> print(out.shape) paddle.Size([2, 9, 4, 4]) c>[TU]5 [U[5(d [ S5eUS::a [ S5eUS;a[ SU35eXlX lX0lg)Nz!Downscale factor must be int typerz!Downscale factor must be positiverr) rrrrrr_downscale_factorrr)rdownscale_factorrrr s r!rPixelUnshuffle.__init__sv *C00?@ @ q @A A . .,,7=:  "2' r#cn[R"XRURUR5$r%)rpixel_unshufflerFrrr's r!r)PixelUnshuffle.forwards,)) %%t'8'8$**  r#cSUR3nURS:waUSUR3- nURbUSUR3- nU$)Nzdownscale_factor=rr,r-)rFrrr.s r!r0PixelUnshuffle.extra_reprs_&t'='=&>?    & .):):(;< `_ . by Zhang et. al (2017) for more details. Parameters: groups (int): Number of groups to divide channels in. data_format (str, optional): The data format of the input and output data. An optional string of NCHW or NHWC. The default is NCHW. When it is NCHW, the data is stored in the order of [batch_size, input_channels, input_height, input_width]. name (str|None, optional): Name for the operation (optional, default is None). Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Shape: - **x**: 4-D tensor with shape of [N, C, H, W] or [N, H, W, C]. - **out**: 4-D tensor with shape and dtype same as x. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> x = paddle.arange(0, 0.6, 0.1, 'float32') >>> x = paddle.reshape(x, [1, 6, 1, 1]) >>> print(x) Tensor(shape=[1, 6, 1, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[[[0. ]], [[0.10000000]], [[0.20000000]], [[0.30000001]], [[0.40000001]], [[0.50000000]]]]) >>> channel_shuffle = nn.ChannelShuffle(3) >>> y = channel_shuffle(x) >>> print(y) Tensor(shape=[1, 6, 1, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[[[0. ]], [[0.20000000]], [[0.40000001]], [[0.10000000]], [[0.30000001]], [[0.50000000]]]]) c>[TU]5 [U[5(d [ S5eUS::a [ S5eUS;a[ SU35eXlX lX0lg)Nzgroups must be int typerzgroups must be positiverr) rrrrrr_groupsrr)rgroupsrrr s r!rChannelShuffle.__init__ss &#&&56 6 Q;67 7 . .,,7=:   ' r#cn[R"XRURUR5$r%)rchannel_shufflerRrrr's r!r)ChannelShuffle.forwards*)) ||T..   r#cSUR3nURS:waUSUR3- nURbUSUR3- nU$)Nzgroups=rr,r-)rRrrr.s r!r0ChannelShuffle.extra_reprs\T\\N+    & .):):(;< rbsM# + E5EPDUDNQUQr#