Function conv2d

python/paddle/nn/functional/conv.py:716–954

r""" The convolution2D layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input and Output are in NCHW or NHWC format, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of t

(
    x: Tensor,
    weight: Tensor,
    bias: Tensor | None = None,
    stride: Size2 = 1,
    padding: _PaddingSizeMode | Size2 | Size4 | Sequence[Size2] = 0,
    dilation: Size2 = 1,
    groups: int = 1,
    data_format: DataLayout2D = "NCHW",
    name: str | None = None,
)

@param_one_alias(["x", "input"])
def conv2d(
    x: Tensor,
    weight: Tensor,
    bias: Tensor | None = None,
    stride: Size2 = 1,
    padding: _PaddingSizeMode | Size2 | Size4 | Sequence[Size2] = 0,
    dilation: Size2 = 1,
    groups: int = 1,
    data_format: DataLayout2D = "NCHW",
    name: str | None = None,
) -> Tensor:
    r"""

    The convolution2D layer calculates the output based on the input, filter
    and strides, paddings, dilations, groups parameters. Input and
    Output are in NCHW or NHWC format, where N is batch size, C is the number of
    channels, H is the height of the feature, and W is the width of the feature.
    Filter is in MCHW format, where M is the number of output image channels,
    C is the number of input image channels, H is the height of the filter,
    and W is the width of the filter. If the groups is greater than 1,
    C will equal the number of input image channels divided by the groups.
    Please refer to UFLDL's `convolution
    <http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_
    for more details.
    If bias attribution and activation type are provided, bias is added to the
    output of the convolution, and the corresponding activation function is
    applied to the final result.

    For each input :math:`X`, the equation is:

    ..  math::

        Out = \sigma (W \ast X + b)

    Where:

    * :math:`X`: Input value, a tensor with NCHW or NHWC format.
    * :math:`W`: Filter value, a tensor with MCHW format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`

        - Output:

          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`

        Where

        ..  math::