# Conv2D¶

class paddle.nn. Conv2D ( in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', weight_attr=None, bias_attr=None, data_format='NCHW' ) [source]

This interface is used to construct a callable object of the Conv2D class. For more details, refer to code examples. The convolution2D layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input and Output are in NCHW format, where N is batch size, C is the number of the feature map, H is the height of the feature map, and W is the width of the feature map. Filter’s shape is [MCHW] , where M is the number of output feature map, C is the number of input feature map, 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 feature map divided by the groups. Please refer to UFLDL’s convolution 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 $$X$$, the equation is:

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

Where:

• $$X$$: Input value, a Tensor with NCHW format.

• $$W$$: Filter value, a Tensor with shape [MCHW] .

• $$\\ast$$: Convolution operation.

• $$b$$: Bias value, a 1-D Tensor with shape [M].

• $$\\sigma$$: Activation function.

• $$Out$$: Output value, the shape of $$Out$$ and $$X$$ may be different.

Parameters
• in_channels (int) – The number of input channels in the input image.

• out_channels (int) – The number of output channels produced by the convolution.

• kernel_size (int|list|tuple, optional) – The size of the convolving kernel.

• stride (int|list|tuple, optional) – The stride size. If stride is a list/tuple, it must contain three integers, (stride_H, stride_W). Otherwise, the stride_H = stride_W = stride. The default value is 1.

• dilation (int|list|tuple, optional) – The dilation size. If dilation is a list/tuple, it must contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the dilation_D = dilation_H = dilation_W = dilation. The default value is 1.

• groups (int, optional) – The groups number of the Conv3D Layer. According to grouped convolution in Alex Krizhevsky’s Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels. The default value is 1.

• padding_mode (str, optional) – 'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'.

• weight_attr (ParamAttr, optional) – The parameter attribute for learnable parameters/weights of conv2d. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as param_attr. If it is set to None, the parameter is initialized with $$Normal(0.0, std)$$, and the $$std$$ is $$(\frac{2.0 }{filter\_elem\_num})^{0.5}$$. The default value is None.

• bias_attr (ParamAttr|bool, optional) – The parameter attribute for the bias of conv2d. If it is set to False, no bias will be added to the output units. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. The default value is None.

• data_format (str, optional) – Data format that specifies the layout of input. It can be “NCHW” or “NHWC”. Default: “NCHW”.

Attribute:

weight (Parameter): the learnable weights of filter of this layer.

bias (Parameter or None): the learnable bias of this layer.

Shape:

• x: $$(N, C_{in}, H_{in}, W_{in})$$

• weight: $$(C_{out}, C_{in}, K_{h}, K_{w})$$

• bias: $$(C_{out})$$

• output: $$(N, C_{out}, H_{out}, W_{out})$$

Where

\begin{align}\begin{aligned}H_{out}&= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (kernel\_size[0] - 1) + 1))}{strides[0]} + 1\\W_{out}&= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (kernel\_size[1] - 1) + 1))}{strides[1]} + 1\end{aligned}\end{align}

Examples

import paddle

x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.)

conv = nn.Conv2D(4, 6, (3, 3))
y_var = conv(x_var)
y_np = y_var.numpy()
print(y_np.shape)
# (2, 6, 6, 6)

forward ( x )

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments