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Conv2d

class torch.nn.Conv2d(in_channels: int, out_channels: int, kernel_size: Union[T, Tuple[T, T]], stride: Union[T, Tuple[T, T]] = 1, padding: Union[T, Tuple[T, T]] = 0, dilation: Union[T, Tuple[T, T]] = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros')[source]

Applies a 2D convolution over an input signal composed of several input planes.

In the simplest case, the output value of the layer with input size (N,Cin,H,W)(N, C_{\text{in}}, H, W) and output (N,Cout,Hout,Wout)(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}}) can be precisely described as:

out(Ni,Coutj)=bias(Coutj)+k=0Cin1weight(Coutj,k)input(Ni,k)\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) + \sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)

where \star is the valid 2D cross-correlation operator, NN is a batch size, CC denotes a number of channels, HH is a height of input planes in pixels, and WW is width in pixels.

This module supports TensorFloat32.

  • stride controls the stride for the cross-correlation, a single number or a tuple.

  • padding controls the amount of implicit padding on both sides for padding number of points for each dimension.

  • dilation controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this link has a nice visualization of what dilation does.

  • groups controls the connections between inputs and outputs. in_channels and out_channels must both be divisible by groups. For example,

    • At groups=1, all inputs are convolved to all outputs.

    • At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels and producing half the output channels, and both subsequently concatenated.

    • At groups= in_channels, each input channel is convolved with its own set of filters (of size out_channelsin_channels\frac{\text{out\_channels}}{\text{in\_channels}} ).

The parameters kernel_size, stride, padding, dilation can either be:

  • a single int – in which case the same value is used for the height and width dimension

  • a tuple of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension

Note

When groups == in_channels and out_channels == K * in_channels, where K is a positive integer, this operation is also known as a “depthwise convolution”.

In other words, for an input of size (N,Cin,Lin)(N, C_{in}, L_{in}) , a depthwise convolution with a depthwise multiplier K can be performed with the arguments (Cin=Cin,Cout=Cin×K,...,groups=Cin)(C_\text{in}=C_\text{in}, C_\text{out}=C_\text{in} \times \text{K}, ..., \text{groups}=C_\text{in}) .

Note

In some circumstances when given tensors on a CUDA device and using CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting torch.backends.cudnn.deterministic = True. See Reproducibility for more information.

Parameters
  • in_channels (int) – Number of channels in the input image

  • out_channels (int) – Number of channels produced by the convolution

  • kernel_size (int or tuple) – Size of the convolving kernel

  • stride (int or tuple, optional) – Stride of the convolution. Default: 1

  • padding (int or tuple, optional) – Zero-padding added to both sides of the input. Default: 0

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

  • dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1

  • groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1

  • bias (bool, optional) – If True, adds a learnable bias to the output. Default: True

Shape:
  • Input: (N,Cin,Hin,Win)(N, C_{in}, H_{in}, W_{in})

  • Output: (N,Cout,Hout,Wout)(N, C_{out}, H_{out}, W_{out}) where

    Hout=Hin+2×padding[0]dilation[0]×(kernel_size[0]1)1stride[0]+1H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] \times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
    Wout=Win+2×padding[1]dilation[1]×(kernel_size[1]1)1stride[1]+1W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[1] - \text{dilation}[1] \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor
Variables
  • ~Conv2d.weight (Tensor) – the learnable weights of the module of shape (out_channels,in_channelsgroups,(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}}, kernel_size[0],kernel_size[1])\text{kernel\_size[0]}, \text{kernel\_size[1]}) . The values of these weights are sampled from U(k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k}) where k=groupsCini=01kernel_size[i]k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}

  • ~Conv2d.bias (Tensor) – the learnable bias of the module of shape (out_channels). If bias is True, then the values of these weights are sampled from U(k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k}) where k=groupsCini=01kernel_size[i]k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}

Examples

>>> # With square kernels and equal stride
>>> m = nn.Conv2d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
>>> # non-square kernels and unequal stride and with padding and dilation
>>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
>>> input = torch.randn(20, 16, 50, 100)
>>> output = m(input)

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