nn_conv2d | R Documentation |
Applies a 2D convolution over an input signal composed of several input planes.
nn_conv2d(
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
groups = 1,
bias = TRUE,
padding_mode = "zeros"
)
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 or string, optional): Zero-padding added to both sides of
the input. controls the amount of padding applied to the input. It
can be either a string |
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 |
padding_mode |
(string, optional): |
In the simplest case, the output value of the layer with input size
(N, C_{\mbox{in}}, H, W)
and output (N, C_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}})
can be precisely described as:
\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) +
\sum_{k = 0}^{C_{\mbox{in}} - 1} \mbox{weight}(C_{\mbox{out}_j}, k) \star \mbox{input}(N_i, k)
where \star
is the valid 2D cross-correlation operator,
N
is a batch size, C
denotes a number of channels,
H
is a height of input planes in pixels, and W
is
width in pixels.
stride
controls the stride for the cross-correlation, a single
number or a tuple.
padding
controls the amount of implicit zero-paddings 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:
\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor
.
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
Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid cross-correlation, and not a full cross-correlation. It is up to the user to add proper padding.
When groups == in_channels
and out_channels == K * in_channels
,
where K
is a positive integer, this operation is also termed in
literature as depthwise convolution.
In other words, for an input of size :math:(N, C_{in}, H_{in}, W_{in})
,
a depthwise convolution with a depthwise multiplier K
, can be constructed by arguments
(in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})
.
In some circumstances when using the CUDA backend with 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 backends_cudnn_deterministic = TRUE
.
Input: (N, C_{in}, H_{in}, W_{in})
Output: (N, C_{out}, H_{out}, W_{out})
where
H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
\times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1]
\times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
weight (Tensor): the learnable weights of the module of shape
(\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}
,
\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})
.
The values of these weights are sampled from
\mathcal{U}(-\sqrt{k}, \sqrt{k})
where
k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}
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 \mathcal{U}(-\sqrt{k}, \sqrt{k})
where
k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}
if (torch_is_installed()) {
# 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, c(3, 5), stride = c(2, 1), padding = c(4, 2))
# non-square kernels and unequal stride and with padding and dilation
m <- nn_conv2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2), dilation = c(3, 1))
input <- torch_randn(20, 16, 50, 100)
output <- m(input)
}
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