crypten/nn/module.py [2001:2095]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - r""" Module that performs 1D convolution. Applies a 1D convolution over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:`(N, C_{\text{in}}, L)` and output :math:`(N, C_{\text{out}}, L_{\text{out}})` can be precisely described as: .. math:: \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 :math:`\star` is the valid `cross-correlation`_ operator, :math:`N` is a batch size, :math:`C` denotes a number of channels, and :math:`L` is a length of signal sequence. * :attr:`stride` controls the stride for the cross-correlation, a single number or a one-element tuple. * :attr:`padding` controls the amount of implicit zero-paddings on both sides for :attr:`padding` number of points for each dimension. * :attr:`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 :attr:`dilation` does. * :attr:`groups` controls the connections between inputs and outputs. :attr:`in_channels` and :attr:`out_channels` must both be divisible by :attr:`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= :attr:`in_channels`, each input channel is convolved with its own set of filters, of size: :math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`. The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`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 Args: 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). Accepted values `zeros` and `circular` Default: `zeros` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` Shape: - Input: :math:`(N, C_{in}, L_{in})` - Output: :math:`(N, C_{out}, L_{out})` where .. math:: L_{out} = \left\lfloor\frac{L_{in} + 2 \times \text{padding} - \text{dilation} \times (\text{kernel\_size} - 1) - 1}{\text{stride}} + 1\right\rfloor .. _cross-correlation: https://en.wikipedia.org/wiki/Cross-correlation .. _link: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md """ # noqa: W605 def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, ): # check inputs: super().__init__() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - crypten/nn/module.py [2134:2233]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - r""" Module that performs 2D convolution. 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 :math:`(N, C_{\text{in}}, H, W)` and output :math:`(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})` can be precisely described as: .. math:: \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 :math:`\star` is the valid 2D `cross-correlation`_ operator, :math:`N` is a batch size, :math:`C` denotes a number of channels, :math:`H` is a height of input planes in pixels, and :math:`W` is width in pixels. * :attr:`stride` controls the stride for the cross-correlation, a single number or a tuple. * :attr:`padding` controls the amount of implicit zero-paddings on both sides for :attr:`padding` number of points for each dimension. * :attr:`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 :attr:`dilation` does. * :attr:`groups` controls the connections between inputs and outputs. :attr:`in_channels` and :attr:`out_channels` must both be divisible by :attr:`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= :attr:`in_channels`, each input channel is convolved with its own set of filters, of size: :math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`. The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`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 Args: 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). Accepted values `zeros` and `circular` Default: `zeros` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` Shape: - Input: :math:`(N, C_{in}, H_{in}, W_{in})` - Output: :math:`(N, C_{out}, H_{out}, W_{out})` where .. math:: H_{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 .. math:: W_{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 .. _cross-correlation: https://en.wikipedia.org/wiki/Cross-correlation .. _link: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md """ def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, ): # check inputs: super().__init__() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -