# coding=utf-8 import math import torch from torch.nn.parameter import Parameter from .. import functional as F from .. import init from .module import Module from .utils import _single, _pair, _triple from ..._jit_internal import weak_module, weak_script_method, List @weak_module class _ConvNd(Module): __constants__ = ['stride', 'padding', 'dilation', 'groups', 'bias', 'padding_mode'] def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode): super(_ConvNd, self).__init__() if in_channels % groups != 0: raise ValueError('in_channels must be divisible by groups') if out_channels % groups != 0: raise ValueError('out_channels must be divisible by groups') self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.dilation = dilation self.transposed = transposed self.output_padding = output_padding self.groups = groups self.padding_mode = padding_mode if transposed: self.weight = Parameter(torch.Tensor( in_channels, out_channels // groups, *kernel_size)) else: self.weight = Parameter(torch.Tensor( out_channels, in_channels // groups, *kernel_size)) if bias: self.bias = Parameter(torch.Tensor(out_channels)) else: self.register_parameter('bias', None) self.reset_parameters() def reset_parameters(self): init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if self.bias is not None: fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) init.uniform_(self.bias, -bound, bound) def extra_repr(self): s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}' ', stride={stride}') if self.padding != (0,) * len(self.padding): s += ', padding={padding}' if self.dilation != (1,) * len(self.dilation): s += ', dilation={dilation}' if self.output_padding != (0,) * len(self.output_padding): s += ', output_padding={output_padding}' if self.groups != 1: s += ', groups={groups}' if self.bias is None: s += ', bias=False' return s.format(**self.__dict__) @weak_module class Conv1d(_ConvNd): r"""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_{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, :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. * :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. .. note:: 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. .. note:: 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}, L_{in}), a depthwise convolution with a depthwise multiplier K, can be constructed by arguments :math:(C_\text{in}=C_{in}, C_\text{out}=C_{in} \times K, ..., \text{groups}=C_{in}). .. include:: cudnn_deterministic.rst 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 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: :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 Attributes: weight (Tensor): the learnable weights of the module of shape :math:(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}}, \text{kernel\_size}). The values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \text{kernel\_size}} bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:bias is True, then the values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \text{kernel\_size}} Examples:: >>> m = nn.Conv1d(16, 33, 3, stride=2) >>> input = torch.randn(20, 16, 50) >>> output = m(input) .. _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, padding_mode='zeros'): kernel_size = _single(kernel_size) stride = _single(stride) padding = _single(padding) dilation = _single(dilation) super(Conv1d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _single(0), groups, bias, padding_mode) @weak_script_method def forward(self, input): if self.padding_mode == 'circular': expanded_padding = ((self.padding[0] + 1) // 2, self.padding[0] // 2) return F.conv1d(F.pad(input, expanded_padding, mode='circular'), self.weight, self.bias, self.stride, _single(0), self.dilation, self.groups) return F.conv1d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) @weak_module class Conv2d(_ConvNd): r"""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 .. note:: 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. .. note:: 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 :math:(in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in}). .. include:: cudnn_deterministic.rst 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 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: :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 Attributes: weight (Tensor): the learnable weights of the module of shape :math:(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}}, :math:\text{kernel\_size[0]}, \text{kernel\_size[1]}). The values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]} bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:bias is True, then the values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{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) .. _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, padding_mode='zeros'): kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) super(Conv2d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _pair(0), groups, bias, padding_mode) @weak_script_method def forward(self, input): if self.padding_mode == 'circular': expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2, (self.padding[0] + 1) // 2, self.padding[0] // 2) return F.conv2d(F.pad(input, expanded_padding, mode='circular'), self.weight, self.bias, self.stride, _pair(0), self.dilation, self.groups) return F.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) @weak_module class Conv3d(_ConvNd): r"""Applies a 3D 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_{in}, D, H, W) and output :math:(N, C_{out}, D_{out}, H_{out}, W_{out}) can be precisely described as: .. math:: out(N_i, C_{out_j}) = bias(C_{out_j}) + \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k) where :math:\star is the valid 3D cross-correlation_ operator * :attr:stride controls the stride for the cross-correlation. * :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 depth, height and width dimension - a tuple of three ints -- in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension .. note:: 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. .. note:: 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}, D_{in}, H_{in}, W_{in}), a depthwise convolution with a depthwise multiplier K, can be constructed by arguments :math:(in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in}). .. include:: cudnn_deterministic.rst 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 all three 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 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: :math:(N, C_{in}, D_{in}, H_{in}, W_{in}) - Output: :math:(N, C_{out}, D_{out}, H_{out}, W_{out}) where .. math:: D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] \times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor .. math:: H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1] \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor .. math:: W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2] \times (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 1\right\rfloor Attributes: weight (Tensor): the learnable weights of the module of shape :math:(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}}, :math:\text{kernel\_size[0]}, \text{kernel\_size[1]}, \text{kernel\_size[2]}). The values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]} bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:bias is True, then the values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]} Examples:: >>> # With square kernels and equal stride >>> m = nn.Conv3d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.Conv3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(4, 2, 0)) >>> input = torch.randn(20, 16, 10, 50, 100) >>> output = m(input) .. _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, padding_mode='zeros'): kernel_size = _triple(kernel_size) stride = _triple(stride) padding = _triple(padding) dilation = _triple(dilation) super(Conv3d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _triple(0), groups, bias, padding_mode) @weak_script_method def forward(self, input): if self.padding_mode == 'circular': expanded_padding = ((self.padding[2] + 1) // 2, self.padding[2] // 2, (self.padding[1] + 1) // 2, self.padding[1] // 2, (self.padding[0] + 1) // 2, self.padding[0] // 2) return F.conv3d(F.pad(input, expanded_padding, mode='circular'), self.weight, self.bias, self.stride, _triple(0), self.dilation, self.groups) return F.conv3d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) @weak_module class _ConvTransposeMixin(object): __constants__ = ['stride', 'padding', 'kernel_size', 'dim_size', 'output_padding', 'groups', 'dilation', 'transposed', 'bias', 'padding_mode'] @weak_script_method def forward(self, input, output_size=None): # type(Tensor, Optional[List[int]]) -> Tensor output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size) func = self._backend.ConvNd( self.stride, self.padding, self.dilation, self.transposed, output_padding, self.groups) if self.bias is None: return func(input, self.weight) else: return func(input, self.weight, self.bias) @weak_script_method def _output_padding(self, input, output_size, stride, padding, kernel_size): # type: (Tensor, Optional[List[int]], List[int], List[int], List[int]) -> List[int] if output_size is None: ret = _single(self.output_padding) # converting to list if was not already else: k = input.dim() - 2 if len(output_size) == k + 2: output_size = output_size[2:] if len(output_size) != k: raise ValueError( "output_size must have {} or {} elements (got {})" .format(k, k + 2, len(output_size))) min_sizes = torch.jit.annotate(List[int], []) max_sizes = torch.jit.annotate(List[int], []) for d in range(k): dim_size = ((input.size(d + 2) - 1) * stride[d] - 2 * padding[d] + kernel_size[d]) min_sizes.append(dim_size) max_sizes.append(min_sizes[d] + stride[d] - 1) for i in range(len(output_size)): size = output_size[i] min_size = min_sizes[i] max_size = max_sizes[i] if size < min_size or size > max_size: raise ValueError(( "requested an output size of {}, but valid sizes range " "from {} to {} (for an input of {})").format( output_size, min_sizes, max_sizes, input.size()[2:])) res = torch.jit.annotate(List[int], []) for d in range(k): res.append(output_size[d] - min_sizes[d]) ret = res return ret @weak_module class ConvTranspose1d(_ConvTransposeMixin, _ConvNd): r"""Applies a 1D transposed convolution operator over an input image composed of several input planes. This module can be seen as the gradient of Conv1d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation). * :attr:stride controls the stride for the cross-correlation. * :attr:padding controls the amount of implicit zero-paddings on both sides for dilation * (kernel_size - 1) - padding number of points. See note below for details. * :attr:output_padding controls the additional size added to one side of the output shape. See note below for details. * :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). .. note:: 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. .. note:: The :attr:padding argument effectively adds dilation * (kernel_size - 1) - padding amount of zero padding to both sizes of the input. This is set so that when a :class:~torch.nn.Conv1d and a :class:~torch.nn.ConvTranspose1d are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, when stride > 1, :class:~torch.nn.Conv1d maps multiple input shapes to the same output shape. :attr:output_padding is provided to resolve this ambiguity by effectively increasing the calculated output shape on one side. Note that :attr:output_padding is only used to find output shape, but does not actually add zero-padding to output. .. include:: cudnn_deterministic.rst 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): dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of the output shape. Default: 0 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 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 Shape: - Input: :math:(N, C_{in}, L_{in}) - Output: :math:(N, C_{out}, L_{out}) where .. math:: L_{out} = (L_{in} - 1) \times \text{stride} - 2 \times \text{padding} + \text{dilation} \times (\text{kernel\_size} - 1) + \text{output\_padding} + 1 Attributes: weight (Tensor): the learnable weights of the module of shape :math:(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}}, :math:\text{kernel\_size}). The values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \text{kernel\_size}} bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:bias is True, then the values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \text{kernel\_size}} """ def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros'): kernel_size = _single(kernel_size) stride = _single(stride) padding = _single(padding) dilation = _single(dilation) output_padding = _single(output_padding) super(ConvTranspose1d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias, padding_mode) @weak_script_method def forward(self, input, output_size=None): # type: (Tensor, Optional[List[int]]) -> Tensor if self.padding_mode != 'zeros': raise ValueError('Only zeros padding mode is supported for ConvTranspose1d') output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size) return F.conv_transpose1d( input, self.weight, self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation) @weak_module class ConvTranspose2d(_ConvTransposeMixin, _ConvNd): r"""Applies a 2D transposed convolution operator over an input image composed of several input planes. This module can be seen as the gradient of Conv2d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation). * :attr:stride controls the stride for the cross-correlation. * :attr:padding controls the amount of implicit zero-paddings on both sides for dilation * (kernel_size - 1) - padding number of points. See note below for details. * :attr:output_padding controls the additional size added to one side of the output shape. See note below for details. * :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:output_padding can either be: - a single int -- in which case the same value is used for the height and width dimensions - 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:: 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. .. note:: The :attr:padding argument effectively adds dilation * (kernel_size - 1) - padding amount of zero padding to both sizes of the input. This is set so that when a :class:~torch.nn.Conv2d and a :class:~torch.nn.ConvTranspose2d are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, when stride > 1, :class:~torch.nn.Conv2d maps multiple input shapes to the same output shape. :attr:output_padding is provided to resolve this ambiguity by effectively increasing the calculated output shape on one side. Note that :attr:output_padding is only used to find output shape, but does not actually add zero-padding to output. .. include:: cudnn_deterministic.rst 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): dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of each dimension in the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0 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 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 Shape: - Input: :math:(N, C_{in}, H_{in}, W_{in}) - Output: :math:(N, C_{out}, H_{out}, W_{out}) where .. math:: H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0] \times (\text{kernel\_size}[0] - 1) + \text{output\_padding}[0] + 1 .. math:: W_{out} = (W_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1] \times (\text{kernel\_size}[1] - 1) + \text{output\_padding}[1] + 1 Attributes: weight (Tensor): the learnable weights of the module of shape :math:(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}}, :math:\text{kernel\_size[0]}, \text{kernel\_size[1]}). The values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]} bias (Tensor): the learnable bias of the module of shape (out_channels) If :attr:bias is True, then the values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]} Examples:: >>> # With square kernels and equal stride >>> m = nn.ConvTranspose2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> input = torch.randn(20, 16, 50, 100) >>> output = m(input) >>> # exact output size can be also specified as an argument >>> input = torch.randn(1, 16, 12, 12) >>> downsample = nn.Conv2d(16, 16, 3, stride=2, padding=1) >>> upsample = nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1) >>> h = downsample(input) >>> h.size() torch.Size([1, 16, 6, 6]) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12, 12]) .. _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, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros'): kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) output_padding = _pair(output_padding) super(ConvTranspose2d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias, padding_mode) @weak_script_method def forward(self, input, output_size=None): # type: (Tensor, Optional[List[int]]) -> Tensor if self.padding_mode != 'zeros': raise ValueError('Only zeros padding mode is supported for ConvTranspose2d') output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size) return F.conv_transpose2d( input, self.weight, self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation) @weak_module class ConvTranspose3d(_ConvTransposeMixin, _ConvNd): r"""Applies a 3D transposed convolution operator over an input image composed of several input planes. The transposed convolution operator multiplies each input value element-wise by a learnable kernel, and sums over the outputs from all input feature planes. This module can be seen as the gradient of Conv3d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation). * :attr:stride controls the stride for the cross-correlation. * :attr:padding controls the amount of implicit zero-paddings on both sides for dilation * (kernel_size - 1) - padding number of points. See note below for details. * :attr:output_padding controls the additional size added to one side of the output shape. See note below for details. * :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:output_padding can either be: - a single int -- in which case the same value is used for the depth, height and width dimensions - a tuple of three ints -- in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension .. note:: 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. .. note:: The :attr:padding argument effectively adds dilation * (kernel_size - 1) - padding amount of zero padding to both sizes of the input. This is set so that when a :class:~torch.nn.Conv3d and a :class:~torch.nn.ConvTranspose3d are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, when stride > 1, :class:~torch.nn.Conv3d maps multiple input shapes to the same output shape. :attr:output_padding is provided to resolve this ambiguity by effectively increasing the calculated output shape on one side. Note that :attr:output_padding is only used to find output shape, but does not actually add zero-padding to output. .. include:: cudnn_deterministic.rst 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): dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of each dimension in the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0 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 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 Shape: - Input: :math:(N, C_{in}, D_{in}, H_{in}, W_{in}) - Output: :math:(N, C_{out}, D_{out}, H_{out}, W_{out}) where .. math:: D_{out} = (D_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0] \times (\text{kernel\_size}[0] - 1) + \text{output\_padding}[0] + 1 .. math:: H_{out} = (H_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1] \times (\text{kernel\_size}[1] - 1) + \text{output\_padding}[1] + 1 .. math:: W_{out} = (W_{in} - 1) \times \text{stride}[2] - 2 \times \text{padding}[2] + \text{dilation}[2] \times (\text{kernel\_size}[2] - 1) + \text{output\_padding}[2] + 1 Attributes: weight (Tensor): the learnable weights of the module of shape :math:(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}}, :math:\text{kernel\_size[0]}, \text{kernel\_size[1]}, \text{kernel\_size[2]}). The values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]} bias (Tensor): the learnable bias of the module of shape (out_channels) If :attr:bias is True, then the values of these weights are sampled from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where :math:k = \frac{1}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]} Examples:: >>> # With square kernels and equal stride >>> m = nn.ConvTranspose3d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.ConvTranspose3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(0, 4, 2)) >>> input = torch.randn(20, 16, 10, 50, 100) >>> output = m(input) .. _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, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros'): kernel_size = _triple(kernel_size) stride = _triple(stride) padding = _triple(padding) dilation = _triple(dilation) output_padding = _triple(output_padding) super(ConvTranspose3d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias, padding_mode) @weak_script_method def forward(self, input, output_size=None): # type: (Tensor, Optional[List[int]]) -> Tensor if self.padding_mode != 'zeros': raise ValueError('Only zeros padding mode is supported for ConvTranspose3d') output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size) return F.conv_transpose3d( input, self.weight, self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation) # TODO: Conv2dLocal # TODO: Conv2dMap # TODO: ConvTranspose2dMap