torch.nn.functional Convolution functions conv1d  
torch.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) → Tensor  
Applies a 1D convolution over an input signal composed of several input planes. This operator supports TensorFloat32. See Conv1d for details and output shape.  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 
 
input – input tensor of shape (minibatch,in_channels,iW)(\text{minibatch} , \text{in\_channels} , iW) 
 
weight – filters of shape (out_channels,in_channelsgroups,kW)(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kW) 
 
bias – optional bias of shape (out_channels)(\text{out\_channels}) . Default: None
 
stride – the stride of the convolving kernel. Can be a single number or a one-element tuple (sW,). Default: 1 
padding – implicit paddings on both sides of the input. Can be a single number or a one-element tuple (padW,). Default: 0 
dilation – the spacing between kernel elements. Can be a single number or a one-element tuple (dW,). Default: 1 
groups – split input into groups, in_channels\text{in\_channels}  should be divisible by the number of groups. Default: 1    Examples: >>> filters = torch.randn(33, 16, 3)
>>> inputs = torch.randn(20, 16, 50)
>>> F.conv1d(inputs, filters)
 
 conv2d  
torch.nn.functional.conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) → Tensor  
Applies a 2D convolution over an input image composed of several input planes. This operator supports TensorFloat32. See Conv2d for details and output shape.  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 
 
input – input tensor of shape (minibatch,in_channels,iH,iW)(\text{minibatch} , \text{in\_channels} , iH , iW) 
 
weight – filters of shape (out_channels,in_channelsgroups,kH,kW)(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW) 
 
bias – optional bias tensor of shape (out_channels)(\text{out\_channels}) . Default: None
 
stride – the stride of the convolving kernel. Can be a single number or a tuple (sH, sW). Default: 1 
padding – implicit paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0 
dilation – the spacing between kernel elements. Can be a single number or a tuple (dH, dW). Default: 1 
groups – split input into groups, in_channels\text{in\_channels}  should be divisible by the number of groups. Default: 1    Examples: >>> # With square kernels and equal stride
>>> filters = torch.randn(8,4,3,3)
>>> inputs = torch.randn(1,4,5,5)
>>> F.conv2d(inputs, filters, padding=1)
 
 conv3d  
torch.nn.functional.conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) → Tensor  
Applies a 3D convolution over an input image composed of several input planes. This operator supports TensorFloat32. See Conv3d for details and output shape.  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 
 
input – input tensor of shape (minibatch,in_channels,iT,iH,iW)(\text{minibatch} , \text{in\_channels} , iT , iH , iW) 
 
weight – filters of shape (out_channels,in_channelsgroups,kT,kH,kW)(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kT , kH , kW) 
 
bias – optional bias tensor of shape (out_channels)(\text{out\_channels}) . Default: None 
stride – the stride of the convolving kernel. Can be a single number or a tuple (sT, sH, sW). Default: 1 
padding – implicit paddings on both sides of the input. Can be a single number or a tuple (padT, padH, padW). Default: 0 
dilation – the spacing between kernel elements. Can be a single number or a tuple (dT, dH, dW). Default: 1 
groups – split input into groups, in_channels\text{in\_channels}  should be divisible by the number of groups. Default: 1    Examples: >>> filters = torch.randn(33, 16, 3, 3, 3)
>>> inputs = torch.randn(20, 16, 50, 10, 20)
>>> F.conv3d(inputs, filters)
 
 conv_transpose1d  
torch.nn.functional.conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) → Tensor  
Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called “deconvolution”. This operator supports TensorFloat32. See ConvTranspose1d for details and output shape.  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 
 
input – input tensor of shape (minibatch,in_channels,iW)(\text{minibatch} , \text{in\_channels} , iW) 
 
weight – filters of shape (in_channels,out_channelsgroups,kW)(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kW) 
 
bias – optional bias of shape (out_channels)(\text{out\_channels}) . Default: None 
stride – the stride of the convolving kernel. Can be a single number or a tuple (sW,). Default: 1 
padding – dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple (padW,). Default: 0 
output_padding – additional size added to one side of each dimension in the output shape. Can be a single number or a tuple (out_padW). Default: 0 
groups – split input into groups, in_channels\text{in\_channels}  should be divisible by the number of groups. Default: 1 
dilation – the spacing between kernel elements. Can be a single number or a tuple (dW,). Default: 1    Examples: >>> inputs = torch.randn(20, 16, 50)
>>> weights = torch.randn(16, 33, 5)
>>> F.conv_transpose1d(inputs, weights)
 
 conv_transpose2d  
torch.nn.functional.conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) → Tensor  
Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”. This operator supports TensorFloat32. See ConvTranspose2d for details and output shape.  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 
 
input – input tensor of shape (minibatch,in_channels,iH,iW)(\text{minibatch} , \text{in\_channels} , iH , iW) 
 
weight – filters of shape (in_channels,out_channelsgroups,kH,kW)(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kH , kW) 
 
bias – optional bias of shape (out_channels)(\text{out\_channels}) . Default: None 
stride – the stride of the convolving kernel. Can be a single number or a tuple (sH, sW). Default: 1 
padding – dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple (padH, padW). Default: 0 
output_padding – additional size added to one side of each dimension in the output shape. Can be a single number or a tuple (out_padH, out_padW). Default: 0 
groups – split input into groups, in_channels\text{in\_channels}  should be divisible by the number of groups. Default: 1 
dilation – the spacing between kernel elements. Can be a single number or a tuple (dH, dW). Default: 1    Examples: >>> # With square kernels and equal stride
>>> inputs = torch.randn(1, 4, 5, 5)
>>> weights = torch.randn(4, 8, 3, 3)
>>> F.conv_transpose2d(inputs, weights, padding=1)
 
 conv_transpose3d  
torch.nn.functional.conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) → Tensor  
Applies a 3D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution” This operator supports TensorFloat32. See ConvTranspose3d for details and output shape.  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 
 
input – input tensor of shape (minibatch,in_channels,iT,iH,iW)(\text{minibatch} , \text{in\_channels} , iT , iH , iW) 
 
weight – filters of shape (in_channels,out_channelsgroups,kT,kH,kW)(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kT , kH , kW) 
 
bias – optional bias of shape (out_channels)(\text{out\_channels}) . Default: None 
stride – the stride of the convolving kernel. Can be a single number or a tuple (sT, sH, sW). Default: 1 
padding – dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple (padT, padH, padW). Default: 0 
output_padding – additional size added to one side of each dimension in the output shape. Can be a single number or a tuple (out_padT, out_padH, out_padW). Default: 0 
groups – split input into groups, in_channels\text{in\_channels}  should be divisible by the number of groups. Default: 1 
dilation – the spacing between kernel elements. Can be a single number or a tuple (dT, dH, dW). Default: 1    Examples: >>> inputs = torch.randn(20, 16, 50, 10, 20)
>>> weights = torch.randn(16, 33, 3, 3, 3)
>>> F.conv_transpose3d(inputs, weights)
 
 unfold  
torch.nn.functional.unfold(input, kernel_size, dilation=1, padding=0, stride=1) [source]
 
Extracts sliding local blocks from a batched input tensor.  Warning Currently, only 4-D input tensors (batched image-like tensors) are supported.   Warning More than one element of the unfolded tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensor, please clone it first.  See torch.nn.Unfold for details 
 fold  
torch.nn.functional.fold(input, output_size, kernel_size, dilation=1, padding=0, stride=1) [source]
 
Combines an array of sliding local blocks into a large containing tensor.  Warning Currently, only 3-D output tensors (unfolded batched image-like tensors) are supported.  See torch.nn.Fold for details 
 Pooling functions avg_pool1d  
torch.nn.functional.avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) → Tensor  
Applies a 1D average pooling over an input signal composed of several input planes. See AvgPool1d for details and output shape.  Parameters 
 
input – input tensor of shape (minibatch,in_channels,iW)(\text{minibatch} , \text{in\_channels} , iW) 
 
kernel_size – the size of the window. Can be a single number or a tuple (kW,)
 
stride – the stride of the window. Can be a single number or a tuple (sW,). Default: kernel_size
 
padding – implicit zero paddings on both sides of the input. Can be a single number or a tuple (padW,). Default: 0 
ceil_mode – when True, will use ceil instead of floor to compute the output shape. Default: False
 
count_include_pad – when True, will include the zero-padding in the averaging calculation. Default: True
    Examples: >>> # pool of square window of size=3, stride=2
>>> input = torch.tensor([[[1, 2, 3, 4, 5, 6, 7]]], dtype=torch.float32)
>>> F.avg_pool1d(input, kernel_size=3, stride=2)
tensor([[[ 2.,  4.,  6.]]])
 
 avg_pool2d  
torch.nn.functional.avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) → Tensor  
Applies 2D average-pooling operation in kH×kWkH \times kW  regions by step size sH×sWsH \times sW  steps. The number of output features is equal to the number of input planes. See AvgPool2d for details and output shape.  Parameters 
 
input – input tensor (minibatch,in_channels,iH,iW)(\text{minibatch} , \text{in\_channels} , iH , iW) 
 
kernel_size – size of the pooling region. Can be a single number or a tuple (kH, kW)
 
stride – stride of the pooling operation. Can be a single number or a tuple (sH, sW). Default: kernel_size
 
padding – implicit zero paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0 
ceil_mode – when True, will use ceil instead of floor in the formula to compute the output shape. Default: False
 
count_include_pad – when True, will include the zero-padding in the averaging calculation. Default: True
 
divisor_override – if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: None    
 avg_pool3d  
torch.nn.functional.avg_pool3d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) → Tensor  
Applies 3D average-pooling operation in kT×kH×kWkT \times kH \times kW  regions by step size sT×sH×sWsT \times sH \times sW  steps. The number of output features is equal to ⌊input planessT⌋\lfloor\frac{\text{input planes}}{sT}\rfloor . See AvgPool3d for details and output shape.  Parameters 
 
input – input tensor (minibatch,in_channels,iT×iH,iW)(\text{minibatch} , \text{in\_channels} , iT \times iH , iW) 
 
kernel_size – size of the pooling region. Can be a single number or a tuple (kT, kH, kW)
 
stride – stride of the pooling operation. Can be a single number or a tuple (sT, sH, sW). Default: kernel_size
 
padding – implicit zero paddings on both sides of the input. Can be a single number or a tuple (padT, padH, padW), Default: 0 
ceil_mode – when True, will use ceil instead of floor in the formula to compute the output shape 
count_include_pad – when True, will include the zero-padding in the averaging calculation 
divisor_override – if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: None    
 max_pool1d  
torch.nn.functional.max_pool1d(*args, **kwargs)  
Applies a 1D max pooling over an input signal composed of several input planes. See MaxPool1d for details. 
 max_pool2d  
torch.nn.functional.max_pool2d(*args, **kwargs)  
Applies a 2D max pooling over an input signal composed of several input planes. See MaxPool2d for details. 
 max_pool3d  
torch.nn.functional.max_pool3d(*args, **kwargs)  
Applies a 3D max pooling over an input signal composed of several input planes. See MaxPool3d for details. 
 max_unpool1d  
torch.nn.functional.max_unpool1d(input, indices, kernel_size, stride=None, padding=0, output_size=None) [source]
 
Computes a partial inverse of MaxPool1d. See MaxUnpool1d for details. 
 max_unpool2d  
torch.nn.functional.max_unpool2d(input, indices, kernel_size, stride=None, padding=0, output_size=None) [source]
 
Computes a partial inverse of MaxPool2d. See MaxUnpool2d for details. 
 max_unpool3d  
torch.nn.functional.max_unpool3d(input, indices, kernel_size, stride=None, padding=0, output_size=None) [source]
 
Computes a partial inverse of MaxPool3d. See MaxUnpool3d for details. 
 lp_pool1d  
torch.nn.functional.lp_pool1d(input, norm_type, kernel_size, stride=None, ceil_mode=False) [source]
 
Applies a 1D power-average pooling over an input signal composed of several input planes. If the sum of all inputs to the power of p is zero, the gradient is set to zero as well. See LPPool1d for details. 
 lp_pool2d  
torch.nn.functional.lp_pool2d(input, norm_type, kernel_size, stride=None, ceil_mode=False) [source]
 
Applies a 2D power-average pooling over an input signal composed of several input planes. If the sum of all inputs to the power of p is zero, the gradient is set to zero as well. See LPPool2d for details. 
 adaptive_max_pool1d  
torch.nn.functional.adaptive_max_pool1d(*args, **kwargs)  
Applies a 1D adaptive max pooling over an input signal composed of several input planes. See AdaptiveMaxPool1d for details and output shape.  Parameters 
 
output_size – the target output size (single integer) 
return_indices – whether to return pooling indices. Default: False
    
 adaptive_max_pool2d  
torch.nn.functional.adaptive_max_pool2d(*args, **kwargs)  
Applies a 2D adaptive max pooling over an input signal composed of several input planes. See AdaptiveMaxPool2d for details and output shape.  Parameters 
 
output_size – the target output size (single integer or double-integer tuple) 
return_indices – whether to return pooling indices. Default: False
    
 adaptive_max_pool3d  
torch.nn.functional.adaptive_max_pool3d(*args, **kwargs)  
Applies a 3D adaptive max pooling over an input signal composed of several input planes. See AdaptiveMaxPool3d for details and output shape.  Parameters 
 
output_size – the target output size (single integer or triple-integer tuple) 
return_indices – whether to return pooling indices. Default: False
    
 adaptive_avg_pool1d  
torch.nn.functional.adaptive_avg_pool1d(input, output_size) → Tensor  
Applies a 1D adaptive average pooling over an input signal composed of several input planes. See AdaptiveAvgPool1d for details and output shape.  Parameters 
output_size – the target output size (single integer)   
 adaptive_avg_pool2d  
torch.nn.functional.adaptive_avg_pool2d(input, output_size) [source]
 
Applies a 2D adaptive average pooling over an input signal composed of several input planes. See AdaptiveAvgPool2d for details and output shape.  Parameters 
output_size – the target output size (single integer or double-integer tuple)   
 adaptive_avg_pool3d  
torch.nn.functional.adaptive_avg_pool3d(input, output_size) [source]
 
Applies a 3D adaptive average pooling over an input signal composed of several input planes. See AdaptiveAvgPool3d for details and output shape.  Parameters 
output_size – the target output size (single integer or triple-integer tuple)   
 Non-linear activation functions threshold  
torch.nn.functional.threshold(input, threshold, value, inplace=False)  
Thresholds each element of the input Tensor. See Threshold for more details. 
  
torch.nn.functional.threshold_(input, threshold, value) → Tensor  
In-place version of threshold(). 
 relu  
torch.nn.functional.relu(input, inplace=False) → Tensor [source]
 
Applies the rectified linear unit function element-wise. See ReLU for more details. 
  
torch.nn.functional.relu_(input) → Tensor  
In-place version of relu(). 
 hardtanh  
torch.nn.functional.hardtanh(input, min_val=-1., max_val=1., inplace=False) → Tensor [source]
 
Applies the HardTanh function element-wise. See Hardtanh for more details. 
  
torch.nn.functional.hardtanh_(input, min_val=-1., max_val=1.) → Tensor  
In-place version of hardtanh(). 
 hardswish  
torch.nn.functional.hardswish(input, inplace=False) [source]
 
Applies the hardswish function, element-wise, as described in the paper: Searching for MobileNetV3.  Hardswish(x)={0if x≤−3,xif x≥+3,x⋅(x+3)/6otherwise\text{Hardswish}(x) = \begin{cases} 0 & \text{if~} x \le -3, \\ x & \text{if~} x \ge +3, \\ x \cdot (x + 3) /6 & \text{otherwise} \end{cases}  
See Hardswish for more details. 
 relu6  
torch.nn.functional.relu6(input, inplace=False) → Tensor [source]
 
Applies the element-wise function ReLU6(x)=min⁡(max⁡(0,x),6)\text{ReLU6}(x) = \min(\max(0,x), 6) . See ReLU6 for more details. 
 elu  
torch.nn.functional.elu(input, alpha=1.0, inplace=False) [source]
 
Applies element-wise, ELU(x)=max⁡(0,x)+min⁡(0,α∗(exp⁡(x)−1))\text{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1)) . See ELU for more details. 
  
torch.nn.functional.elu_(input, alpha=1.) → Tensor  
In-place version of elu(). 
 selu  
torch.nn.functional.selu(input, inplace=False) → Tensor [source]
 
Applies element-wise, SELU(x)=scale∗(max⁡(0,x)+min⁡(0,α∗(exp⁡(x)−1)))\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1))) , with α=1.6732632423543772848170429916717\alpha=1.6732632423543772848170429916717  and scale=1.0507009873554804934193349852946scale=1.0507009873554804934193349852946 . See SELU for more details. 
 celu  
torch.nn.functional.celu(input, alpha=1., inplace=False) → Tensor [source]
 
Applies element-wise, CELU(x)=max⁡(0,x)+min⁡(0,α∗(exp⁡(x/α)−1))\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1)) . See CELU for more details. 
 leaky_relu  
torch.nn.functional.leaky_relu(input, negative_slope=0.01, inplace=False) → Tensor [source]
 
Applies element-wise, LeakyReLU(x)=max⁡(0,x)+negative_slope∗min⁡(0,x)\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)  See LeakyReLU for more details. 
  
torch.nn.functional.leaky_relu_(input, negative_slope=0.01) → Tensor  
In-place version of leaky_relu(). 
 prelu  
torch.nn.functional.prelu(input, weight) → Tensor [source]
 
Applies element-wise the function PReLU(x)=max⁡(0,x)+weight∗min⁡(0,x)\text{PReLU}(x) = \max(0,x) + \text{weight} * \min(0,x)  where weight is a learnable parameter. See PReLU for more details. 
 rrelu  
torch.nn.functional.rrelu(input, lower=1./8, upper=1./3, training=False, inplace=False) → Tensor [source]
 
Randomized leaky ReLU. See RReLU for more details. 
  
torch.nn.functional.rrelu_(input, lower=1./8, upper=1./3, training=False) → Tensor  
In-place version of rrelu(). 
 glu  
torch.nn.functional.glu(input, dim=-1) → Tensor [source]
 
The gated linear unit. Computes:  GLU(a,b)=a⊗σ(b)\text{GLU}(a, b) = a \otimes \sigma(b)  
where input is split in half along dim to form a and b, σ\sigma  is the sigmoid function and ⊗\otimes  is the element-wise product between matrices. See Language Modeling with Gated Convolutional Networks.  Parameters 
 
input (Tensor) – input tensor 
dim (int) – dimension on which to split the input. Default: -1    
 gelu  
torch.nn.functional.gelu(input) → Tensor [source]
 
Applies element-wise the function GELU(x)=x∗Φ(x)\text{GELU}(x) = x * \Phi(x)  where Φ(x)\Phi(x)  is the Cumulative Distribution Function for Gaussian Distribution. See Gaussian Error Linear Units (GELUs). 
 logsigmoid  
torch.nn.functional.logsigmoid(input) → Tensor  
Applies element-wise LogSigmoid(xi)=log⁡(11+exp⁡(−xi))\text{LogSigmoid}(x_i) = \log \left(\frac{1}{1 + \exp(-x_i)}\right)  See LogSigmoid for more details. 
 hardshrink  
torch.nn.functional.hardshrink(input, lambd=0.5) → Tensor [source]
 
Applies the hard shrinkage function element-wise See Hardshrink for more details. 
 tanhshrink  
torch.nn.functional.tanhshrink(input) → Tensor [source]
 
Applies element-wise, Tanhshrink(x)=x−Tanh(x)\text{Tanhshrink}(x) = x - \text{Tanh}(x)  See Tanhshrink for more details. 
 softsign  
torch.nn.functional.softsign(input) → Tensor [source]
 
Applies element-wise, the function SoftSign(x)=x1+∣x∣\text{SoftSign}(x) = \frac{x}{1 + |x|}  See Softsign for more details. 
 softplus  
torch.nn.functional.softplus(input, beta=1, threshold=20) → Tensor  
Applies element-wise, the function Softplus(x)=1β∗log⁡(1+exp⁡(β∗x))\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) . For numerical stability the implementation reverts to the linear function when input×β>thresholdinput \times \beta > threshold . See Softplus for more details. 
 softmin  
torch.nn.functional.softmin(input, dim=None, _stacklevel=3, dtype=None) [source]
 
Applies a softmin function. Note that Softmin(x)=Softmax(−x)\text{Softmin}(x) = \text{Softmax}(-x) . See softmax definition for mathematical formula. See Softmin for more details.  Parameters 
 
input (Tensor) – input 
dim (int) – A dimension along which softmin will be computed (so every slice along dim will sum to 1). 
dtype (torch.dtype, optional) – the desired data type of returned tensor. If specified, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows. Default: None.    
 softmax  
torch.nn.functional.softmax(input, dim=None, _stacklevel=3, dtype=None) [source]
 
Applies a softmax function. Softmax is defined as: Softmax(xi)=exp⁡(xi)∑jexp⁡(xj)\text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}  It is applied to all slices along dim, and will re-scale them so that the elements lie in the range [0, 1] and sum to 1. See Softmax for more details.  Parameters 
 
input (Tensor) – input 
dim (int) – A dimension along which softmax will be computed. 
dtype (torch.dtype, optional) – the desired data type of returned tensor. If specified, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows. Default: None.     Note This function doesn’t work directly with NLLLoss, which expects the Log to be computed between the Softmax and itself. Use log_softmax instead (it’s faster and has better numerical properties).  
 softshrink  
torch.nn.functional.softshrink(input, lambd=0.5) → Tensor  
Applies the soft shrinkage function elementwise See Softshrink for more details. 
 gumbel_softmax  
torch.nn.functional.gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=-1) [source]
 
Samples from the Gumbel-Softmax distribution (Link 1 Link 2) and optionally discretizes.  Parameters 
 
logits – […, num_features] unnormalized log probabilities 
tau – non-negative scalar temperature 
hard – if True, the returned samples will be discretized as one-hot vectors, but will be differentiated as if it is the soft sample in autograd 
dim (int) – A dimension along which softmax will be computed. Default: -1.   Returns 
Sampled tensor of same shape as logits from the Gumbel-Softmax distribution. If hard=True, the returned samples will be one-hot, otherwise they will be probability distributions that sum to 1 across dim.    Note This function is here for legacy reasons, may be removed from nn.Functional in the future.   Note The main trick for hard is to do y_hard - y_soft.detach() + y_soft It achieves two things: - makes the output value exactly one-hot (since we add then subtract y_soft value) - makes the gradient equal to y_soft gradient (since we strip all other gradients)   Examples::

>>> logits = torch.randn(20, 32)
>>> # Sample soft categorical using reparametrization trick:
>>> F.gumbel_softmax(logits, tau=1, hard=False)
>>> # Sample hard categorical using "Straight-through" trick:
>>> F.gumbel_softmax(logits, tau=1, hard=True)
   
 log_softmax  
torch.nn.functional.log_softmax(input, dim=None, _stacklevel=3, dtype=None) [source]
 
Applies a softmax followed by a logarithm. While mathematically equivalent to log(softmax(x)), doing these two operations separately is slower, and numerically unstable. This function uses an alternative formulation to compute the output and gradient correctly. See LogSoftmax for more details.  Parameters 
 
input (Tensor) – input 
dim (int) – A dimension along which log_softmax will be computed. 
dtype (torch.dtype, optional) – the desired data type of returned tensor. If specified, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows. Default: None.    
 tanh  
torch.nn.functional.tanh(input) → Tensor [source]
 
Applies element-wise, Tanh(x)=tanh⁡(x)=exp⁡(x)−exp⁡(−x)exp⁡(x)+exp⁡(−x)\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}  See Tanh for more details. 
 sigmoid  
torch.nn.functional.sigmoid(input) → Tensor [source]
 
Applies the element-wise function Sigmoid(x)=11+exp⁡(−x)\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}  See Sigmoid for more details. 
 hardsigmoid  
torch.nn.functional.hardsigmoid(input) → Tensor [source]
 
Applies the element-wise function  Hardsigmoid(x)={0if x≤−3,1if x≥+3,x/6+1/2otherwise\text{Hardsigmoid}(x) = \begin{cases} 0 & \text{if~} x \le -3, \\ 1 & \text{if~} x \ge +3, \\ x / 6 + 1 / 2 & \text{otherwise} \end{cases}  
 Parameters 
inplace – If set to True, will do this operation in-place. Default: False   See Hardsigmoid for more details. 
 silu  
torch.nn.functional.silu(input, inplace=False) [source]
 
Applies the silu function, element-wise.  silu(x)=x∗σ(x),where σ(x) is the logistic sigmoid.\text{silu}(x) = x * \sigma(x), \text{where } \sigma(x) \text{ is the logistic sigmoid.}  
 Note See Gaussian Error Linear Units (GELUs) where the SiLU (Sigmoid Linear Unit) was originally coined, and see Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning and Swish: a Self-Gated Activation Function where the SiLU was experimented with later.  See SiLU for more details. 
 Normalization functions batch_norm  
torch.nn.functional.batch_norm(input, running_mean, running_var, weight=None, bias=None, training=False, momentum=0.1, eps=1e-05) [source]
 
Applies Batch Normalization for each channel across a batch of data. See BatchNorm1d, BatchNorm2d, BatchNorm3d for details. 
 instance_norm  
torch.nn.functional.instance_norm(input, running_mean=None, running_var=None, weight=None, bias=None, use_input_stats=True, momentum=0.1, eps=1e-05) [source]
 
Applies Instance Normalization for each channel in each data sample in a batch. See InstanceNorm1d, InstanceNorm2d, InstanceNorm3d for details. 
 layer_norm  
torch.nn.functional.layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-05) [source]
 
Applies Layer Normalization for last certain number of dimensions. See LayerNorm for details. 
 local_response_norm  
torch.nn.functional.local_response_norm(input, size, alpha=0.0001, beta=0.75, k=1.0) [source]
 
Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension. Applies normalization across channels. See LocalResponseNorm for details. 
 normalize  
torch.nn.functional.normalize(input, p=2, dim=1, eps=1e-12, out=None) [source]
 
Performs LpL_p  normalization of inputs over specified dimension. For a tensor input of sizes (n0,...,ndim,...,nk)(n_0, ..., n_{dim}, ..., n_k) , each ndimn_{dim}  -element vector vv  along dimension dim is transformed as  v=vmax⁡(∥v∥p,ϵ).v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)}.  
With the default arguments it uses the Euclidean norm over vectors along dimension 11  for normalization.  Parameters 
 
input – input tensor of any shape 
p (float) – the exponent value in the norm formulation. Default: 2 
dim (int) – the dimension to reduce. Default: 1 
eps (float) – small value to avoid division by zero. Default: 1e-12 
out (Tensor, optional) – the output tensor. If out is used, this operation won’t be differentiable.    
 Linear functions linear  
torch.nn.functional.linear(input, weight, bias=None) [source]
 
Applies a linear transformation to the incoming data: y=xAT+by = xA^T + b . This operator supports TensorFloat32. Shape:  Input: (N,∗,in_features)(N, *, in\_features)  N is the batch size, * means any number of additional dimensions Weight: (out_features,in_features)(out\_features, in\_features) 
 Bias: (out_features)(out\_features) 
 Output: (N,∗,out_features)(N, *, out\_features) 
  
 bilinear  
torch.nn.functional.bilinear(input1, input2, weight, bias=None) [source]
 
Applies a bilinear transformation to the incoming data: y=x1TAx2+by = x_1^T A x_2 + b  Shape:  input1: (N,∗,Hin1)(N, *, H_{in1})  where Hin1=in1_featuresH_{in1}=\text{in1\_features}  and ∗*  means any number of additional dimensions. All but the last dimension of the inputs should be the same. input2: (N,∗,Hin2)(N, *, H_{in2})  where Hin2=in2_featuresH_{in2}=\text{in2\_features} 
 weight: (out_features,in1_features,in2_features)(\text{out\_features}, \text{in1\_features}, \text{in2\_features}) 
 bias: (out_features)(\text{out\_features}) 
 output: (N,∗,Hout)(N, *, H_{out})  where Hout=out_featuresH_{out}=\text{out\_features}  and all but the last dimension are the same shape as the input.  
 Dropout functions dropout  
torch.nn.functional.dropout(input, p=0.5, training=True, inplace=False) [source]
 
During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution. See Dropout for details.  Parameters 
 
p – probability of an element to be zeroed. Default: 0.5 
training – apply dropout if is True. Default: True
 
inplace – If set to True, will do this operation in-place. Default: False
    
 alpha_dropout  
torch.nn.functional.alpha_dropout(input, p=0.5, training=False, inplace=False) [source]
 
Applies alpha dropout to the input. See AlphaDropout for details. 
 feature_alpha_dropout  
torch.nn.functional.feature_alpha_dropout(input, p=0.5, training=False, inplace=False) [source]
 
Randomly masks out entire channels (a channel is a feature map, e.g. the jj -th channel of the ii -th sample in the batch input is a tensor input[i,j]\text{input}[i, j] ) of the input tensor). Instead of setting activations to zero, as in regular Dropout, the activations are set to the negative saturation value of the SELU activation function. Each element will be masked independently on every forward call with probability p using samples from a Bernoulli distribution. The elements to be masked are randomized on every forward call, and scaled and shifted to maintain zero mean and unit variance. See FeatureAlphaDropout for details.  Parameters 
 
p – dropout probability of a channel to be zeroed. Default: 0.5 
training – apply dropout if is True. Default: True
 
inplace – If set to True, will do this operation in-place. Default: False
    
 dropout2d  
torch.nn.functional.dropout2d(input, p=0.5, training=True, inplace=False) [source]
 
Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor input[i,j]\text{input}[i, j] ) of the input tensor). Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution. See Dropout2d for details.  Parameters 
 
p – probability of a channel to be zeroed. Default: 0.5 
training – apply dropout if is True. Default: True
 
inplace – If set to True, will do this operation in-place. Default: False
    
 dropout3d  
torch.nn.functional.dropout3d(input, p=0.5, training=True, inplace=False) [source]
 
Randomly zero out entire channels (a channel is a 3D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 3D tensor input[i,j]\text{input}[i, j] ) of the input tensor). Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution. See Dropout3d for details.  Parameters 
 
p – probability of a channel to be zeroed. Default: 0.5 
training – apply dropout if is True. Default: True
 
inplace – If set to True, will do this operation in-place. Default: False
    
 Sparse functions embedding  
torch.nn.functional.embedding(input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False) [source]
 
A simple lookup table that looks up embeddings in a fixed dictionary and size. This module is often used to retrieve word embeddings using indices. The input to the module is a list of indices, and the embedding matrix, and the output is the corresponding word embeddings. See torch.nn.Embedding for more details.  Parameters 
 
input (LongTensor) – Tensor containing indices into the embedding matrix 
weight (Tensor) – The embedding matrix with number of rows equal to the maximum possible index + 1, and number of columns equal to the embedding size 
padding_idx (int, optional) – If given, pads the output with the embedding vector at padding_idx (initialized to zeros) whenever it encounters the index. 
max_norm (float, optional) – If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm. Note: this will modify weight in-place. 
norm_type (float, optional) – The p of the p-norm to compute for the max_norm option. Default 2. 
scale_grad_by_freq (boolean, optional) – If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default False. 
sparse (bool, optional) – If True, gradient w.r.t. weight will be a sparse tensor. See Notes under torch.nn.Embedding for more details regarding sparse gradients.     Shape:

 Input: LongTensor of arbitrary shape containing the indices to extract 
 
Weight: Embedding matrix of floating point type with shape (V, embedding_dim), 

where V = maximum index + 1 and embedding_dim = the embedding size    Output: (*, embedding_dim), where * is the input shape    Examples: >>> # a batch of 2 samples of 4 indices each
>>> input = torch.tensor([[1,2,4,5],[4,3,2,9]])
>>> # an embedding matrix containing 10 tensors of size 3
>>> embedding_matrix = torch.rand(10, 3)
>>> F.embedding(input, embedding_matrix)
tensor([[[ 0.8490,  0.9625,  0.6753],
         [ 0.9666,  0.7761,  0.6108],
         [ 0.6246,  0.9751,  0.3618],
         [ 0.4161,  0.2419,  0.7383]],

        [[ 0.6246,  0.9751,  0.3618],
         [ 0.0237,  0.7794,  0.0528],
         [ 0.9666,  0.7761,  0.6108],
         [ 0.3385,  0.8612,  0.1867]]])

>>> # example with padding_idx
>>> weights = torch.rand(10, 3)
>>> weights[0, :].zero_()
>>> embedding_matrix = weights
>>> input = torch.tensor([[0,2,0,5]])
>>> F.embedding(input, embedding_matrix, padding_idx=0)
tensor([[[ 0.0000,  0.0000,  0.0000],
         [ 0.5609,  0.5384,  0.8720],
         [ 0.0000,  0.0000,  0.0000],
         [ 0.6262,  0.2438,  0.7471]]])
 
 embedding_bag  
torch.nn.functional.embedding_bag(input, weight, offsets=None, max_norm=None, norm_type=2, scale_grad_by_freq=False, mode='mean', sparse=False, per_sample_weights=None, include_last_offset=False) [source]
 
Computes sums, means or maxes of bags of embeddings, without instantiating the intermediate embeddings. See torch.nn.EmbeddingBag for more details.  Note This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.   Parameters 
 
input (LongTensor) – Tensor containing bags of indices into the embedding matrix 
weight (Tensor) – The embedding matrix with number of rows equal to the maximum possible index + 1, and number of columns equal to the embedding size 
offsets (LongTensor, optional) – Only used when input is 1D. offsets determines the starting index position of each bag (sequence) in input. 
max_norm (float, optional) – If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm. Note: this will modify weight in-place. 
norm_type (float, optional) – The p in the p-norm to compute for the max_norm option. Default 2. 
scale_grad_by_freq (boolean, optional) – if given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default False. Note: this option is not supported when mode="max". 
mode (string, optional) – "sum", "mean" or "max". Specifies the way to reduce the bag. Default: "mean"
 
sparse (bool, optional) – if True, gradient w.r.t. weight will be a sparse tensor. See Notes under torch.nn.Embedding for more details regarding sparse gradients. Note: this option is not supported when mode="max". 
per_sample_weights (Tensor, optional) – a tensor of float / double weights, or None to indicate all weights should be taken to be 1. If specified, per_sample_weights must have exactly the same shape as input and is treated as having the same offsets, if those are not None. 
include_last_offset (bool, optional) – if True, the size of offsets is equal to the number of bags + 1. 
last element is the size of the input, or the ending index position of the last bag (The) –     Shape:  
input (LongTensor) and offsets (LongTensor, optional)  
If input is 2D of shape (B, N), it will be treated as B bags (sequences) each of fixed length N, and this will return B values aggregated in a way depending on the mode. offsets is ignored and required to be None in this case.  
If input is 1D of shape (N), it will be treated as a concatenation of multiple bags (sequences). offsets is required to be a 1D tensor containing the starting index positions of each bag in input. Therefore, for offsets of shape (B), input will be viewed as having B bags. Empty bags (i.e., having 0-length) will have returned vectors filled by zeros.    
weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
 
per_sample_weights (Tensor, optional). Has the same shape as input. 
output: aggregated embedding values of shape (B, embedding_dim)
  Examples: >>> # an Embedding module containing 10 tensors of size 3
>>> embedding_matrix = torch.rand(10, 3)
>>> # a batch of 2 samples of 4 indices each
>>> input = torch.tensor([1,2,4,5,4,3,2,9])
>>> offsets = torch.tensor([0,4])
>>> F.embedding_bag(embedding_matrix, input, offsets)
tensor([[ 0.3397,  0.3552,  0.5545],
        [ 0.5893,  0.4386,  0.5882]])
 
 one_hot  
torch.nn.functional.one_hot(tensor, num_classes=-1) → LongTensor  
Takes LongTensor with index values of shape (*) and returns a tensor of shape (*, num_classes) that have zeros everywhere except where the index of last dimension matches the corresponding value of the input tensor, in which case it will be 1. See also One-hot on Wikipedia .  Parameters 
 
tensor (LongTensor) – class values of any shape. 
num_classes (int) – Total number of classes. If set to -1, the number of classes will be inferred as one greater than the largest class value in the input tensor.   Returns 
LongTensor that has one more dimension with 1 values at the index of last dimension indicated by the input, and 0 everywhere else.   Examples >>> F.one_hot(torch.arange(0, 5) % 3)
tensor([[1, 0, 0],
        [0, 1, 0],
        [0, 0, 1],
        [1, 0, 0],
        [0, 1, 0]])
>>> F.one_hot(torch.arange(0, 5) % 3, num_classes=5)
tensor([[1, 0, 0, 0, 0],
        [0, 1, 0, 0, 0],
        [0, 0, 1, 0, 0],
        [1, 0, 0, 0, 0],
        [0, 1, 0, 0, 0]])
>>> F.one_hot(torch.arange(0, 6).view(3,2) % 3)
tensor([[[1, 0, 0],
         [0, 1, 0]],
        [[0, 0, 1],
         [1, 0, 0]],
        [[0, 1, 0],
         [0, 0, 1]]])
 
 Distance functions pairwise_distance  
torch.nn.functional.pairwise_distance(x1, x2, p=2.0, eps=1e-06, keepdim=False) [source]
 
See torch.nn.PairwiseDistance for details 
 cosine_similarity  
torch.nn.functional.cosine_similarity(x1, x2, dim=1, eps=1e-8) → Tensor  
Returns cosine similarity between x1 and x2, computed along dim.  similarity=x1⋅x2max⁡(∥x1∥2⋅∥x2∥2,ϵ)\text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}  
 Parameters 
 
x1 (Tensor) – First input. 
x2 (Tensor) – Second input (of size matching x1). 
dim (int, optional) – Dimension of vectors. Default: 1 
eps (float, optional) – Small value to avoid division by zero. Default: 1e-8     Shape:

 Input: (∗1,D,∗2)(\ast_1, D, \ast_2)  where D is at position dim. Output: (∗1,∗2)(\ast_1, \ast_2)  where 1 is at position dim.    Example: >>> input1 = torch.randn(100, 128)
>>> input2 = torch.randn(100, 128)
>>> output = F.cosine_similarity(input1, input2)
>>> print(output)
 
 pdist  
torch.nn.functional.pdist(input, p=2) → Tensor  
Computes the p-norm distance between every pair of row vectors in the input. This is identical to the upper triangular portion, excluding the diagonal, of torch.norm(input[:, None] - input, dim=2, p=p). This function will be faster if the rows are contiguous. If input has shape N×MN \times M  then the output will have shape 12N(N−1)\frac{1}{2} N (N - 1) . This function is equivalent to scipy.spatial.distance.pdist(input, ‘minkowski’, p=p) if p∈(0,∞)p \in (0, \infty) . When p=0p = 0  it is equivalent to scipy.spatial.distance.pdist(input, ‘hamming’) * M. When p=∞p = \infty , the closest scipy function is scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max()).  Parameters 
 
input – input tensor of shape N×MN \times M . 
p – p value for the p-norm distance to calculate between each vector pair ∈[0,∞]\in [0, \infty] .    
 Loss functions binary_cross_entropy  
torch.nn.functional.binary_cross_entropy(input, target, weight=None, size_average=None, reduce=None, reduction='mean') [source]
 
Function that measures the Binary Cross Entropy between the target and the output. See BCELoss for details.  Parameters 
 
input – Tensor of arbitrary shape 
target – Tensor of the same shape as input 
weight (Tensor, optional) – a manual rescaling weight if provided it’s repeated to match input tensor shape 
size_average (bool, optional) – Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
 
reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True
 
reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'
    Examples: >>> input = torch.randn((3, 2), requires_grad=True)
>>> target = torch.rand((3, 2), requires_grad=False)
>>> loss = F.binary_cross_entropy(F.sigmoid(input), target)
>>> loss.backward()
 
 binary_cross_entropy_with_logits  
torch.nn.functional.binary_cross_entropy_with_logits(input, target, weight=None, size_average=None, reduce=None, reduction='mean', pos_weight=None) [source]
 
Function that measures Binary Cross Entropy between target and output logits. See BCEWithLogitsLoss for details.  Parameters 
 
input – Tensor of arbitrary shape 
target – Tensor of the same shape as input 
weight (Tensor, optional) – a manual rescaling weight if provided it’s repeated to match input tensor shape 
size_average (bool, optional) – Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
 
reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True
 
reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'
 
pos_weight (Tensor, optional) – a weight of positive examples. Must be a vector with length equal to the number of classes.    Examples: >>> input = torch.randn(3, requires_grad=True)
>>> target = torch.empty(3).random_(2)
>>> loss = F.binary_cross_entropy_with_logits(input, target)
>>> loss.backward()
 
 poisson_nll_loss  
torch.nn.functional.poisson_nll_loss(input, target, log_input=True, full=False, size_average=None, eps=1e-08, reduce=None, reduction='mean') [source]
 
Poisson negative log likelihood loss. See PoissonNLLLoss for details.  Parameters 
 
input – expectation of underlying Poisson distribution. 
target – random sample target∼Poisson(input)target \sim \text{Poisson}(input) . 
log_input – if True the loss is computed as exp⁡(input)−target∗input\exp(\text{input}) - \text{target} * \text{input} , if False then loss is input−target∗log⁡(input+eps)\text{input} - \text{target} * \log(\text{input}+\text{eps}) . Default: True
 
full – whether to compute full loss, i. e. to add the Stirling approximation term. Default: False target∗log⁡(target)−target+0.5∗log⁡(2∗π∗target)\text{target} * \log(\text{target}) - \text{target} + 0.5 * \log(2 * \pi * \text{target}) . 
size_average (bool, optional) – Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
 
eps (float, optional) – Small value to avoid evaluation of log⁡(0)\log(0)  when log_input`=``False`. Default: 1e-8 
reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True
 
reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'
    
 cosine_embedding_loss  
torch.nn.functional.cosine_embedding_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean') → Tensor [source]
 
See CosineEmbeddingLoss for details. 
 cross_entropy  
torch.nn.functional.cross_entropy(input, target, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean') [source]
 
This criterion combines log_softmax and nll_loss in a single function. See CrossEntropyLoss for details.  Parameters 
 
input (Tensor) – (N,C)(N, C)  where C = number of classes or (N,C,H,W)(N, C, H, W)  in case of 2D Loss, or (N,C,d1,d2,...,dK)(N, C, d_1, d_2, ..., d_K)  where K≥1K \geq 1  in the case of K-dimensional loss. 
target (Tensor) – (N)(N)  where each value is 0≤targets[i]≤C−10 \leq \text{targets}[i] \leq C-1 , or (N,d1,d2,...,dK)(N, d_1, d_2, ..., d_K)  where K≥1K \geq 1  for K-dimensional loss. 
weight (Tensor, optional) – a manual rescaling weight given to each class. If given, has to be a Tensor of size C
 
size_average (bool, optional) – Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
 
ignore_index (int, optional) – Specifies a target value that is ignored and does not contribute to the input gradient. When size_average is True, the loss is averaged over non-ignored targets. Default: -100 
reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True
 
reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'
    Examples: >>> input = torch.randn(3, 5, requires_grad=True)
>>> target = torch.randint(5, (3,), dtype=torch.int64)
>>> loss = F.cross_entropy(input, target)
>>> loss.backward()
 
 ctc_loss  
torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean', zero_infinity=False) [source]
 
The Connectionist Temporal Classification loss. See CTCLoss for details.  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.   Note This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.   Parameters 
 
log_probs – (T,N,C)(T, N, C)  where C = number of characters in alphabet including blank, T = input length, and N = batch size. The logarithmized probabilities of the outputs (e.g. obtained with torch.nn.functional.log_softmax()). 
targets – (N,S)(N, S)  or (sum(target_lengths)). Targets cannot be blank. In the second form, the targets are assumed to be concatenated. 
input_lengths – (N)(N) . Lengths of the inputs (must each be ≤T\leq T ) 
target_lengths – (N)(N) . Lengths of the targets 
blank (int, optional) – Blank label. Default 00 . 
reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the output losses will be divided by the target lengths and then the mean over the batch is taken, 'sum': the output will be summed. Default: 'mean'
 
zero_infinity (bool, optional) – Whether to zero infinite losses and the associated gradients. Default: False Infinite losses mainly occur when the inputs are too short to be aligned to the targets.    Example: >>> log_probs = torch.randn(50, 16, 20).log_softmax(2).detach().requires_grad_()
>>> targets = torch.randint(1, 20, (16, 30), dtype=torch.long)
>>> input_lengths = torch.full((16,), 50, dtype=torch.long)
>>> target_lengths = torch.randint(10,30,(16,), dtype=torch.long)
>>> loss = F.ctc_loss(log_probs, targets, input_lengths, target_lengths)
>>> loss.backward()
 
 hinge_embedding_loss  
torch.nn.functional.hinge_embedding_loss(input, target, margin=1.0, size_average=None, reduce=None, reduction='mean') → Tensor [source]
 
See HingeEmbeddingLoss for details. 
 kl_div  
torch.nn.functional.kl_div(input, target, size_average=None, reduce=None, reduction='mean', log_target=False) [source]
 
The Kullback-Leibler divergence Loss See KLDivLoss for details.  Parameters 
 
input – Tensor of arbitrary shape 
target – Tensor of the same shape as input 
size_average (bool, optional) – Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
 
reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True
 
reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'batchmean' | 'sum' | 'mean'. 'none': no reduction will be applied 'batchmean': the sum of the output will be divided by the batchsize 'sum': the output will be summed 'mean': the output will be divided by the number of elements in the output Default: 'mean'
 
log_target (bool) – A flag indicating whether target is passed in the log space. It is recommended to pass certain distributions (like softmax) in the log space to avoid numerical issues caused by explicit log. Default: False
     Note size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction.   Note :attr:reduction = 'mean' doesn’t return the true kl divergence value, please use :attr:reduction = 'batchmean' which aligns with KL math definition. In the next major release, 'mean' will be changed to be the same as ‘batchmean’.  
 l1_loss  
torch.nn.functional.l1_loss(input, target, size_average=None, reduce=None, reduction='mean') → Tensor [source]
 
Function that takes the mean element-wise absolute value difference. See L1Loss for details. 
 mse_loss  
torch.nn.functional.mse_loss(input, target, size_average=None, reduce=None, reduction='mean') → Tensor [source]
 
Measures the element-wise mean squared error. See MSELoss for details. 
 margin_ranking_loss  
torch.nn.functional.margin_ranking_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean') → Tensor [source]
 
See MarginRankingLoss for details. 
 multilabel_margin_loss  
torch.nn.functional.multilabel_margin_loss(input, target, size_average=None, reduce=None, reduction='mean') → Tensor [source]
 
See MultiLabelMarginLoss for details. 
 multilabel_soft_margin_loss  
torch.nn.functional.multilabel_soft_margin_loss(input, target, weight=None, size_average=None) → Tensor [source]
 
See MultiLabelSoftMarginLoss for details. 
 multi_margin_loss  
torch.nn.functional.multi_margin_loss(input, target, p=1, margin=1.0, weight=None, size_average=None, reduce=None, reduction='mean') [source]
 
 multi_margin_loss(input, target, p=1, margin=1, weight=None, size_average=None,

reduce=None, reduction=’mean’) -> Tensor   See MultiMarginLoss for details. 
 nll_loss  
torch.nn.functional.nll_loss(input, target, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean') [source]
 
The negative log likelihood loss. See NLLLoss for details.  Parameters 
 
input – (N,C)(N, C)  where C = number of classes or (N,C,H,W)(N, C, H, W)  in case of 2D Loss, or (N,C,d1,d2,...,dK)(N, C, d_1, d_2, ..., d_K)  where K≥1K \geq 1  in the case of K-dimensional loss. 
target – (N)(N)  where each value is 0≤targets[i]≤C−10 \leq \text{targets}[i] \leq C-1 , or (N,d1,d2,...,dK)(N, d_1, d_2, ..., d_K)  where K≥1K \geq 1  for K-dimensional loss. 
weight (Tensor, optional) – a manual rescaling weight given to each class. If given, has to be a Tensor of size C
 
size_average (bool, optional) – Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
 
ignore_index (int, optional) – Specifies a target value that is ignored and does not contribute to the input gradient. When size_average is True, the loss is averaged over non-ignored targets. Default: -100 
reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True
 
reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'
    Example: >>> # input is of size N x C = 3 x 5
>>> input = torch.randn(3, 5, requires_grad=True)
>>> # each element in target has to have 0 <= value < C
>>> target = torch.tensor([1, 0, 4])
>>> output = F.nll_loss(F.log_softmax(input), target)
>>> output.backward()
 
 smooth_l1_loss  
torch.nn.functional.smooth_l1_loss(input, target, size_average=None, reduce=None, reduction='mean', beta=1.0) [source]
 
Function that uses a squared term if the absolute element-wise error falls below beta and an L1 term otherwise. See SmoothL1Loss for details. 
 soft_margin_loss  
torch.nn.functional.soft_margin_loss(input, target, size_average=None, reduce=None, reduction='mean') → Tensor [source]
 
See SoftMarginLoss for details. 
 triplet_margin_loss  
torch.nn.functional.triplet_margin_loss(anchor, positive, negative, margin=1.0, p=2, eps=1e-06, swap=False, size_average=None, reduce=None, reduction='mean') [source]
 
See TripletMarginLoss for details 
 triplet_margin_with_distance_loss  
torch.nn.functional.triplet_margin_with_distance_loss(anchor, positive, negative, *, distance_function=None, margin=1.0, swap=False, reduction='mean') [source]
 
See TripletMarginWithDistanceLoss for details. 
 Vision functions pixel_shuffle  
torch.nn.functional.pixel_shuffle(input, upscale_factor) → Tensor  
Rearranges elements in a tensor of shape (∗,C×r2,H,W)(*, C \times r^2, H, W)  to a tensor of shape (∗,C,H×r,W×r)(*, C, H \times r, W \times r) , where r is the upscale_factor. See PixelShuffle for details.  Parameters 
 
input (Tensor) – the input tensor 
upscale_factor (int) – factor to increase spatial resolution by    Examples: >>> input = torch.randn(1, 9, 4, 4)
>>> output = torch.nn.functional.pixel_shuffle(input, 3)
>>> print(output.size())
torch.Size([1, 1, 12, 12])
 
 pixel_unshuffle  
torch.nn.functional.pixel_unshuffle(input, downscale_factor) → Tensor  
Reverses the PixelShuffle operation by rearranging elements in a tensor of shape (∗,C,H×r,W×r)(*, C, H \times r, W \times r)  to a tensor of shape (∗,C×r2,H,W)(*, C \times r^2, H, W) , where r is the downscale_factor. See PixelUnshuffle for details.  Parameters 
 
input (Tensor) – the input tensor 
downscale_factor (int) – factor to increase spatial resolution by    Examples: >>> input = torch.randn(1, 1, 12, 12)
>>> output = torch.nn.functional.pixel_unshuffle(input, 3)
>>> print(output.size())
torch.Size([1, 9, 4, 4])
 
 pad  
torch.nn.functional.pad(input, pad, mode='constant', value=0)  
Pads tensor.  Padding size:

The padding size by which to pad some dimensions of input are described starting from the last dimension and moving forward. ⌊len(pad)2⌋\left\lfloor\frac{\text{len(pad)}}{2}\right\rfloor  dimensions of input will be padded. For example, to pad only the last dimension of the input tensor, then pad has the form (padding_left,padding_right)(\text{padding\_left}, \text{padding\_right}) ; to pad the last 2 dimensions of the input tensor, then use (padding_left,padding_right,(\text{padding\_left}, \text{padding\_right},  padding_top,padding_bottom)\text{padding\_top}, \text{padding\_bottom}) ; to pad the last 3 dimensions, use (padding_left,padding_right,(\text{padding\_left}, \text{padding\_right},  padding_top,padding_bottom\text{padding\_top}, \text{padding\_bottom}  padding_front,padding_back)\text{padding\_front}, \text{padding\_back}) .  Padding mode:

See torch.nn.ConstantPad2d, torch.nn.ReflectionPad2d, and torch.nn.ReplicationPad2d for concrete examples on how each of the padding modes works. Constant padding is implemented for arbitrary dimensions. Replicate padding is implemented for padding the last 3 dimensions of 5D input tensor, or the last 2 dimensions of 4D input tensor, or the last dimension of 3D input tensor. Reflect padding is only implemented for padding the last 2 dimensions of 4D input tensor, or the last dimension of 3D input tensor.    Note When using the CUDA backend, this operation may induce nondeterministic behaviour in its backward pass that is not easily switched off. Please see the notes on Reproducibility for background.   Parameters 
 
input (Tensor) – N-dimensional tensor 
pad (tuple) – m-elements tuple, where m2≤\frac{m}{2} \leq  input dimensions and mm  is even. 
mode – 'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'
 
value – fill value for 'constant' padding. Default: 0
    Examples: >>> t4d = torch.empty(3, 3, 4, 2)
>>> p1d = (1, 1) # pad last dim by 1 on each side
>>> out = F.pad(t4d, p1d, "constant", 0)  # effectively zero padding
>>> print(out.size())
torch.Size([3, 3, 4, 4])
>>> p2d = (1, 1, 2, 2) # pad last dim by (1, 1) and 2nd to last by (2, 2)
>>> out = F.pad(t4d, p2d, "constant", 0)
>>> print(out.size())
torch.Size([3, 3, 8, 4])
>>> t4d = torch.empty(3, 3, 4, 2)
>>> p3d = (0, 1, 2, 1, 3, 3) # pad by (0, 1), (2, 1), and (3, 3)
>>> out = F.pad(t4d, p3d, "constant", 0)
>>> print(out.size())
torch.Size([3, 9, 7, 3])
 
 interpolate  
torch.nn.functional.interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None) [source]
 
Down/up samples the input to either the given size or the given scale_factor The algorithm used for interpolation is determined by mode. Currently temporal, spatial and volumetric sampling are supported, i.e. expected inputs are 3-D, 4-D or 5-D in shape. The input dimensions are interpreted in the form: mini-batch x channels x [optional depth] x [optional height] x width. The modes available for resizing are: nearest, linear (3D-only), bilinear, bicubic (4D-only), trilinear (5D-only), area  Parameters 
 
input (Tensor) – the input tensor 
size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]) – output spatial size. 
scale_factor (float or Tuple[float]) – multiplier for spatial size. Has to match input size if it is a tuple. 
mode (str) – algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area'. Default: 'nearest'
 
align_corners (bool, optional) – Geometrically, we consider the pixels of the input and output as squares rather than points. If set to True, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels. If set to False, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation independent of input size when scale_factor is kept the same. This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: False
 
recompute_scale_factor (bool, optional) – recompute the scale_factor for use in the interpolation calculation. When scale_factor is passed as a parameter, it is used to compute the output_size. If recompute_scale_factor is False or not specified, the passed-in scale_factor will be used in the interpolation computation. Otherwise, a new scale_factor will be computed based on the output and input sizes for use in the interpolation computation (i.e. the computation will be identical to if the computed output_size were passed-in explicitly). Note that when scale_factor is floating-point, the recomputed scale_factor may differ from the one passed in due to rounding and precision issues.     Note With mode='bicubic', it’s possible to cause overshoot, in other words it can produce negative values or values greater than 255 for images. Explicitly call result.clamp(min=0, max=255) if you want to reduce the overshoot when displaying the image.   Warning With align_corners = True, the linearly interpolating modes (linear, bilinear, and trilinear) don’t proportionally align the output and input pixels, and thus the output values can depend on the input size. This was the default behavior for these modes up to version 0.3.1. Since then, the default behavior is align_corners = False. See Upsample for concrete examples on how this affects the outputs.   Warning When scale_factor is specified, if recompute_scale_factor=True, scale_factor is used to compute the output_size which will then be used to infer new scales for the interpolation. The default behavior for recompute_scale_factor changed to False in 1.6.0, and scale_factor is used in the interpolation calculation.   Note This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.  
 upsample  
torch.nn.functional.upsample(input, size=None, scale_factor=None, mode='nearest', align_corners=None) [source]
 
Upsamples the input to either the given size or the given scale_factor  Warning This function is deprecated in favor of torch.nn.functional.interpolate(). This is equivalent with nn.functional.interpolate(...).   Note This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.  The algorithm used for upsampling is determined by mode. Currently temporal, spatial and volumetric upsampling are supported, i.e. expected inputs are 3-D, 4-D or 5-D in shape. The input dimensions are interpreted in the form: mini-batch x channels x [optional depth] x [optional height] x width. The modes available for upsampling are: nearest, linear (3D-only), bilinear, bicubic (4D-only), trilinear (5D-only)  Parameters 
 
input (Tensor) – the input tensor 
size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]) – output spatial size. 
scale_factor (float or Tuple[float]) – multiplier for spatial size. Has to match input size if it is a tuple. 
mode (string) – algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear'. Default: 'nearest'
 
align_corners (bool, optional) – Geometrically, we consider the pixels of the input and output as squares rather than points. If set to True, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels. If set to False, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation independent of input size when scale_factor is kept the same. This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: False
     Note With mode='bicubic', it’s possible to cause overshoot, in other words it can produce negative values or values greater than 255 for images. Explicitly call result.clamp(min=0, max=255) if you want to reduce the overshoot when displaying the image.   Warning With align_corners = True, the linearly interpolating modes (linear, bilinear, and trilinear) don’t proportionally align the output and input pixels, and thus the output values can depend on the input size. This was the default behavior for these modes up to version 0.3.1. Since then, the default behavior is align_corners = False. See Upsample for concrete examples on how this affects the outputs.  
 upsample_nearest  
torch.nn.functional.upsample_nearest(input, size=None, scale_factor=None) [source]
 
Upsamples the input, using nearest neighbours’ pixel values.  Warning This function is deprecated in favor of torch.nn.functional.interpolate(). This is equivalent with nn.functional.interpolate(..., mode='nearest').  Currently spatial and volumetric upsampling are supported (i.e. expected inputs are 4 or 5 dimensional).  Parameters 
 
input (Tensor) – input 
size (int or Tuple[int, int] or Tuple[int, int, int]) – output spatia size. 
scale_factor (int) – multiplier for spatial size. Has to be an integer.     Note This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.  
 upsample_bilinear  
torch.nn.functional.upsample_bilinear(input, size=None, scale_factor=None) [source]
 
Upsamples the input, using bilinear upsampling.  Warning This function is deprecated in favor of torch.nn.functional.interpolate(). This is equivalent with nn.functional.interpolate(..., mode='bilinear', align_corners=True).  Expected inputs are spatial (4 dimensional). Use upsample_trilinear fo volumetric (5 dimensional) inputs.  Parameters 
 
input (Tensor) – input 
size (int or Tuple[int, int]) – output spatial size. 
scale_factor (int or Tuple[int, int]) – multiplier for spatial size     Note This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.  
 grid_sample  
torch.nn.functional.grid_sample(input, grid, mode='bilinear', padding_mode='zeros', align_corners=None) [source]
 
Given an input and a flow-field grid, computes the output using input values and pixel locations from grid. Currently, only spatial (4-D) and volumetric (5-D) input are supported. In the spatial (4-D) case, for input with shape (N,C,Hin,Win)(N, C, H_\text{in}, W_\text{in})  and grid with shape (N,Hout,Wout,2)(N, H_\text{out}, W_\text{out}, 2) , the output will have shape (N,C,Hout,Wout)(N, C, H_\text{out}, W_\text{out}) . For each output location output[n, :, h, w], the size-2 vector grid[n, h, w] specifies input pixel locations x and y, which are used to interpolate the output value output[n, :, h, w]. In the case of 5D inputs, grid[n, d, h, w] specifies the x, y, z pixel locations for interpolating output[n, :, d, h, w]. mode argument specifies nearest or bilinear interpolation method to sample the input pixels. grid specifies the sampling pixel locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. For example, values x = -1, y = -1 is the left-top pixel of input, and values x = 1, y = 1 is the right-bottom pixel of input. If grid has values outside the range of [-1, 1], the corresponding outputs are handled as defined by padding_mode. Options are  
padding_mode="zeros": use 0 for out-of-bound grid locations, 
padding_mode="border": use border values for out-of-bound grid locations, 
padding_mode="reflection": use values at locations reflected by the border for out-of-bound grid locations. For location far away from the border, it will keep being reflected until becoming in bound, e.g., (normalized) pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = -0.5.   Note This function is often used in conjunction with affine_grid() to build Spatial Transformer Networks .   Note When using the CUDA backend, this operation may induce nondeterministic behaviour in its backward pass that is not easily switched off. Please see the notes on Reproducibility for background.   Note NaN values in grid would be interpreted as -1.   Parameters 
 
input (Tensor) – input of shape (N,C,Hin,Win)(N, C, H_\text{in}, W_\text{in})  (4-D case) or (N,C,Din,Hin,Win)(N, C, D_\text{in}, H_\text{in}, W_\text{in})  (5-D case) 
grid (Tensor) – flow-field of shape (N,Hout,Wout,2)(N, H_\text{out}, W_\text{out}, 2)  (4-D case) or (N,Dout,Hout,Wout,3)(N, D_\text{out}, H_\text{out}, W_\text{out}, 3)  (5-D case) 
mode (str) – interpolation mode to calculate output values 'bilinear' | 'nearest' | 'bicubic'. Default: 'bilinear' Note: mode='bicubic' supports only 4-D input. When mode='bilinear' and the input is 5-D, the interpolation mode used internally will actually be trilinear. However, when the input is 4-D, the interpolation mode will legitimately be bilinear. 
padding_mode (str) – padding mode for outside grid values 'zeros' | 'border' | 'reflection'. Default: 'zeros'
 
align_corners (bool, optional) – Geometrically, we consider the pixels of the input as squares rather than points. If set to True, the extrema (-1 and 1) are considered as referring to the center points of the input’s corner pixels. If set to False, they are instead considered as referring to the corner points of the input’s corner pixels, making the sampling more resolution agnostic. This option parallels the align_corners option in interpolate(), and so whichever option is used here should also be used there to resize the input image before grid sampling. Default: False
   Returns 
output Tensor  Return type 
output (Tensor)    Warning When align_corners = True, the grid positions depend on the pixel size relative to the input image size, and so the locations sampled by grid_sample() will differ for the same input given at different resolutions (that is, after being upsampled or downsampled). The default behavior up to version 1.2.0 was align_corners = True. Since then, the default behavior has been changed to align_corners = False, in order to bring it in line with the default for interpolate().   Note mode='bicubic' is implemented using the cubic convolution algorithm with α=−0.75\alpha=-0.75 . The constant α\alpha  might be different from packages to packages. For example, PIL and OpenCV use -0.5 and -0.75 respectively. This algorithm may “overshoot” the range of values it’s interpolating. For example, it may produce negative values or values greater than 255 when interpolating input in [0, 255]. Clamp the results with :func: torch.clamp to ensure they are within the valid range.  
 affine_grid  
torch.nn.functional.affine_grid(theta, size, align_corners=None) [source]
 
Generates a 2D or 3D flow field (sampling grid), given a batch of affine matrices theta.  Note This function is often used in conjunction with grid_sample() to build Spatial Transformer Networks .   Parameters 
 
theta (Tensor) – input batch of affine matrices with shape (N×2×3N \times 2 \times 3 ) for 2D or (N×3×4N \times 3 \times 4 ) for 3D 
size (torch.Size) – the target output image size. (N×C×H×WN \times C \times H \times W  for 2D or N×C×D×H×WN \times C \times D \times H \times W  for 3D) Example: torch.Size((32, 3, 24, 24)) 
align_corners (bool, optional) – if True, consider -1 and 1 to refer to the centers of the corner pixels rather than the image corners. Refer to grid_sample() for a more complete description. A grid generated by affine_grid() should be passed to grid_sample() with the same setting for this option. Default: False
   Returns 
output Tensor of size (N×H×W×2N \times H \times W \times 2 )  Return type 
output (Tensor)    Warning When align_corners = True, the grid positions depend on the pixel size relative to the input image size, and so the locations sampled by grid_sample() will differ for the same input given at different resolutions (that is, after being upsampled or downsampled). The default behavior up to version 1.2.0 was align_corners = True. Since then, the default behavior has been changed to align_corners = False, in order to bring it in line with the default for interpolate().   Warning When align_corners = True, 2D affine transforms on 1D data and 3D affine transforms on 2D data (that is, when one of the spatial dimensions has unit size) are ill-defined, and not an intended use case. This is not a problem when align_corners = False. Up to version 1.2.0, all grid points along a unit dimension were considered arbitrarily to be at -1. From version 1.3.0, under align_corners = True all grid points along a unit dimension are considered to be at `0 (the center of the input image).  
 DataParallel functions (multi-GPU, distributed) data_parallel  
torch.nn.parallel.data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None) [source]
 
Evaluates module(input) in parallel across the GPUs given in device_ids. This is the functional version of the DataParallel module.  Parameters 
 
module (Module) – the module to evaluate in parallel 
inputs (Tensor) – inputs to the module 
device_ids (list of python:int or torch.device) – GPU ids on which to replicate module 
output_device (list of python:int or torch.device) – GPU location of the output Use -1 to indicate the CPU. (default: device_ids[0])   Returns 
a Tensor containing the result of module(input) located on output_device