torch.nn.functional

Convolution functions

conv1d	Applies a 1D convolution over an input signal composed of several input planes.
conv2d	Applies a 2D convolution over an input image composed of several input planes.
conv3d	Applies a 3D convolution over an input image composed of several input planes.
conv_transpose1d	Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called "deconvolution".
conv_transpose2d	Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called "deconvolution".
conv_transpose3d	Applies a 3D transposed convolution operator over an input image composed of several input planes, sometimes also called "deconvolution"
unfold	Extract sliding local blocks from a batched input tensor.
fold	Combine an array of sliding local blocks into a large containing tensor.

Pooling functions

avg_pool1d	Applies a 1D average pooling over an input signal composed of several input planes.
avg_pool2d	Applies 2D average-pooling operation in $kH \times kW$ regions by step size $sH \times sW$ steps.
avg_pool3d	Applies 3D average-pooling operation in $kT \times kH \times kW$ regions by step size $sT \times sH \times sW$ steps.
max_pool1d	Applies a 1D max pooling over an input signal composed of several input planes.
max_pool2d	Applies a 2D max pooling over an input signal composed of several input planes.
max_pool3d	Applies a 3D max pooling over an input signal composed of several input planes.
max_unpool1d	Compute a partial inverse of MaxPool1d.
max_unpool2d	Compute a partial inverse of MaxPool2d.
max_unpool3d	Compute a partial inverse of MaxPool3d.
lp_pool1d	Apply a 1D power-average pooling over an input signal composed of several input planes.
lp_pool2d	Apply a 2D power-average pooling over an input signal composed of several input planes.
lp_pool3d	Apply a 3D power-average pooling over an input signal composed of several input planes.
adaptive_max_pool1d	Applies a 1D adaptive max pooling over an input signal composed of several input planes.
adaptive_max_pool2d	Applies a 2D adaptive max pooling over an input signal composed of several input planes.
adaptive_max_pool3d	Applies a 3D adaptive max pooling over an input signal composed of several input planes.
adaptive_avg_pool1d	Applies a 1D adaptive average pooling over an input signal composed of several input planes.
adaptive_avg_pool2d	Apply a 2D adaptive average pooling over an input signal composed of several input planes.
adaptive_avg_pool3d	Apply a 3D adaptive average pooling over an input signal composed of several input planes.
fractional_max_pool2d	Applies 2D fractional max pooling over an input signal composed of several input planes.
fractional_max_pool3d	Applies 3D fractional max pooling over an input signal composed of several input planes.

Attention Mechanisms

The torch.nn.attention.bias module contains attention_biases that are designed to be used with scaled_dot_product_attention.

scaled_dot_product_attention

scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0,

Non-linear activation functions

threshold	Apply a threshold to each element of the input Tensor.
threshold_	In-place version of threshold().
relu	Applies the rectified linear unit function element-wise.
relu_	In-place version of relu().
hardtanh	Applies the HardTanh function element-wise.
hardtanh_	In-place version of hardtanh().
hardswish	Apply hardswish function, element-wise.
relu6	Applies the element-wise function $\text{ReLU6}(x) = \min(\max(0,x), 6)$.
elu	Apply the Exponential Linear Unit (ELU) function element-wise.
elu_	In-place version of elu().
selu	Applies element-wise, $\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))$, with $\alpha=1.6732632423543772848170429916717$ and $scale=1.0507009873554804934193349852946$.
celu	Applies element-wise, $\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))$.
leaky_relu	Applies element-wise, $\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)$
leaky_relu_	In-place version of leaky_relu().
prelu	Applies element-wise the function $\text{PReLU}(x) = \max(0,x) + \text{weight} * \min(0,x)$ where weight is a learnable parameter.
rrelu	Randomized leaky ReLU.
rrelu_	In-place version of rrelu().
glu	The gated linear unit.
gelu	When the approximate argument is 'none', it applies element-wise the function $\text{GELU}(x) = x * \Phi(x)$
logsigmoid	Applies element-wise $\text{LogSigmoid}(x_i) = \log \left(\frac{1}{1 + \exp(-x_i)}\right)$
hardshrink	Applies the hard shrinkage function element-wise
tanhshrink	Applies element-wise, $\text{Tanhshrink}(x) = x - \text{Tanh}(x)$
softsign	Applies element-wise, the function $\text{SoftSign}(x) = \frac{x}{1 + |x|}$
softplus	Applies element-wise, the function $\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))$.
softmin	Apply a softmin function.
softmax	Apply a softmax function.
softshrink	Applies the soft shrinkage function elementwise
gumbel_softmax	Sample from the Gumbel-Softmax distribution (Link 1 Link 2) and optionally discretize.
log_softmax	Apply a softmax followed by a logarithm.
tanh	Applies element-wise, $\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}$
sigmoid	Applies the element-wise function $\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}$
hardsigmoid	Apply the Hardsigmoid function element-wise.
silu	Apply the Sigmoid Linear Unit (SiLU) function, element-wise.
mish	Apply the Mish function, element-wise.
batch_norm	Apply Batch Normalization for each channel across a batch of data.
group_norm	Apply Group Normalization for last certain number of dimensions.
instance_norm	Apply Instance Normalization independently for each channel in every data sample within a batch.
layer_norm	Apply Layer Normalization for last certain number of dimensions.
local_response_norm	Apply local response normalization over an input signal.
rms_norm	Apply Root Mean Square Layer Normalization.
normalize	Perform $L_p$ normalization of inputs over specified dimension.

Linear functions

linear	Applies a linear transformation to the incoming data: $y = xA^T + b$.
bilinear	Applies a bilinear transformation to the incoming data: $y = x_1^T A x_2 + b$

Dropout functions

dropout	During training, randomly zeroes some elements of the input tensor with probability p.
alpha_dropout	Apply alpha dropout to the input.
feature_alpha_dropout	Randomly masks out entire channels (a channel is a feature map).
dropout1d	Randomly zero out entire channels (a channel is a 1D feature map).
dropout2d	Randomly zero out entire channels (a channel is a 2D feature map).
dropout3d	Randomly zero out entire channels (a channel is a 3D feature map).

Sparse functions

embedding	Generate a simple lookup table that looks up embeddings in a fixed dictionary and size.
embedding_bag	Compute sums, means or maxes of bags of embeddings.
one_hot	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.

Distance functions

pairwise_distance	See torch.nn.PairwiseDistance for details
cosine_similarity	Returns cosine similarity between x1 and x2, computed along dim.
pdist	Computes the p-norm distance between every pair of row vectors in the input.

Loss functions

binary_cross_entropy	Measure Binary Cross Entropy between the target and input probabilities.
binary_cross_entropy_with_logits	Calculate Binary Cross Entropy between target and input logits.
poisson_nll_loss	Poisson negative log likelihood loss.
cosine_embedding_loss	See CosineEmbeddingLoss for details.
cross_entropy	Compute the cross entropy loss between input logits and target.
ctc_loss	Apply the Connectionist Temporal Classification loss.
gaussian_nll_loss	Gaussian negative log likelihood loss.
hinge_embedding_loss	See HingeEmbeddingLoss for details.
kl_div	Compute the KL Divergence loss.
l1_loss	Function that takes the mean element-wise absolute value difference.
mse_loss	Measures the element-wise mean squared error.
margin_ranking_loss	See MarginRankingLoss for details.
multilabel_margin_loss	See MultiLabelMarginLoss for details.
multilabel_soft_margin_loss	See MultiLabelSoftMarginLoss for details.
multi_margin_loss	See MultiMarginLoss for details.
nll_loss	Compute the negative log likelihood loss.
huber_loss	Compute the Huber loss.
smooth_l1_loss	Compute the Smooth L1 loss.
soft_margin_loss	See SoftMarginLoss for details.
triplet_margin_loss	Compute the triplet loss between given input tensors and a margin greater than 0.
triplet_margin_with_distance_loss	Compute the triplet margin loss for input tensors using a custom distance function.

Vision functions

pixel_shuffle	Rearranges elements in a tensor of shape $(*, C \times r^2, H, W)$ to a tensor of shape $(*, C, H \times r, W \times r)$, where r is the upscale_factor.
pixel_unshuffle	Reverses the PixelShuffle operation by rearranging elements in a tensor of shape $(*, C, H \times r, W \times r)$ to a tensor of shape $(*, C \times r^2, H, W)$, where r is the downscale_factor.
pad	Pads tensor.
interpolate	Down/up samples the input.
upsample	Upsample input.
upsample_nearest	Upsamples the input, using nearest neighbours' pixel values.
upsample_bilinear	Upsamples the input, using bilinear upsampling.
grid_sample	Compute grid sample.
affine_grid	Generate 2D or 3D flow field (sampling grid), given a batch of affine matrices theta.

DataParallel functions (multi-GPU, distributed)

data_parallel

torch.nn.parallel.data_parallel

Evaluate module(input) in parallel across the GPUs given in device_ids.