oneflow.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”.
fold	The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.fold.html.
unfold	The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.unfold.html.

BatchNorm functions

batch_norm

Applies Batch Normalization for each channel across a batch of data.

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	Computes a partial inverse of MaxPool1d.
max_unpool2d	Computes a partial inverse of MaxPool2d.
max_unpool3d	Computes a partial inverse of MaxPool3d.
adaptive_avg_pool1d	Applies a 1D adaptive average pooling over an input signal composed of several input planes.
adaptive_avg_pool2d	Applies a 2D adaptive average pooling over an input signal composed of several input planes.
adaptive_avg_pool3d	Applies a 3D adaptive 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.

Non-linear activation functions

threshold	Thresholds each element of the input Tensor.
relu	Applies the rectified linear unit function element-wise.
hardtanh	Applies the HardTanh function element-wise.
hardswish	Applies the hardswish function, element-wise, as described in the paper:
relu6	Applies the element-wise function \(\text{ReLU6}(x) = \min(\max(0,x), 6)\).
elu	Applies element-wise,
selu	Applies element-wise function
celu	Applies the element-wise function:
leaky_relu	Applies element-wise, :math:` ext{LeakyReLU}(x) = max(0, x) + ext{negative_slope} * min(0, x)`
square_relu	Applies the relu^2 activation introduced in https://arxiv.org/abs/2109.08668v2
prelu	Applies the element-wise function:
glu	The equation is:
gelu	Applies the Gaussian Error Linear Units function:
quick_gelu	Applies GELU approximation that is fast but somewhat inaccurate.
logsigmoid	Applies the element-wise function:
hardshrink	Applies the hard shrinkage function in an element-wise manner.
softsign	The formula is:
softplus	Applies the element-wise function:
softmax	Applies a softmax function.
softshrink	Applies the soft shrinkage function in an element-wise manner.
log_softmax	LogSoftmax is defined as:
gumbel_softmax	Solve the problem that the output values of argmax do not reflect the probability distribution of the model’s output.
tanh	The equation is:
sigmoid	Applies the element-wise function \(\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}\)
hardsigmoid	Applies the element-wise function
silu	The formula is:
mish	Applies the element-wise function:
layer_norm	Applies Layer Normalization for last certain number of dimensions.
normalize	Performs \(L_p\) normalization of inputs over specified dimension

Linear functions

linear

Applies a linear transformation to the incoming data: \(y = xA^T + b\).

Dropout functions

dropout	During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution.
dropout1d	The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.dropout1d.html.
dropout2d	dropout1d(x: Tensor, p: float = 0.5, training: bool = True) -> Tensor
dropout3d	dropout1d(x: Tensor, p: float = 0.5, training: bool = True) -> Tensor

Sparse functions

embedding	A simple lookup table that looks up embeddings in a fixed dictionary and size.
one_hot	This operator generates a onehot Tensor from input Tensor.

Distance functions

cosine_similarity	Returns cosine similarity between x1 and x2, computed along dim.
pairwise_distance	Computes the pairwise distance between vectors \(v_1\), \(v_2\) using the p-norm:

Loss functions

sparse_softmax_cross_entropy	The interface is consistent with TensorFlow.
cross_entropy	See CrossEntropyLoss for details.
ctc_loss	The Connectionist Temporal Classification loss.
l1_loss	This operator computes the L1 loss between each element in input and target.
mse_loss	This operator computes the mean squared error (squared L2 norm) loss between each element in input and target.
smooth_l1_loss	Function that uses a squared term if the absolute element-wise error falls below beta and an L1 term otherwise.
triplet_margin_loss	Creates a criterion that measures the triplet loss given an input tensors \(x1\), \(x2\), \(x3\) and a margin with a value greater than \(0\).
binary_cross_entropy	The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.binary_cross_entropy.html.
binary_cross_entropy_with_logits	The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.binary_cross_entropy_with_logits.html.

Vision functions

deform_conv2d	Performs Deformable Convolution v2, described in Deformable ConvNets v2: More Deformable, Better Results if mask is not None and Performs Deformable Convolution, described in Deformable Convolutional Networks if mask is None.
pad	Pads tensor.
interpolate	The interface is consistent with PyTorch.
upsample	alias of oneflow.nn.modules.upsampling.Upsample
grid_sample	The interface is consistent with PyTorch.
affine_grid	The interface is consistent with PyTorch.

Greedy decoder

ctc_greedy_decoder

Performs greedy decoding on the logits given in input (best path).