oneflow.nn.functional¶

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”.

`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.
`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.

`threshold`
`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)`
`prelu`	Applies the element-wise function:
`glu`	The equation is:
`gelu`	The equation is:
`logsigmoid`	Applies the element-wise function:
`hardshrink`
`softsign`	The formula is:
`softplus`	Applies the element-wise function:
`softmax`	Softmax is defined as:
`softshrink`
`log_softmax`	LogSoftmax is defined as:
`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

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

During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution.

`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.

`sparse_softmax_cross_entropy`	The interface is consistent with TensorFlow.
`cross_entropy`	See `CrossEntropyLoss` for details.
`smooth_l1_loss`
`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 \(0\).

`pad`	Pads tensor.
`interpolate`	The interface is consistent with PyTorch.
`grid_sample`	The interface is consistent with PyTorch.
`affine_grid`	The interface is consistent with PyTorch.