# Relay Core Tensor Operators¶

This page contains the list of core tensor operator primitives pre-defined in tvm.relay. The core tensor operator primitives cover typical workloads in deep learning. They can represent workloads in front-end frameworks and provide basic building blocks for optimization. Since deep learning is a fast evolving field, it is possible to have operators that are not in here.

Note

This document will directly list the function signature of these operators in the python frontend.

## Overview of Operators¶

Level 1: Basic Operators

This level enables fully connected multi-layer perceptron.

 tvm.relay.log Compute elementwise log of data. tvm.relay.sqrt Compute elementwise sqrt of data. tvm.relay.rsqrt Compute elementwise rsqrt of data. tvm.relay.exp Compute elementwise exp of data. tvm.relay.sigmoid Compute elementwise sigmoid of data. tvm.relay.add Addition with numpy-style broadcasting. tvm.relay.subtract Subtraction with numpy-style broadcasting. tvm.relay.multiply Multiplication with numpy-style broadcasting. tvm.relay.divide Division with numpy-style broadcasting. tvm.relay.mod Mod with numpy-style broadcasting. tvm.relay.tanh Compute element-wise tanh of data. tvm.relay.concatenate Concatenate the input tensors along the given axis. tvm.relay.expand_dims Insert num_newaxis axises at the position given by axis. tvm.relay.nn.softmax Computes softmax. tvm.relay.nn.log_softmax Computes log softmax. tvm.relay.nn.relu Rectified linear unit. tvm.relay.nn.dropout Applies the dropout operation to the input array. tvm.relay.nn.batch_norm Batch normalization layer (Ioffe and Szegedy, 2014). tvm.relay.nn.bias_add add_bias operator.

Level 2: Convolutions

This level enables typical convnet models.

 tvm.relay.nn.conv2d 2D convolution. tvm.relay.nn.conv2d_transpose Two dimensional transposed convolution operator. tvm.relay.nn.dense Dense operator. tvm.relay.nn.max_pool2d 2D maximum pooling operator. tvm.relay.nn.avg_pool2d 2D average pooling operator. tvm.relay.nn.global_max_pool2d 2D global maximum pooling operator. tvm.relay.nn.global_avg_pool2d 2D global average pooling operator. tvm.relay.nn.upsampling Upsampling. tvm.relay.nn.batch_flatten BatchFlatten. tvm.relay.nn.pad Padding tvm.relay.nn.lrn This operator takes data as input and does local response normalization. tvm.relay.nn.l2_normalize Perform L2 normalization on the input data tvm.relay.nn.contrib_conv2d_winograd_without_weight_transform 2D convolution with winograd algorithm. tvm.relay.nn.contrib_conv2d_winograd_weight_transform Weight Transformation part for 2D convolution with winograd algorithm.

Level 3: Additional Math And Transform Operators

This level enables additional math and transform operators.

 tvm.relay.nn.leaky_relu This operator takes data as input and does Leaky version of a Rectified Linear Unit. tvm.relay.nn.prelu This operator takes data as input and does Leaky version of a Rectified Linear Unit. tvm.relay.reshape Reshapes the input array. tvm.relay.reshape_like Reshapes the input array by the size of another array. tvm.relay.copy Copy a tensor. tvm.relay.transpose Permutes the dimensions of an array. tvm.relay.squeeze Squeeze axes in the array. tvm.relay.floor Compute element-wise floor of data. tvm.relay.ceil Compute element-wise ceil of data. tvm.relay.sign Compute element-wise absolute of data. tvm.relay.trunc Compute element-wise trunc of data. tvm.relay.clip Clip the elements in a between a_min and a_max. tvm.relay.round Compute element-wise round of data. tvm.relay.abs Compute element-wise absolute of data. tvm.relay.negative Compute element-wise negative of data. tvm.relay.take Take elements from an array along an axis. tvm.relay.zeros Fill array with zeros. tvm.relay.zeros_like Returns an array of zeros, with same type and shape as the input. tvm.relay.ones Fill array with ones. tvm.relay.ones_like Returns an array of ones, with same type and shape as the input. tvm.relay.gather_nd Gather elements or slices from data and store to a tensor whose shape is defined by indices. tvm.relay.full Fill array with scalar value. tvm.relay.full_like Return a scalar value array with the same shape and type as the input array. tvm.relay.cast Cast input tensor to data type. tvm.relay.split Split input tensor along axis by sections or indices. tvm.relay.arange Return evenly spaced values within a given interval. tvm.relay.stack Join a sequence of arrays along a new axis. tvm.relay.repeat Repeats elements of an array. tvm.relay.tile Repeats the whole array multiple times. tvm.relay.reverse Reverses the order of elements along given axis while preserving array shape.

 tvm.relay.right_shift Right shift with numpy-style broadcasting. tvm.relay.left_shift Left shift with numpy-style broadcasting. tvm.relay.equal Broadcasted elementwise test for (lhs == rhs). tvm.relay.not_equal Broadcasted elementwise test for (lhs != rhs). tvm.relay.greater Broadcasted elementwise test for (lhs > rhs). tvm.relay.greater_equal Broadcasted elementwise test for (lhs >= rhs). tvm.relay.less Broadcasted elementwise test for (lhs < rhs). tvm.relay.less_equal Broadcasted elementwise test for (lhs <= rhs). tvm.relay.all Computes the logical AND of boolean array elements over given axes. tvm.relay.logical_and logical AND with numpy-style broadcasting. tvm.relay.logical_or logical OR with numpy-style broadcasting. tvm.relay.logical_not Compute element-wise logical not of data. tvm.relay.maximum Maximum with numpy-style broadcasting. tvm.relay.minimum Minimum with numpy-style broadcasting. tvm.relay.power Power with numpy-style broadcasting. tvm.relay.where Selecting elements from either x or y depending on the value of the condition. tvm.relay.argmax Returns the indices of the maximum values along an axis. tvm.relay.argmin Returns the indices of the minimum values along an axis. tvm.relay.sum Computes the sum of array elements over given axes. tvm.relay.max Computes the max of array elements over given axes. tvm.relay.min Computes the min of array elements over given axes. tvm.relay.mean Computes the mean of array elements over given axes. tvm.relay.prod Computes the products of array elements over given axes. tvm.relay.strided_slice Strided slice of an array. tvm.relay.broadcast_to Return a scalar value array with the same type, broadcast to the provided shape.

Level 5: Vision/Image Operators

 tvm.relay.image.resize Image resize operator. tvm.relay.vision.multibox_prior Generate prior(anchor) boxes from data, sizes and ratios. tvm.relay.vision.multibox_transform_loc Location transformation for multibox detection tvm.relay.vision.nms Non-maximum suppression operations. tvm.relay.vision.yolo_reorg Yolo reorg operation used in darknet models.

Level 6: Algorithm Operators

 tvm.relay.argsort Performs sorting along the given axis and returns an array of indicies having same shape as an input array that index data in sorted order. tvm.relay.topk Get the top k elements in an input tensor along the given axis.

Level 10: Temporary Operators

This level support backpropagation of broadcast operators. It is temporary.

 tvm.relay.broadcast_to_like Return a scalar value array with the same shape and type as the input array. tvm.relay.collapse_sum_like Return a scalar value array with the same shape and type as the input array. tvm.relay.slice_like Slice the first input with respect to the second input. tvm.relay.shape_of Get shape of a tensor. tvm.relay.layout_transform Transform the layout of a tensor tvm.relay.device_copy Copy data from the source device to the destination device. tvm.relay.annotation.on_device Annotate an expression with a certain device type. tvm.relay.reverse_reshape Reshapes the input array where the special values are inferred from right to left. tvm.relay.nn.batch_matmul Computes batch matrix multiplication of x and y when x and y are data in batch. tvm.relay.contrib.adaptive_max_pool2d 2D adaptive max pooling operator. tvm.relay.contrib.adaptive_avg_pool2d 2D adaptive average pooling operator.

## Level 1 Definitions¶

tvm.relay.log(data)

Compute elementwise log of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.sqrt(data)

Compute elementwise sqrt of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.rsqrt(data)

Compute elementwise rsqrt of data.

$1/sqrt(x)$
Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.exp(data)

Compute elementwise exp of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.sigmoid(data)

Compute elementwise sigmoid of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.add(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr

Examples

x = relay.Var("a") # shape is [2, 3]
y = relay.Var("b") # shape is [2, 1]
z = relay.add(x, y)  # result shape is [2, 3]

tvm.relay.subtract(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.multiply(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.divide(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.mod(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.tanh(data)

Compute element-wise tanh of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.concatenate(data, axis)

Concatenate the input tensors along the given axis.

Parameters: data (Union(List[relay.Expr], Tuple[relay.Expr])) – A list of tensors. axis (int) – The axis along which the tensors are concatenated. result – The concatenated tensor. relay.Expr
tvm.relay.expand_dims(data, axis, num_newaxis=1)

Insert num_newaxis axises at the position given by axis.

Parameters: data (relay.Expr) – The input data to the operator. axis (int) – The axis at which the input array is expanded. Should lie in range [-data.ndim - 1, data.ndim]. If axis < 0, it is the first axis inserted; If axis >= 0, it is the last axis inserted in Python’s negative indexing. num_newaxis (int) – Number of axes to be inserted. Should be >= 0. result – The reshaped result. relay.Expr
tvm.relay.nn.softmax(data, axis=-1)

Computes softmax.

$\text{softmax}(x)_i = \frac{exp(x_i)}{\sum_j exp(x_j)}$

Note

This operator can be optimized away for inference.

Parameters: data (tvm.relay.Expr) – The input data to the operator. axis (int, optional) – The axis to sum over when computing softmax result – The computed result. tvm.relay.Expr
tvm.relay.nn.log_softmax(data, axis=-1)

Computes log softmax.

$\text{log_softmax}(x)_i = \log \frac{exp(x_i)}{\sum_j exp(x_j)}$

Note

This operator can be optimized away for inference.

Parameters: data (tvm.relay.Expr) – The input data to the operator. axis (int) – The axis to sum over when computing softmax result – The computed result. tvm.relay.Expr
tvm.relay.nn.relu(data)

Rectified linear unit.

$out = max(x, 0)$
Parameters: data (tvm.relay.Expr) – The input data result – The computed result. tvm.relay.Expr
tvm.relay.nn.dropout(data, rate=0.5)

Applies the dropout operation to the input array.

During training, each element of the input is set to zero with probability p. The whole array is rescaled by 1/(1-p) to keep the expected sum of the input unchanged.

Parameters: data (tvm.relay.Expr) – The input data to the operator. rate (float, optional (default=0.5)) – The probability for an element to be reset to 0. result – The result of dropout tvm.relay.Expr
tvm.relay.nn.batch_norm(data, gamma, beta, moving_mean, moving_var, axis=1, epsilon=1e-05, center=True, scale=True)

Batch normalization layer (Ioffe and Szegedy, 2014). Normalizes the input at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.

$\begin{split}data\_mean[i] = mean(data[:,i,:,...]) \\ data\_var[i] = var(data[:,i,:,...])\end{split}$

Then compute the normalized output, which has the same shape as input, as following:

$out[:,i,:,...] = \frac{data[:,i,:,...] - data\_mean[i]}{\sqrt{data\_var[i]+\epsilon}} * gamma[i] + beta[i]$

Both mean and var returns a scalar by treating the input as a vector.

Assume the input has size k on axis 1, then both gamma and beta have shape (k,).

Besides the inputs and the outputs, this operator accepts two auxiliary states, moving_mean and moving_var, which are k-length vectors. They are global statistics for the whole dataset, which are updated by:

moving_mean = moving_mean * momentum + data_mean * (1 - momentum) moving_var = moving_var * momentum + data_var * (1 - momentum)

The parameter axis specifies which axis of the input shape denotes the ‘channel’ (separately normalized groups). The default is 1. Specifying -1 sets the channel axis to be the last item in the input shape.

Note

This operator can be optimized away for inference.

Parameters: data (tvm.relay.Expr) – Input to which batch_norm will be applied. gamma (tvm.relay.Expr) – The gamma scale factor. beta (tvm.relay.Expr) – The beta offset factor. moving_mean (tvm.relay.Expr) – Running mean of input, moving_var (tvm.relay.Expr) – Running variance of input. axis (int, optional, default=1) – Specify along which shape axis the channel is specified. epsilon (double, optional, default=1e-5) – Small float added to variance to avoid diving by zero. center (boolean, optional, default=True) – If True, add offset of beta to normalized tensor, If False, beta is ignored. scale (boolean, optional, default=True) – If true, multiply by gamma. If False, gamma is not used. When the next layer is piecewise linear (also e.g. nn.relu), this can be disabled since the scaling will be done by the next layer. result – Tuple of normed data (same shape as input), new running mean (k-length vector), and new running variance (k-length vector) relay.Tuple([tvm.relay.Expr, tvm.relay.Expr, tvm.relay.Expr])
tvm.relay.nn.bias_add(data, bias, axis=1)

Add 1D bias to the axis of data. This function is a special case of add which allows inference of shape of the bias from data.

Parameters: data (tvm.relay.Expr) – The input data to the operator. bias (tvm.relay.Expr) – The bias to be added. axis (int, optional) – The axis to add the bias. result – The final result. tvm.relay.Expr

## Level 2 Definitions¶

tvm.relay.nn.conv2d(data, weight, strides=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, channels=None, kernel_size=None, data_layout='NCHW', kernel_layout='OIHW', out_layout='', out_dtype='')

2D convolution.

This operator takes the weight as the convolution kernel and convolves it with data to produce an output.

In the default case, where the data_layout is NCHW and kernel_layout is OIHW, conv2d takes in a data Tensor with shape (batch_size, in_channels, height, width), and a weight Tensor with shape (channels, in_channels, kernel_size[0], kernel_size[1]) to produce an output Tensor with the following rule:

$\mbox{out}[b, c, y, x] = \sum_{dy, dx, k} \mbox{data}[b, k, \mbox{strides}[0] * y + dy, \mbox{strides}[1] * x + dx] * \mbox{weight}[c, k, dy, dx]$

Padding and dilation are applied to data and weight respectively before the computation. This operator accepts data layout specification. Semantically, the operator will convert the layout to the canonical layout (NCHW for data and OIHW for weight), perform the computation, then convert to the out_layout.

Parameters: data (tvm.relay.Expr) – The input data to the operator. weight (tvm.relay.Expr) – The weight expressions. strides (tuple of int, optional) – The strides of convolution. padding (tuple of int, optional) – The padding of convolution on both sides of inputs before convolution. dilation (tuple of int, optional) – Specifies the dilation rate to be used for dilated convolution. groups (int, optional) – Number of groups for grouped convolution. channels (int, optional) – Number of output channels of this convolution. kernel_size (tuple of int, optional) – The spatial of the convolution kernel. data_layout (str, optional) – Layout of the input. kernel_layout (str, optional) – Layout of the weight. out_layout (str, optional) – Layout of the output, by default, out_layout is the same as data_layout out_dtype (str, optional) – Specifies the output data type for mixed precision conv2d. result – The computed result. tvm.relay.Expr
tvm.relay.nn.conv2d_transpose(data, weight, strides=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, channels=None, kernel_size=None, data_layout='NCHW', kernel_layout='OIHW', output_padding=(0, 0), out_dtype='')

Two dimensional transposed convolution operator.

Parameters: data (tvm.relay.Expr) – The input data to the operator. weight (tvm.relay.Expr) – The weight expressions. strides (Tuple[int], optional) – The strides of convolution. padding (Tuple[int], optional) – The padding of convolution on both sides of inputs. dilation (Tuple[int], optional) – Specifies the dilation rate to be used for dilated convolution. groups (int, optional) – Number of groups for grouped convolution. data_layout (str, optional) – Layout of the input. kernel_layout (str, optional) – Layout of the weight. output_padding (Tuple[int], optional) – Additional zero-padding to be added to one side of the output. out_dtype (str, optional) – Specifies the output data type for mixed precision conv2d. result – The computed result. tvm.relay.Expr
tvm.relay.nn.dense(data, weight, units=None, out_dtype='')

Dense operator. Applies a linear transformation



Y = X * W

Parameters: data (tvm.relay.Expr) – The input data to the operator. weight (tvm.relay.Expr) – The weight expressions. units (int, optional) – Number of hidden units of the dense transformation. out_dtype (str, optional) – Specifies the output data type for mixed precision dense. result – The computed result. tvm.relay.Expr
tvm.relay.nn.max_pool2d(data, pool_size=(1, 1), strides=(1, 1), padding=(0, 0), layout='NCHW', ceil_mode=False)

2D maximum pooling operator.

This operator takes data as input and does 2D max value calculation with in pool_size sized window by striding defined by stride

In the default case, where the data_layout is NCHW a data Tensor with shape (batch_size, in_channels, height, width), to produce an output Tensor with the following rule:

with data of shape (b, c, h, w) and pool_size (kh, kw)

$\mbox{out}(b, c, y, x) = \max_{m=0, \ldots, kh-1} \max_{n=0, \ldots, kw-1} \mbox{data}(b, c, \mbox{stride}[0] * y + m, \mbox{stride}[1] * x + n)$

Padding is applied to data before the computation. ceil_mode is used to take ceil or floor while computing out shape. This operator accepts data layout specification.

Parameters: data (tvm.relay.Expr) – The input data to the operator. strides (tuple of int, optional) – The strides of pooling. padding (tuple of int, optional) – The padding for pooling. layout (str, optional) – Layout of the input. ceil_mode (bool, optional) – To enable or disable ceil while pooling. result – The computed result. tvm.relay.Expr
tvm.relay.nn.avg_pool2d(data, pool_size=(1, 1), strides=(1, 1), padding=(0, 0), layout='NCHW', ceil_mode=False, count_include_pad=False)

2D average pooling operator.

This operator takes data as input and does 2D average value calculation with in pool_size sized window by striding defined by stride

In the default case, where the data_layout is NCHW a data Tensor with shape (batch_size, in_channels, height, width), to produce an output Tensor with the following rule:

with data of shape (b, c, h, w), pool_size (kh, kw)

$\mbox{out}(b, c, y, x) = \frac{1}{kh * kw} \sum_{m=0}^{kh-1} \sum_{n=0}^{kw-1} \mbox{data}(b, c, \mbox{stride}[0] * y + m, \mbox{stride}[1] * x + n)$

Padding is applied to data before the computation. ceil_mode is used to take ceil or floor while computing out shape. count_include_pad indicates including or excluding padded input values in computation. This operator accepts data layout specification.

Parameters: data (tvm.relay.Expr) – The input data to the operator. strides (tuple of int, optional) – The strides of pooling. padding (tuple of int, optional) – The padding for pooling. layout (str, optional) – Layout of the input. ceil_mode (bool, optional) – To enable or disable ceil while pooling. count_include_pad (bool, optional) – To include padding to compute the average. result – The computed result. tvm.relay.Expr
tvm.relay.nn.global_max_pool2d(data, layout='NCHW')

2D global maximum pooling operator.

This operator takes data as input and does 2D max value calculation across each window represented by WxH.

In the default case, where the data_layout is NCHW a data Tensor with shape (batch_size, in_channels, height, width), to produce an output Tensor with the following rule:

with data of shape (b, c, h, w)

$\mbox{out}(b, c, 1, 1) = \max_{m=0, \ldots, h} \max_{n=0, \ldots, w} \mbox{data}(b, c, m, n)$
Parameters: data (tvm.relay.Expr) – The input data to the operator. layout (str, optional) – Layout of the input. result – The computed result. tvm.relay.Expr
tvm.relay.nn.global_avg_pool2d(data, layout='NCHW')

2D global average pooling operator.

This operator takes data as input and does 2D average value calculation across each window represented by WxH.

In the default case, where the data_layout is NCHW a data Tensor with shape (batch_size, in_channels, height, width), to produce an output Tensor with the following rule:

with data of shape (b, c, h, w)

$\mbox{out}(b, c, 1, 1) = \frac{1}{h * w} \sum_{m=0}^{h-1} \sum_{n=0}^{w-1} \mbox{data}(b, c, m, n)$
Parameters: data (tvm.relay.Expr) – The input data to the operator. layout (str, optional) – Layout of the input. result – The computed result. tvm.relay.Expr
tvm.relay.nn.upsampling(data, scale=1, layout='NCHW', method='NEAREST_NEIGHBOR')

Upsampling.

This operator takes data as input and does 2D scaling to the given scale factor. In the default case, where the data_layout is NCHW with data of shape (n, c, h, w) out will have a shape (n, c, h*scale, w*scale)

method indicates the algorithm to be used while calculating the out value and method can be one of (“BILINEAR”, “NEAREST_NEIGHBOR”)

Parameters: data (tvm.relay.Expr) – The input data to the operator. scale (tvm.relay.Expr) – The scale factor for upsampling. layout (str, optional) – Layout of the input. method (str, optional) – Scale method to used [NEAREST_NEIGHBOR, BILINEAR]. result – The computed result. tvm.relay.Expr
tvm.relay.nn.batch_flatten(data)

BatchFlatten.

This operator flattens all the dimensions except for the batch dimension. which results a 2D output.

For data with shape (d1, d2, ..., dk) batch_flatten(data) returns reshaped output of shape (d1, d2*...*dk).

Parameters: data (tvm.relay.Expr) – The input data to the operator. result – The Flattened result. tvm.relay.Expr
tvm.relay.nn.pad(data, pad_width, pad_value=0.0)

This operator takes in a tensor and pads each axis by the specified widths using the specified value.

Parameters: data (tvm.relay.Expr) – The input data to the operator pad_width (tuple of >, required) – Number of values padded to the edges of each axis, in the format of ((before_1, after_1), …, (before_N, after_N)) pad_value (float, optional, default=0.0) – The value used for padding result – The computed result. tvm.relay.Expr
tvm.relay.nn.lrn(data, size=5, axis=1, bias=2, alpha=1e-05, beta=0.75)

This operator takes data as input and does local response normalization.

Normalize the input in a local region across or within feature maps. Each input value is divided by (data / (bias + (alpha * sum_data ^2 /size))^beta) where n is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).

$(data / (bias + (alpha * sum_data ^2 /size))^beta)$
Parameters: data (tvm.relay.Expr) – The input data to the operator. size (int, optional) – The size of the local region to be considered for normalization. axis (int, optional) – Input data layout channel axis. Default value is 1 for NCHW format bias (float, optional) – The offset parameter to avoid dividing by 0. alpha (float, optional) – The scaling parameter. beta (float, optional) – The exponent parameter. result – The computed result. tvm.relay.Expr
tvm.relay.nn.l2_normalize(data, eps, axis=None)

Perform L2 normalization on the input data

$y(i, j) = x(i, j) / sqrt(max(sum(x^2), eps))$
Parameters: data (tvm.relay.Expr) – The input data to the operator. eps (float) – epsilon value axis (list of int, optional) – axis over the normalization applied result – The computed result. tvm.relay.Expr
tvm.relay.nn.contrib_conv2d_winograd_without_weight_transform(data, weight, tile_size, strides=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, channels=None, kernel_size=None, data_layout='NCHW', kernel_layout='OIHW', out_layout='', out_dtype='')

The basic parameters are the same as the ones in vanilla conv2d. It assumes the weight is pre-transformed by nn.contrib_conv2d_winograd_weight_transform

Parameters: data (tvm.relay.Expr) – The input data to the operator. weight (tvm.relay.Expr) – The weight expressions. tile_size (int) – The Tile size of winograd. E.g. 2 for F(2x2, 3x3) and 4 for F(4x4, 3x3) strides (tuple of int, optional) – The strides of convolution. padding (tuple of int, optional) – The padding of convolution on both sides of inputs before convolution. dilation (tuple of int, optional) – Specifies the dilation rate to be used for dilated convolution. groups (int, optional) – Number of groups for grouped convolution. channels (int, optional) – Number of output channels of this convolution. kernel_size (tuple of int, optional) – The spatial of the convolution kernel. data_layout (str, optional) – Layout of the input. kernel_layout (str, optional) – Layout of the weight. out_layout (str, optional) – Layout of the output, by default, out_layout is the same as data_layout out_dtype (str, optional) – Specifies the output data type for mixed precision conv2d. result – The computed result. tvm.relay.Expr
tvm.relay.nn.contrib_conv2d_winograd_weight_transform(weight, tile_size)

Weight Transformation part for 2D convolution with winograd algorithm.

We separate this as a single op to enable pre-compute for inference. Use this together with nn.contrib_conv2d_winograd_without_weight_transform

Parameters: weight (tvm.relay.Expr) – The weight expressions. tile_size (int) – The Tile size of winograd. E.g. 2 for F(2x2, 3x3) and 4 for F(4x4, 3x3) result – The computed result. tvm.relay.Expr

## Level 3 Definitions¶

tvm.relay.nn.leaky_relu(data, alpha)

This operator takes data as input and does Leaky version of a Rectified Linear Unit.

$y = x > 0 ? x : alpha * x$
Parameters: data (tvm.relay.Expr) – The input data to the operator. alpha (float) – Slope coefficient for the negative half axis. result – The computed result. tvm.relay.Expr
tvm.relay.nn.prelu(data, alpha, axis=1)

This operator takes data as input and does Leaky version of a Rectified Linear Unit.

$y = x > 0 ? x : alpha * x$
Parameters: data (tvm.relay.Expr) – The input data to the operator. alpha (tvm.relay.Expr) – Slope coefficient for the negative half axis. axis (int, optional) – Specify which shape axis the channel is specified. result – The computed result. tvm.relay.Expr
tvm.relay.reshape(data, newshape)

Reshapes the input array.

Example:

To give user more convenience in without doing manual shape inference, some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}. The significance of each is explained below:

• 0 copy this dimension from the input to the output shape.

Example:

- data.shape = (2,3,4), newshape = (4,0,2), result.shape = (4,3,2)
- data.shape = (2,3,4), newshape = (2,0,0), result.shape = (2,3,4)

• -1 infers the dimension of the output shape by using the remainder of the input dimensions

keeping the size of the new array same as that of the input array. At most one dimension of shape can be -1.

Example:

- data.shape = (2,3,4), newshape = (6,1,-1), result.shape = (6,1,4)
- data.shape = (2,3,4), newshape = (3,-1,8), result.shape = (3,1,8)
- data.shape = (2,3,4), newshape = (-1,), result.shape = (24,)

• -2 copy all/remainder of the input dimensions to the output shape.

Example:

- data.shape = (2,3,4), newshape = (-2,), result.shape = (2,3,4)
- data.shape = (2,3,4), newshape = (2,-2), result.shape = (2,3,4)
- data.shape = (2,3,4), newshape = (-2,1,1), result.shape = (2,3,4,1,1)

• -3 use the product of two consecutive dimensions of the input shape

as the output dimension.

Example:

- data.shape = (2,3,4), newshape = (-3,4), result.shape = (6,4)
- data.shape = (2,3,4,5), newshape = (-3,-3), result.shape = (6,20)
- data.shape = (2,3,4), newshape = (0,-3), result.shape = (2,12)
- data.shape = (2,3,4), newshape = (-3,-2), result.shape = (6,4)

• -4 split one dimension of the input into two dimensions passed subsequent

to -4 in shape (can contain -1).

Example:

- data.shape = (2,3,4), newshape = (-4,1,2,-2), result.shape = (1,2,3,4)
- data.shape = (2,3,4), newshape = (2,-4,-1,3,-2), result.shape = (2,1,3,4)

Parameters: data (relay.Expr) – The input data to the operator. newshape (Union[int, Tuple[int], List[int]]) – The new shape. Should be compatible with the original shape. result – The reshaped result. relay.Expr
tvm.relay.reshape_like(data, shape_like)

Reshapes the input array by the size of another array. For an input array with shape (d1, d2, ..., dk), reshape_like operation reshapes the input array into an output array with the same shape as the second input array.

Note

Sizes for both array should be compatible.

Parameters: data (relay.Expr) – The input data to the operator. shape_like (tuple of int) – The new shape. Should be compatible with the original shape. ret – The computed result. relay.Expr
tvm.relay.copy(data)

Copy a tensor.

Parameters: data (relay.Expr) – The tensor to be copied. result – The copied result. relay.Expr
tvm.relay.transpose(data, axes=None)

Permutes the dimensions of an array.

Parameters: data (relay.Expr) – The input data to the operator. axes (None or List[int]) – The target axes order, reverse order if not specified. result – The transposed result. relay.Expr
tvm.relay.squeeze(data, axis=None)

Squeeze axes in the array.

Parameters: data (tvm.relay.Expr) – The input data to the operator. axis (None or List[int]) – The set of axes to remove. If axis = None, remove all axis of dimensions 1. If any specified axis has dimension that does not equal 1, it is an error. result – The squeezed result. tvm.relay.Expr
tvm.relay.floor(data)

Compute element-wise floor of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.ceil(data)

Compute element-wise ceil of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.sign(data)

Compute element-wise absolute of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.trunc(data)

Compute element-wise trunc of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.clip(a, a_min, a_max)

Clip the elements in a between a_min and a_max. a_min and a_max are cast to a’s dtype.

Parameters: a (relay.Expr) – The input tensor. a_min (float) – The clip minimum. a_max (float) – The clip maximum. result – a with elements clipped between a_min and a_max. relay.Expr

Examples

tvm.relay.round(data)

Compute element-wise round of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.abs(data)

Compute element-wise absolute of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.negative(data)

Compute element-wise negative of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.take(data, indices, axis=None, mode='clip')

Take elements from an array along an axis.

Parameters: data (relay.Expr) – The source array. indices (rely.Expr) – The indices of the values to extract. axis (int, optional) – The axis over which to select values. By default, the flattened input array is used. mode (str, optional) – Specifies how out-of-bound indices will behave [clip, wrap, fast]. clip: clip to the range (default). wrap: wrap around the indices. fast: no clip or wrap around (user must make sure indices are in-bound). ret – The computed result. relay.Expr
tvm.relay.zeros(shape, dtype)

Fill array with zeros.

Parameters: shape (tuple of int) – The shape of the target. dtype (data type) – The data type of the target. result – The resulting tensor. relay.Expr
tvm.relay.zeros_like(data)

Returns an array of zeros, with same type and shape as the input.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.ones(shape, dtype)

Fill array with ones.

Parameters: shape (tuple of int) – The shape of the target. dtype (data type) – The data type of the target. result – The resulting tensor. relay.Expr
tvm.relay.ones_like(data)

Returns an array of ones, with same type and shape as the input.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.gather_nd(data, indices)

Gather elements or slices from data and store to a tensor whose shape is defined by indices.

Parameters: data (relay.Expr) – The input data to the operator. indices (relay.Expr) – The shape of output tensor. ret – The computed result. relay.Expr

Examples

data = [[0, 1], [2, 3]]
indices = [[1, 1, 0], [0, 1, 0]]
relay.gather_nd(data, indices) = [2, 3, 0]

data = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
indices = [[0, 1], [1, 0]]
relay.gather_nd(data, indices) = [[3, 4], [5, 6]]

tvm.relay.full(fill_value, shape=(), dtype='')

Fill array with scalar value.

Parameters: fill_value (relay.Expr) – The value to fill. Must be a scalar. shape (tuple of int) – The shape of the target. dtype (data type, optional (defaults to data type of the fill value)) – The data type of the target. result – The resulting tensor. relay.Expr
tvm.relay.full_like(data, fill_value)

Return a scalar value array with the same shape and type as the input array.

Parameters: data (relay.Expr) – The input tensor. fill_value (relay.Expr) – The scalar value to fill. result – The resulting tensor. relay.Expr
tvm.relay.cast(data, dtype)

Cast input tensor to data type.

Parameters: data (relay.Expr) – The input data to the operator. dtype (str) – The target data type result – The casted result. relay.Expr
tvm.relay.split(data, indices_or_sections, axis=0)

Split input tensor along axis by sections or indices.

If indices_or_sections is an integer, the input will be divided equally along given axis. If such a split is not possible, an error is raised.

If indices_or_sections is a tuple of sorted integers, the entries indicate where along axis the array is split.

Parameters: data (relay.Expr) – The source array. indices_or_sections (int or tuple of int) – Indices or sections to split into. Accepts an int or a tuple axis (int, optional) – The axis over which to split. ret – The computed result. relay.Tuple([relay.Expr, relay.Expr])
tvm.relay.arange(start, stop=None, step=1, dtype='float32')

Return evenly spaced values within a given interval.

Note

Similar to numpy.arange, when only one argument is given, it is used as stop instead of start while start takes default value 0.

Warning: Undefined behavior when dtype is incompatible with start/stop/step. It could lead to different results compared to numpy, MXNet, pytorch, etc.

Parameters: start (tvm.Expr, optional) – Start of interval. The interval includes this value. The default start value is 0. stop (tvm.Expr) – Stop of interval. The interval does not include this value. step (tvm.Expr, optional) – Spacing between values. The default step size is 1. dtype (str, optional) – The target data type. result – The resulting tensor. relay.Expr

Examples

relay.arange(5) = [0, 1, 2, 3, 4]
relay.arange(1, 5) = [1, 2, 3, 4]
relay.arange(1, 5, 1.5) = [1, 2.5, 4]

tvm.relay.stack(data, axis)

Join a sequence of arrays along a new axis.

Parameters: data (Union(List[relay.Expr], Tuple(relay.Expr))) – A list of tensors. axis (int) – The axis in the result array along which the input arrays are stacked. ret – The stacked tensor. relay.Expr
tvm.relay.repeat(data, repeats, axis)

Repeats elements of an array. By default, repeat flattens the input array into 1-D and then repeats the elements.

repeats : int
The number of repetitions for each element.
axis: int
The axis along which to repeat values. The negative numbers are interpreted counting from the backward. By default, use the flattened input array, and return a flat output array.
Returns: ret – The computed result. relay.Expr

Examples

x = [[1, 2], [3, 4]]
relay.repeat(x, repeats=2) = [1., 1., 2., 2., 3., 3., 4., 4.]

relay.repeat(x, repeats=2, axis=1) = [[1., 1., 2., 2.],
[3., 3., 4., 4.]]

tvm.relay.tile(data, reps)

Repeats the whole array multiple times.

Parameters: data (relay.Expr) – The input data to the operator. reps (tuple of int) – The number of times repeating the tensor data.
:param .. note::: Each dim size of reps must be a positive integer. If reps has length d,
the result will have dimension of max(d, data.ndim); If data.ndim < d, data is promoted to be d-dimensional by prepending new axes. If data.ndim >= d, reps is promoted to a.ndim by pre-pending 1’s to it.
Returns: ret – The computed result. relay.Expr

Examples

x = [[1, 2], [3, 4]]
relay.tile(x, reps=(2,3)) = [[1., 2., 1., 2., 1., 2.],
[3., 4., 3., 4., 3., 4.],
[1., 2., 1., 2., 1., 2.],
[3., 4., 3., 4., 3., 4.]]

relay.tile(x, reps=(2,)) = [[1., 2., 1., 2.],
[3., 4., 3., 4.]]

tvm.relay.reverse(data, axis)

Reverses the order of elements along given axis while preserving array shape. By default, repeat flattens the input array into 1-D and then repeats the elements.

Parameters: data (relay.Expr) – The input data to the operator. axis (int) – The axis along which to reverse elements. ret – The computed result. relay.Expr

Examples

x = [[1., 2.], [3., 4.]]
relay.reverse(x, axis=0) = [[3., 4.], [1., 2.]]

relay.reverse(x, axis=1) = [[2., 1.], [4., 3.]]


## Level 4 Definitions¶

tvm.relay.right_shift(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.left_shift(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.equal(lhs, rhs)

Broadcasted elementwise test for (lhs == rhs).

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.not_equal(lhs, rhs)

Broadcasted elementwise test for (lhs != rhs).

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.greater(lhs, rhs)

Broadcasted elementwise test for (lhs > rhs).

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.greater_equal(lhs, rhs)

Broadcasted elementwise test for (lhs >= rhs).

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.less(lhs, rhs)

Broadcasted elementwise test for (lhs < rhs).

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.less_equal(lhs, rhs)

Broadcasted elementwise test for (lhs <= rhs).

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.all(data, axis=None, keepdims=False, exclude=False)

Computes the logical AND of boolean array elements over given axes.

Parameters: data (relay.Expr) – The input boolean tensor axis (None or int or tuple of int) – Axis or axes along which a sum is performed. The default, axis=None, will sum all of the elements of the input array. If axis is negative it counts from the last to the first axis. keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. exclude (bool) – If exclude is true, reduction will be performed on the axes that are NOT in axis instead. result – The computed result. relay.Expr

Examples



data = relay.Constant(tvm.nd.array([[[ True, True, True],
[ True, True, True], [False, True, False]],
[[ True, False, False],
[ True, True, False], [False, True, True]]]))

relay.all(data, axis=1) # [[False, True, False], # [False, False, False]]

relay.all(data, axis=0) # [[ True, False, False], # [ True, True, False], # [False, True, False]]

tvm.relay.logical_and(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.logical_or(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.logical_not(data)

Compute element-wise logical not of data.

Parameters: data (relay.Expr) – The input data result – The computed result. relay.Expr
tvm.relay.maximum(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.minimum(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.power(lhs, rhs)

Parameters: lhs (relay.Expr) – The left hand side input data rhs (relay.Expr) – The right hand side input data result – The computed result. relay.Expr
tvm.relay.where(condition, x, y)

Selecting elements from either x or y depending on the value of the condition.

Note

The shape of condition, x, and y needs to be the same.

Parameters: condition (relay.Expr) – The condition array. The n-th element in y is selected when the n-th value in the condition array is zero. Otherwise, the corresponding element from x will be picked. x (relay.Expr) – The first array to be selected. y (relay.Expr) – The second array to be selected. result – The selected array. relay.Expr

Examples

x = [[1, 2], [3, 4]]
y = [[5, 6], [7, 8]]
condition = [[0, 1], [-1, 0]]
relay.where(conditon, x, y) = [[5, 2], [3, 8]]

condition = [1, 0]
relay.where(conditon, x, y) = [[1, 2], [7, 8]]

tvm.relay.argmax(data, axis=None, keepdims=False, exclude=False)

Returns the indices of the maximum values along an axis.

Parameters: data (relay.Expr) – The input data axis (None or int or tuple of int) – Axis or axes along which a argmax operation is performed. The default, axis=None, will find the indices of the maximum element of the elements of the input array. If axis is negative it counts from the last to the first axis. keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. exclude (bool) – If exclude is true, reduction will be performed on the axes that are NOT in axis instead. result – The computed result. relay.Expr
tvm.relay.argmin(data, axis=None, keepdims=False, exclude=False)

Returns the indices of the minimum values along an axis.

Parameters: data (relay.Expr) – The input data axis (None or int or tuple of int) – Axis or axes along which a argmin operation is performed. The default, axis=None, will find the indices of minimum element all of the elements of the input array. If axis is negative it counts from the last to the first axis. keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. exclude (bool) – If exclude is true, reduction will be performed on the axes that are NOT in axis instead. result – The computed result. relay.Expr
tvm.relay.sum(data, axis=None, keepdims=False, exclude=False)

Computes the sum of array elements over given axes.

Parameters: data (relay.Expr) – The input data axis (None or int or tuple of int) – Axis or axes along which a sum is performed. The default, axis=None, will sum all of the elements of the input array. If axis is negative it counts from the last to the first axis. keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. exclude (bool) – If exclude is true, reduction will be performed on the axes that are NOT in axis instead. result – The computed result. relay.Expr
tvm.relay.max(data, axis=None, keepdims=False, exclude=False)

Computes the max of array elements over given axes.

Parameters: data (relay.Expr) – The input data axis (None or int or tuple of int) – Axis or axes along which the max operation is performed. The default, axis=None, will find the max element from all of the elements of the input array. If axis is negative it counts from the last to the first axis. keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. exclude (bool) – If exclude is true, reduction will be performed on the axes that are NOT in axis instead. result – The computed result. relay.Expr
tvm.relay.min(data, axis=None, keepdims=False, exclude=False)

Computes the min of array elements over given axes.

Parameters: data (relay.Expr) – The input data axis (None or int or tuple of int) – Axis or axes along which a minimum operation is performed. The default, axis=None, will find the minimum element from all of the elements of the input array. If axis is negative it counts from the last to the first axis. keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. exclude (bool) – If exclude is true, reduction will be performed on the axes that are NOT in axis instead. result – The computed result. relay.Expr
tvm.relay.mean(data, axis=None, keepdims=False, exclude=False)

Computes the mean of array elements over given axes.

Parameters: data (relay.Expr) – The input data axis (None or int or tuple of int) – Axis or axes along which a mean operation is performed. The default, axis=None, will find the indices of minimum element all of the elements of the input array. If axis is negative it counts from the last to the first axis. keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. exclude (bool) – If exclude is true, reduction will be performed on the axes that are NOT in axis instead. result – The computed result. relay.Expr
tvm.relay.prod(data, axis=None, keepdims=False, exclude=False)

Computes the products of array elements over given axes.

Parameters: data (relay.Expr) – The input data axis (None or int or tuple of int) – Axis or axes along which a product is performed. The default, axis=None, will find the indices of minimum element all of the elements of the input array. If axis is negative it counts from the last to the first axis. keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. exclude (bool) – If exclude is true, reduction will be performed on the axes that are NOT in axis instead. result – The computed result. relay.Expr
tvm.relay.strided_slice(data, begin, end, strides=None)

Strided slice of an array.

Parameters: data (relay.Expr) – The source array to be sliced. begin (list of int) – The indices to begin with in the slicing. end (list of int) – Indices indicating end of the slice. strides (list of int, optional) – Specifies the stride values, it can be negative in that case, the input tensor will be reversed in that particular axis. ret – The computed result. relay.Expr
tvm.relay.broadcast_to(data, shape)

Return a scalar value array with the same type, broadcast to the provided shape.

Parameters: data (relay.Expr) – The input tensor. shape (shape) – Provide the shape to broadcast to. result – The resulting tensor. relay.Expr

## Level 5 Definitions¶

tvm.relay.image.resize(data, size, layout='NCHW', method='BILINEAR', align_corners=False)

Image resize operator.

This operator takes data as input and does 2D scaling to the given scale factor. In the default case, where the data_layout is NCHW with data of shape (n, c, h, w) out will have a shape (n, c, size[0], size[1])

method indicates the algorithm to be used while calculating ghe out value and method can be one of (“BILINEAR”, “NEAREST_NEIGHBOR”)

Parameters: data (relay.Expr) – The input data to the operator. size (Tuple of Expr) – The out size to which the image will be resized. layout (str, optional) – Layout of the input. method (str, optional) – Scale method to used [NEAREST_NEIGHBOR, BILINEAR]. align_corners (int, optional) – Should be true to preserve the values at the corner pixels result – The resized result. relay.Expr
tvm.relay.vision.multibox_prior(data, sizes=(1.0, ), ratios=(1.0, ), steps=(-1.0, -1.0), offsets=(0.5, 0.5), clip=False)

Generate prior(anchor) boxes from data, sizes and ratios.

Parameters: data (relay.Expr) – The input data tensor. sizes (tuple of float, optional) – Tuple of sizes for anchor boxes. ratios (tuple of float, optional) – Tuple of ratios for anchor boxes. steps (Tuple of float, optional) – Priorbox step across y and x, -1 for auto calculation. offsets (tuple of int, optional) – Priorbox center offsets, y and x respectively. clip (boolean, optional) – Whether to clip out-of-boundary boxes. out – 3-D tensor with shape [1, h_in * w_in * (num_sizes + num_ratios - 1), 4] relay.Expr
tvm.relay.vision.multibox_transform_loc(cls_prob, loc_pred, anchor, clip=True, threshold=0.01, variances=(0.1, 0.1, 0.2, 0.2))

Location transformation for multibox detection

Parameters: cls_prob (tvm.relay.Expr) – Class probabilities. loc_pred (tvm.relay.Expr) – Location regression predictions. anchor (tvm.relay.Expr) – Prior anchor boxes. clip (boolean, optional) – Whether to clip out-of-boundary boxes. threshold (double, optional) – Threshold to be a positive prediction. variances (Tuple of float, optional) – variances to be decoded from box regression output. ret tuple of tvm.relay.Expr
tvm.relay.vision.nms()

Non-maximum suppression operations.

tvm.relay.vision.yolo_reorg(data, stride)

Yolo reorg operation used in darknet models. This layer shuffles the input tensor values based on the stride value. Along with the shuffling, it does the shape transform. If ‘(n, c, h, w)’ is the data shape and ‘s’ is stride, output shape is ‘(n, c*s*s, h/s, w/s)’ Example: data(1, 4, 2, 2) = [[[[ 0 1] [ 2 3]]

[[ 4 5] [ 6 7]] [[ 8 9] [10 11]] [[12 13] [14 15]]]]

stride = 2 ret(1, 16, 1, 1) = [[[[ 0]] [[ 2]] [[ 8]] [[10]]

[[ 1]] [[ 3]] [[ 9]] [[11]] [[ 4]] [[ 6]] [[12]] [[14]] [[ 5]] [[ 7]] [[13]] [[15]]]]

Note: stride=1 has no significance for reorg operation.

Parameters: data (relay.Expr) – The input data tensor. stride (int) – The stride value for reorganisation. ret – The computed result. relay.Expr

## Level 6 Definitions¶

tvm.relay.argsort(data, axis=-1, is_ascend=1, dtype='int32')

Performs sorting along the given axis and returns an array of indicies having same shape as an input array that index data in sorted order.

Parameters: data (relay.Expr) – The input data tensor. valid_count (tvm.Tensor) – The number of valid elements to be sorted. axis (int, optional) – Axis long which to sort the input tensor. is_ascend (boolean, optional) – Whether to sort in ascending or descending order. dtype (string, optional) – The data type of the output indices. out – Tensor with same shape as data. relay.Expr
tvm.relay.topk(data, k=1, axis=-1, ret_type='both', is_ascend=False, dtype='int32')

Get the top k elements in an input tensor along the given axis.

ret_type specifies the return type, can be one of (“both”, “values”, “indices”).

Parameters: data (relay.Expr) – The input data tensor. k (int, optional) – Number of top elements to select. Return all elements if k < 1. axis (int, optional) – Axis long which to sort the input tensor. ret_type (str, optional) – The return type [both, values, indices]. “both”: return both top k data and indices. “values”: return top k data only. “indices”: return top k indices only. is_ascend (boolean, optional) – Whether to sort in ascending or descending order. dtype (string, optional) – The data type of the indices output. out – The computed result. relay.Expr or List[relay.Expr]

## Level 10 Definitions¶

tvm.relay.broadcast_to_like(data, broadcast_type)

Return a scalar value array with the same shape and type as the input array.

Parameters: data (relay.Expr) – The input tensor. broadcast_type (relay.Expr) – Provide the type to broadcast to. result – The resulting tensor. relay.Expr
tvm.relay.collapse_sum_like(data, collapse_type)

Return a scalar value array with the same shape and type as the input array.

Parameters: data (relay.Expr) – The input tensor. collapse_type (relay.Expr) – Provide the type to collapse to. result – The resulting tensor. relay.Expr
tvm.relay.slice_like(data, shape_like, axes=None)

Slice the first input with respect to the second input.

For an input array with shape (d1, d2, ..., dk), slice_like operation slices the the input array corresponding size of second array. By default will slice on all axes.

Parameters: data (tvm.relay.Expr) – The source array. shape_like (tvm.relay.Expr) – The new shape. axes (Optional[Tuple[int]]) – List of axes on which input data will be sliced according to the corresponding size of the second input. By default will slice on all axes. Negative axes mean counting in reverse. result – The computed result. relay.Expr
tvm.relay.shape_of(data, dtype='int32')

Get shape of a tensor.

Parameters: data (tvm.relay.Expr) – The input tensor. dtype (str, optional) – The target data type. result – The shape tensor. tvm.relay.Expr
tvm.relay.layout_transform(data, src_layout, dst_layout)

Transform the layout of a tensor

Parameters: data (relay.Expr) – The source tensor to be transformed src_layout (str) – The source layout. (e.g NCHW) dst_layout (str) – The destination layout. (e.g. NCHW16c) ret – The transformed tensor. relay.Expr
tvm.relay.device_copy(data, src_dev, dst_dev)

Copy data from the source device to the destination device. This operator helps data transferring between difference contexts for heterogeneous execution.

Parameters: data (tvm.relay.Expr) – The tensor to be copied. src_dev (Union[TVMContext, str]) – The source device where the data is copied from. dst_dev (Union[TVMContext, str]) – The destination device where the data is copied to. result – The copied result. tvm.relay.Expr
tvm.relay.annotation.on_device(data, device)

Annotate an expression with a certain device type.

Parameters: data (tvm.relay.Expr) – The expression to be annotated. device (Union[TVMContext, str]) – The device type to annotate. result – The annotated expression. tvm.relay.Expr
tvm.relay.reverse_reshape(data, newshape)

Reshapes the input array where the special values are inferred from right to left.

Example:

The special values have the same semantics as tvm.relay.reshape. The difference is that special values are inferred from right to left. It can be explained in the example below:

- data.shape = (10,5,4), newshape = (-1,0), reshape results in (40,5)
- data.shape = (10,5,4), newshape = (-1,0), reverse_reshape results in (40,5)

Parameters: data (relay.Expr) – The input data to the operator. newshape (Union[int, Tuple[int], List[int]]) – The new shape. Should be compatible with the original shape. result – The reshaped result. relay.Expr
tvm.relay.nn.batch_matmul(x, y)

Computes batch matrix multiplication of x and y when x and y are data in batch.

$\mbox{batch_matmul}(x, y)[i, :, :] = \mbox{matmul}(x[i, :, :], y[i, :, :]^T)$
Parameters: x (tvm.relay.Expr) – The first input. y (tvm.relay.Expr) – The second input. result – The computed result. tvm.relay.Expr
tvm.relay.contrib.adaptive_max_pool2d(data, output_size=None, layout='NCHW')

2D adaptive max pooling operator. This operator is experimental.

This operator takes data as input and does 2D max value calculation across each window represented by WxH.

In the default case, where the data_layout is NCHW a data Tensor with shape (batch_size, in_channels, height, width), to produce an output Tensor with shape (batch_size, in_channels, output_height, output_width).

The pooling kernel and stride sizes are automatically chosen for desired output sizes.

For output_size:

If this argument is not provided, input height and width will be used as output height and width.

If a single integer is provided for output_size, the output size is (N x C x output_size x output_size) for any input (NCHW).

If a tuple of integers (height, width) are provided for output_size, the output size is (N x C x height x width) for any input (NCHW).

Parameters: data (tvm.relay.Expr) – The input data to the operator. output_size (tuple of int. optional) – Output height and width. layout (str, optional) – Layout of the input. result – The computed result. tvm.relay.Expr
tvm.relay.contrib.adaptive_avg_pool2d(data, output_size=None, layout='NCHW')

2D adaptive average pooling operator. This operator is experimental.

This operator takes data as input and does 2D average value calculation across each window represented by WxH.

In the default case, where the data_layout is NCHW a data Tensor with shape (batch_size, in_channels, height, width), to produce an output Tensor with shape (batch_size, in_channels, output_height, output_width).

The pooling kernel and stride sizes are automatically chosen for desired output sizes.

For output_size:

If this argument is not provided, input height and width will be used as output height and width.

If a single integer is provided for output_size, the output size is (N x C x output_size x output_size) for any input (NCHW).

If a tuple of integers (height, width) are provided for output_size, the output size is (N x C x height x width) for any input (NCHW).

Parameters: data (tvm.relay.Expr) – The input data to the operator. output_size (tuple of int. optional) – Output height and width. layout (str, optional) – Layout of the input. result – The computed result. tvm.relay.Expr