# TensorRT Custom Ops¶

## MMCVRoIAlign¶

### Description¶

Perform RoIAlign on output feature, used in bbox_head of most two stage detectors.

### Parameters¶

Type Parameter Description
int output_height height of output roi
int output_width width of output roi
float spatial_scale used to scale the input boxes
int sampling_ratio number of input samples to take for each output sample. 0 means to take samples densely for current models.
str mode pooling mode in each bin. avg or max
int aligned If aligned=0, use the legacy implementation in MMDetection. Else, align the results more perfectly.

### Inputs¶

inputs[0]: T
Input feature map; 4D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the data.
inputs[1]: T
RoIs (Regions of Interest) to pool over; 2-D tensor of shape (num_rois, 5) given as [[batch_index, x1, y1, x2, y2], ...]. The RoIs' coordinates are the coordinate system of inputs[0].

### Outputs¶

outputs[0]: T
RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element output[0][r-1] is a pooled feature map corresponding to the r-th RoI inputs[1][r-1].

### Type Constraints¶

• T:tensor(float32, Linear)

## ScatterND¶

### Description¶

ScatterND takes three inputs data tensor of rank r >= 1, indices tensor of rank q >= 1, and updates tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input data, and then updating its value to values specified by updates at specific index positions specified by indices. Its output shape is the same as the shape of data. Note that indices should not have duplicate entries. That is, two or more updates for the same index-location is not supported.

The output is calculated via the following equation:

  output = np.copy(data)
update_indices = indices.shape[:-1]
for idx in np.ndindex(update_indices):


None

### Inputs¶

inputs[0]: T
Tensor of rank r>=1.
inputs[1]: tensor(int32, Linear)
Tensor of rank q>=1.
inputs[2]: T
Tensor of rank q + r - indices_shape[-1] - 1.

### Outputs¶

outputs[0]: T
Tensor of rank r >= 1.

### Type Constraints¶

• T:tensor(float32, Linear), tensor(int32, Linear)

## NonMaxSuppression¶

### Description¶

Filter out boxes has high IoU overlap with previously selected boxes or low score. Output the indices of valid boxes. Indices of invalid boxes will be filled with -1.

### Parameters¶

Type Parameter Description
int center_point_box 0 - the box data is supplied as [y1, x1, y2, x2], 1-the box data is supplied as [x_center, y_center, width, height].
int max_output_boxes_per_class The maximum number of boxes to be selected per batch per class. Default to 0, number of output boxes equal to number of input boxes.
float iou_threshold The threshold for deciding whether boxes overlap too much with respect to IoU. Value range [0, 1]. Default to 0.
float score_threshold The threshold for deciding when to remove boxes based on score.
int offset 0 or 1, boxes' width or height is (x2 - x1 + offset).

### Inputs¶

inputs[0]: T
Input boxes. 3-D tensor of shape (num_batches, spatial_dimension, 4).
inputs[1]: T
Input scores. 3-D tensor of shape (num_batches, num_classes, spatial_dimension).

### Outputs¶

outputs[0]: tensor(int32, Linear)
Selected indices. 2-D tensor of shape (num_selected_indices, 3) as [[batch_index, class_index, box_index], ...].
num_selected_indices=num_batches* num_classes* min(max_output_boxes_per_class, spatial_dimension).
All invalid indices will be filled with -1.

### Type Constraints¶

• T:tensor(float32, Linear)

## MMCVDeformConv2d¶

### Description¶

Perform Deformable Convolution on input feature, read Deformable Convolutional Network for detail.

### Parameters¶

Type Parameter Description
list of ints stride The stride of the convolving kernel. (sH, sW)
list of ints padding Paddings on both sides of the input. (padH, padW)
list of ints dilation The spacing between kernel elements. (dH, dW)
int deformable_group Groups of deformable offset.
int group Split input into groups. input_channel should be divisible by the number of groups.
int im2col_step DeformableConv2d use im2col to compute convolution. im2col_step is used to split input and offset, reduce memory usage of column.

### Inputs¶

inputs[0]: T
Input feature; 4-D tensor of shape (N, C, inH, inW), where N is the batch size, C is the numbers of channels, inH and inW are the height and width of the data.
inputs[1]: T
Input offset; 4-D tensor of shape (N, deformable_group* 2* kH* kW, outH, outW), where kH and kW is the height and width of weight, outH and outW is the height and width of offset and output.
inputs[2]: T
Input weight; 4-D tensor of shape (output_channel, input_channel, kH, kW).

### Outputs¶

outputs[0]: T
Output feature; 4-D tensor of shape (N, output_channel, outH, outW).

### Type Constraints¶

• T:tensor(float32, Linear)

## grid_sampler¶

### Description¶

Perform sample from input with pixel locations from grid.

### Parameters¶

Type Parameter Description
int interpolation_mode Interpolation mode to calculate output values. (0: bilinear , 1: nearest)
int padding_mode Padding mode for outside grid values. (0: zeros, 1: border, 2: reflection)
int align_corners If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.

### Inputs¶

inputs[0]: T
Input feature; 4-D tensor of shape (N, C, inH, inW), where N is the batch size, C is the numbers of channels, inH and inW are the height and width of the data.
inputs[1]: T
Input offset; 4-D tensor of shape (N, outH, outW, 2), where outH and outW is the height and width of offset and output.

### Outputs¶

outputs[0]: T
Output feature; 4-D tensor of shape (N, C, outH, outW).

### Type Constraints¶

• T:tensor(float32, Linear)

## cummax¶

### Description¶

Returns a namedtuple (values, indices) where values is the cumulative maximum of elements of input in the dimension dim. And indices is the index location of each maximum value found in the dimension dim.

### Parameters¶

Type Parameter Description
int dim The dimension to do the operation over.

### Inputs¶

inputs[0]: T
The input tensor.

### Outputs¶

outputs[0]: T
Output values.
outputs[1]: (int32, Linear)
Output indices.

### Type Constraints¶

• T:tensor(float32, Linear)

## cummin¶

### Description¶

Returns a namedtuple (values, indices) where values is the cumulative minimum of elements of input in the dimension dim. And indices is the index location of each minimum value found in the dimension dim.

### Parameters¶

Type Parameter Description
int dim The dimension to do the operation over.

### Inputs¶

inputs[0]: T
The input tensor.

### Outputs¶

outputs[0]: T
Output values.
outputs[1]: (int32, Linear)
Output indices.

### Type Constraints¶

• T:tensor(float32, Linear)

## MMCVInstanceNormalization¶

### Description¶

Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022.

y = scale * (x - mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel.

### Parameters¶

Type Parameter Description
float epsilon The epsilon value to use to avoid division by zero. Default is 1e-05

### Inputs¶

input: T
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
scale: T
The input 1-dimensional scale tensor of size C.
B: T
The input 1-dimensional bias tensor of size C.

### Outputs¶

output: T
The output tensor of the same shape as input.

### Type Constraints¶

• T:tensor(float32, Linear)

## MMCVModulatedDeformConv2d¶

### Description¶

Perform Modulated Deformable Convolution on input feature, read Deformable ConvNets v2: More Deformable, Better Results for detail.

### Parameters¶

Type Parameter Description
list of ints stride The stride of the convolving kernel. (sH, sW)
list of ints padding Paddings on both sides of the input. (padH, padW)
list of ints dilation The spacing between kernel elements. (dH, dW)
int deformable_group Groups of deformable offset.
int group Split input into groups. input_channel should be divisible by the number of groups.

### Inputs¶

inputs[0]: T
Input feature; 4-D tensor of shape (N, C, inH, inW), where N is the batch size, C is the number of channels, inH and inW are the height and width of the data.
inputs[1]: T
Input offset; 4-D tensor of shape (N, deformable_group* 2* kH* kW, outH, outW), where kH and kW is the height and width of weight, outH and outW is the height and width of offset and output.
inputs[2]: T
Input mask; 4-D tensor of shape (N, deformable_group* kH* kW, outH, outW), where kH and kW is the height and width of weight, outH and outW is the height and width of offset and output.
inputs[3]: T
Input weight; 4-D tensor of shape (output_channel, input_channel, kH, kW).
inputs[4]: T, optional
Input weight; 1-D tensor of shape (output_channel).

### Outputs¶

outputs[0]: T
Output feature; 4-D tensor of shape (N, output_channel, outH, outW).

### Type Constraints¶

• T:tensor(float32, Linear)