TensorRT Ops¶
TRTBatchedNMS¶
Description¶
Batched NMS with a fixed number of output bounding boxes.
Parameters¶
| Type | Parameter | Description |
|---|---|---|
int |
background_label_id |
The label ID for the background class. If there is no background class, set it to -1. |
int |
num_classes |
The number of classes. |
int |
topK |
The number of bounding boxes to be fed into the NMS step. |
int |
keepTopK |
The number of total bounding boxes to be kept per-image after the NMS step. Should be less than or equal to the topK value. |
float |
scoreThreshold |
The scalar threshold for score (low scoring boxes are removed). |
float |
iouThreshold |
The scalar threshold for IoU (new boxes that have high IoU overlap with previously selected boxes are removed). |
int |
isNormalized |
Set to false if the box coordinates are not normalized, meaning they are not in the range [0,1]. Defaults to true. |
int |
clipBoxes |
Forcibly restrict bounding boxes to the normalized range [0,1]. Only applicable if isNormalized is also true. Defaults to true. |
Inputs¶
- inputs[0]: T
- boxes; 4-D tensor of shape (N, num_boxes, num_classes, 4), where N is the batch size; `num_boxes` is the number of boxes; `num_classes` is the number of classes, which could be 1 if the boxes are shared between all classes.
- inputs[1]: T
- scores; 4-D tensor of shape (N, num_boxes, 1, num_classes).
Outputs¶
- outputs[0]: T
- dets; 3-D tensor of shape (N, valid_num_boxes, 5), `valid_num_boxes` is the number of boxes after NMS. For each row `dets[i,j,:] = [x0, y0, x1, y1, score]`
- outputs[1]: tensor(int32, Linear)
- labels; 2-D tensor of shape (N, valid_num_boxes).
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 are 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)
MMCVInstanceNormalization¶
Description¶
Carry 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 are the height and width of weight, outH and outW are 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 are the height and width of weight, outH and outW are 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)
MMCVMultiLevelRoiAlign¶
Description¶
Perform RoIAlign on features from multiple levels. 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. |
list of floats |
featmap_strides |
feature map stride of each level. |
int |
sampling_ratio |
number of input samples to take for each output sample. 0 means to take samples densely for current models. |
float |
roi_scale_factor |
RoIs will be scaled by this factor before RoI Align. |
int |
finest_scale |
Scale threshold of mapping to level 0. Default: 56. |
int |
aligned |
If aligned=0, use the legacy implementation in MMDetection. Else, align the results more perfectly. |
Inputs¶
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)
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):
output[indices[idx]] = updates[idx]
Parameters¶
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)
TRTBatchedRotatedNMS¶
Description¶
Batched rotated NMS with a fixed number of output bounding boxes.
Parameters¶
| Type | Parameter | Description |
|---|---|---|
int |
background_label_id |
The label ID for the background class. If there is no background class, set it to -1. |
int |
num_classes |
The number of classes. |
int |
topK |
The number of bounding boxes to be fed into the NMS step. |
int |
keepTopK |
The number of total bounding boxes to be kept per-image after the NMS step. Should be less than or equal to the topK value. |
float |
scoreThreshold |
The scalar threshold for score (low scoring boxes are removed). |
float |
iouThreshold |
The scalar threshold for IoU (new boxes that have high IoU overlap with previously selected boxes are removed). |
int |
isNormalized |
Set to false if the box coordinates are not normalized, meaning they are not in the range [0,1]. Defaults to true. |
int |
clipBoxes |
Forcibly restrict bounding boxes to the normalized range [0,1]. Only applicable if isNormalized is also true. Defaults to true. |
Inputs¶
- inputs[0]: T
- boxes; 4-D tensor of shape (N, num_boxes, num_classes, 5), where N is the batch size; `num_boxes` is the number of boxes; `num_classes` is the number of classes, which could be 1 if the boxes are shared between all classes.
- inputs[1]: T
- scores; 4-D tensor of shape (N, num_boxes, 1, num_classes).
Outputs¶
- outputs[0]: T
- dets; 3-D tensor of shape (N, valid_num_boxes, 6), `valid_num_boxes` is the number of boxes after NMS. For each row `dets[i,j,:] = [x0, y0, width, height, theta, score]`
- outputs[1]: tensor(int32, Linear)
- labels; 2-D tensor of shape (N, valid_num_boxes).
Type Constraints¶
T:tensor(float32, Linear)
GridPriorsTRT¶
Description¶
Generate the anchors for object detection task.
Parameters¶
| Type | Parameter | Description |
|---|---|---|
int |
stride_w |
The stride of the feature width. |
int |
stride_h |
The stride of the feature height. |
Inputs¶
- inputs[0]: T
- The base anchors; 2-D tensor with shape [num_base_anchor, 4].
- inputs[1]: TAny
- height provider; 1-D tensor with shape [featmap_height]. The data will never been used.
- inputs[2]: TAny
- width provider; 1-D tensor with shape [featmap_width]. The data will never been used.
Outputs¶
- outputs[0]: T
- output anchors; 2-D tensor of shape (num_base_anchor*featmap_height*featmap_widht, 4).
Type Constraints¶
T:tensor(float32, Linear)
TAny: Any
ScaledDotProductAttentionTRT¶
Description¶
Dot product attention used to support multihead attention, read Attention Is All You Need for more detail.
Parameters¶
None
Inputs¶
- inputs[0]: T
- query; 3-D tensor with shape [batch_size, sequence_length, embedding_size].
- inputs[1]: T
- key; 3-D tensor with shape [batch_size, sequence_length, embedding_size].
- inputs[2]: T
- value; 3-D tensor with shape [batch_size, sequence_length, embedding_size].
- inputs[3]: T
- mask; 2-D/3-D tensor with shape [sequence_length, sequence_length] or [batch_size, sequence_length, sequence_length]. optional.
Outputs¶
- outputs[0]: T
- 3-D tensor of shape [batch_size, sequence_length, embedding_size]. `softmax(q@k.T)@v`
- outputs[1]: T
- 3-D tensor of shape [batch_size, sequence_length, sequence_length]. `softmax(q@k.T)`
Type Constraints¶
T:tensor(float32, Linear)
GatherTopk¶
Description¶
TensorRT 8.2~8.4 would give unexpected result for multi-index gather.
data[batch_index, bbox_index, ...]
Read this for more details.
Parameters¶
None
Inputs¶
- inputs[0]: T
- Tensor to be gathered, with shape (A0, ..., An, G0, C0, ...).
- inputs[1]: tensor(int32, Linear)
- Tensor of index. with shape (A0, ..., An, G1)
Outputs¶
- outputs[0]: T
- Tensor of output. With shape (A0, ..., An, G1, C0, ...)
Type Constraints¶
T:tensor(float32, Linear), tensor(int32, Linear)
MMCVMultiScaleDeformableAttention¶
Description¶
Perform attention computation over a small set of key sampling points around a reference point rather than looking over all possible spatial locations. Read Deformable DETR: Deformable Transformers for End-to-End Object Detection for detail.
Parameters¶
None
Inputs¶
- inputs[0]: T
- Input feature; 4-D tensor of shape (N, S, M, D), where N is the batch size, S is the length of feature maps, M is the number of attention heads, and D is hidden_dim.
- inputs[1]: T
- Input offset; 2-D tensor of shape (L, 2), L is the number of feature maps, `2` is shape of feature maps.
- inputs[2]: T
- Input mask; 1-D tensor of shape (L, ), this tensor is used to find the sampling locations for different feature levels as the input feature tensors are flattened.
- inputs[3]: T
- Input weight; 6-D tensor of shape (N, Lq, M, L, P, 2). Lq is the length of feature maps(encoder)/length of queries(decoder), P is the number of points
- inputs[4]: T, optional
- Input weight; 5-D tensor of shape (N, Lq, M, L, P).
Outputs¶
- outputs[0]: T
- Output feature; 3-D tensor of shape (N, Lq, M*D).
Type Constraints¶
T:tensor(float32, Linear)