## TensorRT Ops - [TensorRT Ops](#tensorrt-ops) - [TRTBatchedNMS](#trtbatchednms) - [Description](#description) - [Parameters](#parameters) - [Inputs](#inputs) - [Outputs](#outputs) - [Type Constraints](#type-constraints) - [grid_sampler](#grid_sampler) - [Description](#description-1) - [Parameters](#parameters-1) - [Inputs](#inputs-1) - [Outputs](#outputs-1) - [Type Constraints](#type-constraints-1) - [MMCVInstanceNormalization](#mmcvinstancenormalization) - [Description](#description-2) - [Parameters](#parameters-2) - [Inputs](#inputs-2) - [Outputs](#outputs-2) - [Type Constraints](#type-constraints-2) - [MMCVModulatedDeformConv2d](#mmcvmodulateddeformconv2d) - [Description](#description-3) - [Parameters](#parameters-3) - [Inputs](#inputs-3) - [Outputs](#outputs-3) - [Type Constraints](#type-constraints-3) - [MMCVMultiLevelRoiAlign](#mmcvmultilevelroialign) - [Description](#description-4) - [Parameters](#parameters-4) - [Inputs](#inputs-4) - [Outputs](#outputs-4) - [Type Constraints](#type-constraints-4) - [MMCVRoIAlign](#mmcvroialign) - [Description](#description-5) - [Parameters](#parameters-5) - [Inputs](#inputs-5) - [Outputs](#outputs-5) - [Type Constraints](#type-constraints-5) - [ScatterND](#scatternd) - [Description](#description-6) - [Parameters](#parameters-6) - [Inputs](#inputs-6) - [Outputs](#outputs-6) - [Type Constraints](#type-constraints-6) - [TRTBatchedRotatedNMS](#trtbatchedrotatednms) - [Description](#description-7) - [Parameters](#parameters-7) - [Inputs](#inputs-7) - [Outputs](#outputs-7) - [Type Constraints](#type-constraints-7) - [GridPriorsTRT](#gridpriorstrt) - [Description](#description-8) - [Parameters](#parameters-8) - [Inputs](#inputs-8) - [Outputs](#outputs-8) - [Type Constraints](#type-constraints-8) - [ScaledDotProductAttentionTRT](#scaleddotproductattentiontrt) - [Description](#description-9) - [Parameters](#parameters-9) - [Inputs](#inputs-9) - [Outputs](#outputs-9) - [Type Constraints](#type-constraints-9) - [GatherTopk](#gathertopk) - [Description](#description-10) - [Parameters](#parameters-10) - [Inputs](#inputs-10) - [Outputs](#outputs-10) - [Type Constraints](#type-constraints-10) ### 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](https://arxiv.org/abs/1811.11168?from=timeline) 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
inputs[0]: T
RoIs (Regions of Interest) to pool over; 2-D tensor of shape (num_rois, 5) given as [[batch_index, x1, y1, x2, y2], ...].
inputs[1~]: 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.
#### 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: ```python 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](https://arxiv.org/abs/1706.03762?context=cs) 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. ```python data[batch_index, bbox_index, ...] ``` Read [this](https://github.com/NVIDIA/TensorRT/issues/2299) 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)