Perform sample from input with pixel locations from grid.
| 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. |
- input: 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.
- grid: 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.
- output: T
- Output feature; 4-D tensor of shape (N, C, outH, outW).
- T:tensor(float32, Linear)
Perform Modulated Deformable Convolution on input feature, read Deformable ConvNets v2: More Deformable, Better Results for detail.
| 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_groups |
Groups of deformable offset. |
int |
groups |
Split input into groups. input_channel should be divisible by the number of groups. |
- 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 bias; 1-D tensor of shape (output_channel).
- outputs[0]: T
- Output feature; 4-D tensor of shape (N, output_channel, outH, outW).
- T:tensor(float32, Linear)
Non Max Suppression for rotated bboxes.
| Type | Parameter | Description |
|---|---|---|
float |
iou_threshold |
The IoU threshold for NMS. |
- inputs[0]: T
- Input feature; 2-D tensor of shape (N, 5), where N is the number of rotated bboxes, .
- inputs[1]: T
- Input offset; 1-D tensor of shape (N, ), where N is the number of rotated bboxes.
- outputs[0]: T
- Output feature; 1-D tensor of shape (K, ), where K is the number of keep bboxes.
- T:tensor(float32, Linear)
Perform RoIAlignRotated on output feature, used in bbox_head of most two-stage rotated object detectors.
| 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. |
int |
aligned |
If aligned=0, use the legacy implementation in MMDetection. Else, align the results more perfectly. |
int |
clockwise |
If True, the angle in each proposal follows a clockwise fashion in image space, otherwise, the angle is counterclockwise. Default: False. |
- input: 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.
- rois: T
- RoIs (Regions of Interest) to pool over; 2-D tensor of shape (num_rois, 6) given as [[batch_index, cx, cy, w, h, theta], ...]. The RoIs' coordinates are the coordinate system of input.
- feat: T
- RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element feat[r-1] is a pooled feature map corresponding to the r-th RoI RoIs[r-1].
- T:tensor(float32)