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3 | 3 | <!-- TOC --> |
4 | 4 |
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5 | 5 | - [ONNX Runtime Ops](#onnx-runtime-ops) |
6 | | - - [RoIAlign](#roialign) |
| 6 | + - [grid_sampler](#grid_sampler) |
7 | 7 | - [Description](#description) |
8 | 8 | - [Parameters](#parameters) |
9 | 9 | - [Inputs](#inputs) |
10 | 10 | - [Outputs](#outputs) |
11 | 11 | - [Type Constraints](#type-constraints) |
12 | | - - [grid_sampler](#grid_sampler) |
| 12 | + - [MMCVModulatedDeformConv2d](#mmcvmodulateddeformconv2d) |
13 | 13 | - [Description](#description-1) |
14 | 14 | - [Parameters](#parameters-1) |
15 | 15 | - [Inputs](#inputs-1) |
16 | 16 | - [Outputs](#outputs-1) |
17 | 17 | - [Type Constraints](#type-constraints-1) |
18 | | - - [MMCVModulatedDeformConv2d](#mmcvmodulateddeformconv2d) |
19 | | - - [Description](#description-2) |
20 | | - - [Parameters](#parameters-2) |
21 | | - - [Inputs](#inputs-2) |
22 | | - - [Outputs](#outputs-2) |
23 | | - - [Type Constraints](#type-constraints-2) |
24 | 18 |
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25 | 19 | <!-- TOC --> |
26 | 20 |
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27 | | -### RoIAlign |
28 | | - |
29 | | -#### Description |
30 | | - |
31 | | -Perform RoIAlign on output feature, used in bbox_head of most two-stage detectors. |
32 | | - |
33 | | -#### Parameters |
34 | | - |
35 | | -| Type | Parameter | Description | |
36 | | -| ------- | ---------------- | ------------------------------------------------------------------------------------------------------------- | |
37 | | -| `int` | `output_height` | height of output roi | |
38 | | -| `int` | `output_width` | width of output roi | |
39 | | -| `float` | `spatial_scale` | used to scale the input boxes | |
40 | | -| `int` | `sampling_ratio` | number of input samples to take for each output sample. `0` means to take samples densely for current models. | |
41 | | -| `str` | `mode` | pooling mode in each bin. `avg` or `max` | |
42 | | -| `int` | `aligned` | If `aligned=0`, use the legacy implementation in MMDetection. Else, align the results more perfectly. | |
43 | | - |
44 | | -#### Inputs |
45 | | - |
46 | | -<dl> |
47 | | -<dt><tt>input</tt>: T</dt> |
48 | | -<dd>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.</dd> |
49 | | -<dt><tt>rois</tt>: T</dt> |
50 | | -<dd>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 input.</dd> |
51 | | -</dl> |
52 | | - |
53 | | -#### Outputs |
54 | | - |
55 | | -<dl> |
56 | | -<dt><tt>feat</tt>: T</dt> |
57 | | -<dd>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].<dd> |
58 | | -</dl> |
59 | | - |
60 | | -#### Type Constraints |
61 | | - |
62 | | -- T:tensor(float32) |
63 | | - |
64 | 21 | ### grid_sampler |
65 | 22 |
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66 | 23 | #### Description |
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