Add MMDetection2Paddle.md (#621)

* add MMDetection2paddle.md

* update MMDetection2paddle.md

docs/inference_model_convertor/toolkits/MMDetection2paddle.md

+# MMDetection模型导出为Paddle模型教程
+
+X2Paddle新增对MMDetection模型支持，目前测试支持的列表如下
+| 模型  | 来源 | OP版本 | 备注 |
+| :---- | :---- | :----- | :--- |
+| FCOS | [pytorch(mmdetection)](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py) | 11 | 仅支持batch=1推理；模型导出需固定shape |
+| FSAF | [pytorch(mmdetection)](https://github.com/open-mmlab/mmdetection/blob/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py) | 11 | 仅支持batch=1推理；模型导出需固定shape |
+| RetinaNet | [pytorch(mmdetection)](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | 11 | 仅支持batch=1推理；模型导出需固定shape |
+| SSD | [pytorch(mmdetection)](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/ssd300_coco.py) | 11 | 仅支持batch=1推理；模型导出需固定shape |
+| YOLOv3 | [pytorch(mmdetection)](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py) |11 | 仅支持batch=1推理；模型导出需固定shape |
+| Faster R-CNN | [pytorch(mmdetection)](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) |11 | 仅支持batch=1推理；模型导出需固定shape |
+
+## 导出教程
+
+### 步骤一、通过MMDetection导出ONNX模型
+导出步骤参考文档[MMDetection导出ONNX](https://mmdetection.readthedocs.io/en/latest/tutorials/pytorch2onnx.html)，以COCO数据集训练的YOLOv3为例，导出示例如下
+```bash
+python tools/deployment/pytorch2onnx.py \
+    configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \
+    checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.pth \
+    --output-file checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.onnx \
+    --input-img demo/demo.jpg \
+    --test-img tests/data/color.jpg \
+    --shape 608 608 \
+    --mean 0 0 0 \
+    --std 255 255 255 \
+    --show \
+    --verify \
+    --dynamic-export \
+    --cfg-options \
+      model.test_cfg.deploy_nms_pre=-1 \
+```
+
+### 步骤二、通过X2Paddle将ONNX模型转换为Paddle格式
+安装X2Paddle最新版本
+```
+pip install x2paddle
+```
+使用如下命令转换
+```shell
+x2paddle --framework=onnx --model=onnx_model.onnx --save_dir=pd_model
+```
+转换后的模型为paddle inference格式，保存在pd_model当中
+
+## 结果测试
+
+<table border="1" class="docutils">
+	<tr>
+	    <th align="center">Model</th>
+	    <th align="center">Config</th>
+	    <th align="center">Metric</th>
+	    <th align="center">ONNX Runtime</th>
+	    <th align="center">Paddle</th>
+	</tr >
+  <tr >
+	    <td align="center">FCOS</td>
+	    <td align="center"><code>configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">34</td>
+	    <td align="center">33.8</td>
+	</tr>
+  <tr >
+	    <td align="center">FSAF</td>
+	    <td align="center"><code>configs/fsaf/fsaf_r50_fpn_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">33.7</td>
+	    <td align="center">33.7</td>
+	</tr>
+  <tr >
+	    <td align="center">RetinaNet</td>
+	    <td align="center"><code>configs/retinanet/retinanet_r50_fpn_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">34.1</td>
+	    <td align="center">34.1</td>
+	</tr>
+	<tr >
+	    <td align="center" align="center" >SSD</td>
+	    <td align="center" align="center"><code>configs/ssd/ssd300_coco.py</code></td>
+	    <td align="center" align="center">Box AP</td>
+	    <td align="center" align="center">25.6</td>
+	    <td align="center" align="center">25.6</td>
+	</tr>
+  <tr >
+	    <td align="center">YOLOv3</td>
+	    <td align="center"><code>configs/yolo/yolov3_d53_mstrain-608_273e_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">31.1</td>
+	    <td align="center">31.1</td>
+	</tr>
+  <tr >
+	    <td align="center">Faster R-CNN</td>
+	    <td align="center"><code>configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">34.8</td>
+	    <td align="center">34.8</td>
+	</tr>
+</table>
+
+Notes:
+
+- 上述AP均为固定shape进行测试，除SSD的shape为300x300、YOLOv3为608x608之外，其他shape均为800x1216