feat(models): add keypoint models (#4)

models/megengine_vision_mspn.md

+---
+template: hub1
+title: MSPN
+summary:
+    en_US: MSPN on COCO
+    zh_CN: MSPN（COCO 预训练权重）
+author: MegEngine Team
+tags: [vision, keypoints]
+github-link: https://github.com/megengine/models
+---
+
+```python3
+import megengine.hub
+model = megengine.hub.load('megengine/models', 'mspn_4stage', pretrained=True)
+model.eval()
+```
+<!-- section: zh_CN --> 
+MSPN是单人关节点检测模型，在多人场景下需要配合人体检测器使用。详细的多人检测代码示例可以参考[inference.py](https://github.com/MegEngine/Models/blob/master/official/vision/keypoints/inference.py)。
+
+针对单张图片，这里提供使用retinanet做人体检测，然后用MSPN检测关节点的示例:
+
+```python3
+
+import urllib
+url, filename = ("https://data.megengine.org.cn/images/cat.jpg", "cat.jpg")
+try: urllib.URLopener().retrieve(url, filename)
+except: urllib.request.urlretrieve(url, filename)
+
+# Read and pre-process the image
+import cv2
+image = cv2.imread("cat.jpg")
+
+import official.vision.detection.retinanet_res50_coco_1x_800size as Det
+detector = Det.retinanet_res50_1x_800size(pretrained=True)
+
+models_api = hub.import_module(
+    "megengine/models",
+    git_host="github.com",
+)
+
+@jit.trace(symbolic=True)
+def det_func():
+    pred = detector(detector.inputs)
+    return pred
+
+@jit.trace(symbolic=True)
+def keypoint_func():
+    pred = model.predict()
+    return pred
+
+evaluator = models_api.KeypointEvaluator(
+    detector,
+    det_func,
+    model,
+    keypoint_func
+    )
+
+print("Detecting Persons")
+person_boxes = evaluator.detect_persons(image)
+
+print("Detecting Keypoints")
+all_keypoints = evaluator.predict(image, person_boxes)
+
+print("Visualizing")
+canvas = evaluator.vis_skeletons(image, all_keypoints)
+cv2.imwrite("vis_skeleton.jpg", canvas)
+```
+
+### 模型描述
+本目录使用了在COCO val2017上的Human AP为56.4的人体检测结果，最后在COCO val2017上人体关节点估计结果为
+|Methods|Backbone|Input Size| AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) |
+|---|:---:|---|---|---|---|---|---|---|---|---|---|---|
+| MSPN_4stage |MSPN|256x192| 0.752 | 0.900 | 0.819 | 0.716 | 0.825 | 0.819 | 0.943 | 0.875 | 0.770 | 0.887 |
+
+### 参考文献
+- [Rethinking on Multi-Stage Networks for Human Pose Estimation](https://arxiv.org/pdf/1901.00148.pdf) Wenbo Li1, Zhicheng Wang, Binyi Yin, Qixiang Peng, Yuming Du, Tianzi Xiao, Gang Yu, Hongtao Lu, Yichen Wei and Jian Sun
+
+<!-- section: en_US --> 
+SimpleBaseline is classical network for single person pose estimation. It can also be applied to multi-person cases when combined with a human detector. The details of this pipline can be referred to [inference.py](https://github.com/MegEngine/Models/blob/master/official/vision/keypoints/inference.py).
+
+For single image, here is a sample execution when SimpleBaseline is combined with retinanet
+
+```python3
+
+import urllib
+url, filename = ("https://data.megengine.org.cn/images/cat.jpg", "cat.jpg")
+try: urllib.URLopener().retrieve(url, filename)
+except: urllib.request.urlretrieve(url, filename)
+
+# Read and pre-process the image
+import cv2
+image = cv2.imread("cat.jpg")
+
+import official.vision.detection.retinanet_res50_coco_1x_800size as Det
+detector = Det.retinanet_res50_1x_800size(pretrained=True)
+
+models_api = hub.import_module(
+    "megengine/models",
+    git_host="github.com",
+)
+
+@jit.trace(symbolic=True)
+def det_func():
+    pred = detector(detector.inputs)
+    return pred
+
+@jit.trace(symbolic=True)
+def keypoint_func():
+    pred = model.predict()
+    return pred
+
+evaluator = models_api.KeypointEvaluator(
+    detector,
+    det_func,
+    model,
+    keypoint_func
+    )
+
+print("Detecting Persons")
+person_boxes = evaluator.detect_persons(image)
+
+print("Detecting Keypoints")
+all_keypoints = evaluator.predict(image, person_boxes)
+
+print("Visualizing")
+canvas = evaluator.vis_skeletons(image, all_keypoints)
+cv2.imwrite("vis_skeleton.jpg", canvas)
+```
+### Model Desription
+
+With the AP human detectoin results being 56.4 on COCO val2017 dataset, the performances of simplebline on COCO val2017 dataset is 
+
+|Methods|Backbone|Input Size| AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) |
+|---|:---:|---|---|---|---|---|---|---|---|---|---|---|
+| MSPN_4stage |MSPN|256x192| 0.752 | 0.900 | 0.819 | 0.716 | 0.825 | 0.819 | 0.943 | 0.875 | 0.770 | 0.887 |
+
+### References
+- [Rethinking on Multi-Stage Networks for Human Pose Estimation](https://arxiv.org/pdf/1901.00148.pdf) Wenbo Li1, Zhicheng Wang, Binyi Yin, Qixiang Peng, Yuming Du, Tianzi Xiao, Gang Yu, Hongtao Lu, Yichen Wei and Jian Sun
\ No newline at end of file

models/megengine_vision_simplebaseline.md

+---
+template: hub1
+title: SimpleBaseline
+summary:
+    en_US: SimpleBaeline on COCO
+    zh_CN: SimpleBaeline（COCO 预训练权重）
+author: MegEngine Team
+tags: [vision, keypoints]
+github-link: https://github.com/megengine/models
+---
+
+```python3
+import megengine.hub
+model = megengine.hub.load('megengine/models', 'simplebaseline_res50', pretrained=True)
+# or any of these variants
+# model = megengine.hub.load('megengine/models', 'simplebaseline_res101', pretrained=True)
+# model = megengine.hub.load('megengine/models', 'simplebaseline_res152', pretrained=True)
+model.eval()
+```
+<!-- section: zh_CN --> 
+SimpleBaseline是单人关节点检测模型，在多人场景下需要配合人体检测器使用。详细的多人检测代码示例可以参考[inference.py](https://github.com/MegEngine/Models/blob/master/official/vision/keypoints/inference.py)。
+
+针对单张图片，这里提供使用retinanet做人体检测，然后用SimpleBaseline检测关节点的示例:
+```python3
+
+import urllib
+url, filename = ("https://data.megengine.org.cn/images/cat.jpg", "cat.jpg")
+try: urllib.URLopener().retrieve(url, filename)
+except: urllib.request.urlretrieve(url, filename)
+
+# Read and pre-process the image
+import cv2
+image = cv2.imread("cat.jpg")
+
+import official.vision.detection.retinanet_res50_coco_1x_800size as Det
+detector = Det.retinanet_res50_1x_800size(pretrained=True)
+
+models_api = hub.import_module(
+    "megengine/models",
+    git_host="github.com",
+)
+
+@jit.trace(symbolic=True)
+def det_func():
+    pred = detector(detector.inputs)
+    return pred
+
+@jit.trace(symbolic=True)
+def keypoint_func():
+    pred = model.predict()
+    return pred
+
+evaluator = models_api.KeypointEvaluator(
+    detector,
+    det_func,
+    model,
+    keypoint_func
+    )
+
+print("Detecting Persons")
+person_boxes = evaluator.detect_persons(image)
+
+print("Detecting Keypoints")
+all_keypoints = evaluator.predict(image, person_boxes)
+
+print("Visualizing")
+canvas = evaluator.vis_skeletons(image, all_keypoints)
+cv2.imwrite("vis_skeleton.jpg", canvas)
+```
+
+### 模型描述
+本目录使用了在COCO val2017上的Human AP为56.4的人体检测结果，最后在COCO val2017上人体关节点估计结果为
+|Methods|Backbone|Input Size| AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) |
+|---|:---:|---|---|---|---|---|---|---|---|---|---|---|
+| SimpleBaseline |Res50 |256x192| 0.712 | 0.887 | 0.779 | 0.673 | 0.785 | 0.782 | 0.932 | 0.839 | 0.730 | 0.854 |
+| SimpleBaseline |Res101|256x192| 0.722 | 0.891 | 0.795 | 0.687 | 0.795 | 0.794 | 0.936 | 0.855 | 0.745 | 0.863 |
+| SimpleBaseline |Res152|256x192| 0.724 | 0.888 | 0.794 | 0.688 | 0.795 | 0.795 | 0.934 | 0.856 | 0.746 | 0.863 |
+
+### 参考文献
+- [Simple Baselines for Human Pose Estimation and Tracking](https://arxiv.org/pdf/1804.06208.pdf), Bin Xiao, Haiping Wu, and Yichen Wei
+
+<!-- section: en_US --> 
+SimpleBaseline is classical network for single person pose estimation. It can also be applied to multi-person cases when combined with a human detector. The details of this pipline can be referred to [inference.py](https://github.com/MegEngine/Models/blob/master/official/vision/keypoints/inference.py).
+
+For single image, here is a sample execution when SimpleBaseline is combined with retinanet
+
+```python3
+
+import urllib
+url, filename = ("https://data.megengine.org.cn/images/cat.jpg", "cat.jpg")
+try: urllib.URLopener().retrieve(url, filename)
+except: urllib.request.urlretrieve(url, filename)
+
+# Read and pre-process the image
+import cv2
+image = cv2.imread("cat.jpg")
+
+import official.vision.detection.retinanet_res50_coco_1x_800size as Det
+detector = Det.retinanet_res50_1x_800size(pretrained=True)
+
+models_api = hub.import_module(
+    "megengine/models",
+    git_host="github.com",
+)
+
+@jit.trace(symbolic=True)
+def det_func():
+    pred = detector(detector.inputs)
+    return pred
+
+@jit.trace(symbolic=True)
+def keypoint_func():
+    pred = model.predict()
+    return pred
+
+evaluator = models_api.KeypointEvaluator(
+    detector,
+    det_func,
+    model,
+    keypoint_func
+    )
+
+print("Detecting Persons")
+person_boxes = evaluator.detect_persons(image)
+
+print("Detecting Keypoints")
+all_keypoints = evaluator.predict(image, person_boxes)
+
+print("Visualizing")
+canvas = evaluator.vis_skeletons(image, all_keypoints)
+cv2.imwrite("vis_skeleton.jpg", canvas)
+```
+### Model Desription
+
+With the AP human detectoin results being 56.4 on COCO val2017 dataset, the performances of simplebline on COCO val2017 dataset are
+
+|Methods|Backbone|Input Size| AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) |
+|---|:---:|---|---|---|---|---|---|---|---|---|---|---|
+| SimpleBaseline |Res50 |256x192| 0.712 | 0.887 | 0.779 | 0.673 | 0.785 | 0.782 | 0.932 | 0.839 | 0.730 | 0.854 |
+| SimpleBaseline |Res101|256x192| 0.722 | 0.891 | 0.795 | 0.687 | 0.795 | 0.794 | 0.936 | 0.855 | 0.745 | 0.863 |
+| SimpleBaseline |Res152|256x192| 0.724 | 0.888 | 0.794 | 0.688 | 0.795 | 0.795 | 0.934 | 0.856 | 0.746 | 0.863 |
+
+### References
+- [Simple Baselines for Human Pose Estimation and Tracking](https://arxiv.org/pdf/1804.06208.pdf), Bin Xiao, Haiping Wu, and Yichen Wei
\ No newline at end of file