feat(models/cls): Add FReLU and WeightNet models (#8)

models/megengine_vision_frelu.md

+---
+template: hub1
+title: FReLU 
+summary:
+    en_US: "Funnel Activation for Visual Recognition"
+    zh_CN: FReLU （ImageNet 预训练权重）
+author: MegEngine Team
+tags: [vision, classification]
+github-link: https://github.com/megvii-model/FunnelAct
+---
+
+```python
+import megengine.hub
+model = megengine.hub.load('megvii-model/funnelact', 'resnet50_frelu', pretrained=True)
+# model = megengine.hub.load('megvii-model/funnelact', 'shufflenet_v2_x0_5', pretrained=True)
+model.eval()
+```
+<!-- section: zh_CN -->
+
+所有预训练模型希望数据被正确预处理。
+模型要求输入BGR的图片, 短边缩放到`256`, 并中心裁剪至`(224 x 224)`的大小，无需归一化处理。
+
+下面是一段处理一张图片的样例代码。
+
+```python
+# Download an example image from the megengine data website
+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
+import numpy as np
+import megengine.data.transform as T
+import megengine.functional as F
+
+image = cv2.imread("cat.jpg").astype(np.float32)
+transform = T.Compose([
+    T.Resize(256),
+    T.CenterCrop(224),
+    T.ToMode("CHW"),
+])
+processed_img = transform.apply(image)[np.newaxis, :]  # CHW -> 1CHW
+logits = model(processed_img)
+probs = F.softmax(logits)
+print(probs)
+```
+
+### 模型描述
+
+目前我们提供了部分在ImageNet上的预训练模型(见下表)，各个网络结构在ImageNet验证集上的表现如下：
+
+|        Model             | Activation | Top-1 err.|
+| :----------------------  | :--------: | :------:  |
+|    ResNet50              |  ReLU      | 24.0      |
+|    ResNet50              |  PReLU     | 23.7      |
+|    ResNet50              |  Swish     | 23.5      |
+|    ResNet50              |  FReLU     | **22.4**  |
+|    ShuffleNetV2 0.5x     |  ReLU      | 39.6      |
+|    ShuffleNetV2 0.5x     |  PReLU     | 39.1      |
+|    ShuffleNetV2 0.5x     |  Swish     | 38.7      |
+|    ShuffleNetV2 0.5x     |  FReLU     | **37.1**  |
+
+### 参考文献
+
+- [Funnel Activation for Visual Recognition](https://arxiv.org/abs/2007.11824), Ma, Ningning, et al. "Funnel Activation for Visual Recognition." Proceedings of the European Conference on Computer Vision (ECCV). 2020.
+
+<!-- section: en_US -->
+
+All pre-trained models expect input images normalized in the same way,
+i.e. input images must be 3-channel BGR images of shape `(H x W x 3)`, and reszied shortedge to `256`, center-cropped to `(224 x 224)`.
+No normalization required.
+
+Here's a sample execution.
+
+```python
+# Download an example image from the megengine data website
+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
+import numpy as np
+import megengine.data.transform as T
+import megengine.functional as F
+
+image = cv2.imread("cat.jpg").astype(np.float32)
+transform = T.Compose([
+    T.Resize(256),
+    T.CenterCrop(224),
+    T.ToMode("CHW"),
+])
+processed_img = transform.apply(image)[np.newaxis, :]  # CHW -> 1CHW
+logits = model(processed_img)
+probs = F.softmax(logits)
+print(probs)
+```
+
+### Model Description
+
+Currently we provide several pretrained models(see the table below), Their 1-crop accuracy on ImageNet validation dataset can be found in following table.
+
+|        Model             | Activation | Top-1 err.|
+| :----------------------  | :--------: | :------:  |
+|    ResNet50              |  ReLU      | 24.0      |
+|    ResNet50              |  PReLU     | 23.7      |
+|    ResNet50              |  Swish     | 23.5      |
+|    ResNet50              |  FReLU     | **22.4**  |
+|    ShuffleNetV2 0.5x     |  ReLU      | 39.6      |
+|    ShuffleNetV2 0.5x     |  PReLU     | 39.1      |
+|    ShuffleNetV2 0.5x     |  Swish     | 38.7      |
+|    ShuffleNetV2 0.5x     |  FReLU     | **37.1**  |
+
+### References
+
+- [Funnel Activation for Visual Recognition](https://arxiv.org/abs/2007.11824), Ma, Ningning, et al. "Funnel Activation for Visual Recognition." Proceedings of the European Conference on Computer Vision (ECCV). 2020.

models/megengine_vision_weightnet.md

+---
+template: hub1
+title: WeightNet 
+summary:
+    en_US: "WeightNet: Revisiting the Design Space of Weight Network"
+    zh_CN: WeightNet - ShuffleNet V2（ImageNet 预训练权重）
+author: MegEngine Team
+tags: [vision, classification]
+github-link: https://github.com/megvii-model/WeightNet
+---
+
+```python
+import megengine.hub
+model = megengine.hub.load('megvii-model/weightnet', 'shufflenet_v2_x0_5', pretrained=True)
+model.eval()
+```
+<!-- section: zh_CN -->
+
+所有预训练模型希望数据被正确预处理。
+模型要求输入BGR的图片, 短边缩放到`256`, 并中心裁剪至`(224 x 224)`的大小，无需归一化处理。
+
+下面是一段处理一张图片的样例代码。
+
+```python
+# Download an example image from the megengine data website
+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
+import numpy as np
+import megengine.data.transform as T
+import megengine.functional as F
+
+image = cv2.imread("cat.jpg").astype(np.float32)
+transform = T.Compose([
+    T.Resize(256),
+    T.CenterCrop(224),
+    T.ToMode("CHW"),
+])
+processed_img = transform.apply(image)[np.newaxis, :]  # CHW -> 1CHW
+logits = model(processed_img)
+probs = F.softmax(logits)
+print(probs)
+```
+
+### 模型描述
+
+目前我们提供了部分在ImageNet上的预训练模型(见下表)，各个网络结构在ImageNet验证集上的表现如下：
+
+
+| Model               | #Params. | FLOPs | Top-1 err. |
+|---------------------|----------|-------|------------|
+| ShuffleNetV2 (0.5×) | 1.4M     | 41M   | 39.7       |
+| + WeightNet (1×)    | 1.5M     | 41M   | **36.7**   |
+| ShuffleNetV2 (1.0×) | 2.2M     | 138M  | 30.9       |
+| + WeightNet (1×)    | 2.4M     | 139M  | **28.8**   |
+| ShuffleNetV2 (1.5×) | 3.5M     | 299M  | 27.4       |
+| + WeightNet (1×)    | 3.9M     | 301M  | **25.6**   |
+| ShuffleNetV2 (2.0×) | 5.5M     | 557M  | 25.5       |
+| + WeightNet (1×)    | 6.1M     | 562M  | **24.1**   |
+
+
+
+| Model               | #Params. | FLOPs | Top-1 err. |
+|---------------------|----------|-------|------------|
+| ShuffleNetV2 (0.5×) | 1.4M     | 41M   | 39.7       |
+| + WeightNet (8×)    | 2.7M     | 42M   | **34.0**   |
+| ShuffleNetV2 (1.0×) | 2.2M     | 138M  | 30.9       |
+| + WeightNet (4×)    | 5.1M     | 141M  | **27.6**   |
+| ShuffleNetV2 (1.5×) | 3.5M     | 299M  | 27.4       |
+| + WeightNet (4×)    | 9.6M     | 307M  | **25.0**   |
+| ShuffleNetV2 (2.0×) | 5.5M     | 557M  | 25.5       |
+| + WeightNet (4×)    | 18.1M    | 573M  | **23.5**   |
+
+### 参考文献
+
+- [WeightNet: Revisiting the Design Space of Weight Network](https://arxiv.org/abs/2007.11823), Ma, Ningning, et al. "WeightNet: Revisiting the Design Space of Weight Network." Proceedings of the European Conference on Computer Vision (ECCV). 2020.
+
+<!-- section: en_US -->
+
+All pre-trained models expect input images normalized in the same way,
+i.e. input images must be 3-channel BGR images of shape `(H x W x 3)`, and reszied shortedge to `256`, center-cropped to `(224 x 224)`.
+No normalizations required.
+
+Here's a sample execution.
+
+```python
+# Download an example image from the megengine data website
+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
+import numpy as np
+import megengine.data.transform as T
+import megengine.functional as F
+
+image = cv2.imread("cat.jpg").astype(np.float32)
+transform = T.Compose([
+    T.Resize(256),
+    T.CenterCrop(224),
+    T.ToMode("CHW"),
+])
+processed_img = transform.apply(image)[np.newaxis, :]  # CHW -> 1CHW
+logits = model(processed_img)
+probs = F.softmax(logits)
+print(probs)
+```
+
+### Model Description
+
+Currently we provide several pretrained models(see the table below), Their 1-crop accuracy on ImageNet validation dataset can be found in following table.
+
+| Model               | #Params. | FLOPs | Top-1 err. |
+|---------------------|----------|-------|------------|
+| ShuffleNetV2 (0.5×) | 1.4M     | 41M   | 39.7       |
+| + WeightNet (1×)    | 1.5M     | 41M   | **36.7**   |
+| ShuffleNetV2 (1.0×) | 2.2M     | 138M  | 30.9       |
+| + WeightNet (1×)    | 2.4M     | 139M  | **28.8**   |
+| ShuffleNetV2 (1.5×) | 3.5M     | 299M  | 27.4       |
+| + WeightNet (1×)    | 3.9M     | 301M  | **25.6**   |
+| ShuffleNetV2 (2.0×) | 5.5M     | 557M  | 25.5       |
+| + WeightNet (1×)    | 6.1M     | 562M  | **24.1**   |
+
+
+
+| Model               | #Params. | FLOPs | Top-1 err. |
+|---------------------|----------|-------|------------|
+| ShuffleNetV2 (0.5×) | 1.4M     | 41M   | 39.7       |
+| + WeightNet (8×)    | 2.7M     | 42M   | **34.0**   |
+| ShuffleNetV2 (1.0×) | 2.2M     | 138M  | 30.9       |
+| + WeightNet (4×)    | 5.1M     | 141M  | **27.6**   |
+| ShuffleNetV2 (1.5×) | 3.5M     | 299M  | 27.4       |
+| + WeightNet (4×)    | 9.6M     | 307M  | **25.0**   |
+| ShuffleNetV2 (2.0×) | 5.5M     | 557M  | 25.5       |
+| + WeightNet (4×)    | 18.1M    | 573M  | **23.5**   |
+
+### References
+
+- [WeightNet: Revisiting the Design Space of Weight Network](https://arxiv.org/abs/2007.11823), Ma, Ningning, et al. "WeightNet: Revisiting the Design Space of Weight Network." Proceedings of the European Conference on Computer Vision (ECCV). 2020.