fix res2net code and doc

README.md

@@ -6,15 +6,13 @@
 
 PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.
 
-
 **Recent update**
+- 2020.09.17 Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.1%. Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.9%.
 - 2020.10.12 Add Paddle-Lite demo。
 - 2020.10.10 Add cpp inference demo and improve FAQ tutorial.
 - 2020.09.17 Add `HRNet_W48_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.62%. Add `ResNet34_vd_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.72%.
 - 2020.09.07 Add `HRNet_W18_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 81.16%.
 - 2020.07.14 Add `Res2Net200_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 85.13%. Add `Fix_ResNet50_vd_ssld_v2` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.00%.
-- 2020.06.17 Add English documents.
-- 2020.06.12 Add support for training and evaluation on Windows or CPU.
 - [more](./docs/en/update_history_en.md)
 
 

README_cn.md

@@ -7,13 +7,12 @@
 飞桨图像分类套件PaddleClas是飞桨为工业界和学术界所准备的一个图像分类任务的工具集，助力使用者训练出更好的视觉模型和应用落地。
 
 **近期更新**
+- 2020.10.20 添加 `Res2Net50_vd_26w_4s_ssld `模型，在ImageNet-1k上Top-1 Acc可达83.1%；添加 `Res2Net101_vd_26w_4s_ssld `模型，在ImageNet-1k上Top-1 Acc可达83.9%。
 - 2020.10.12 添加Paddle-Lite demo。
 - 2020.10.10 添加cpp inference demo，完善`FAQ 30问`教程。
 - 2020.09.17 添加 `HRNet_W48_C_ssld `模型，在ImageNet-1k上Top-1 Acc可达83.62%；添加 `ResNet34_vd_ssld `模型，在ImageNet-1k上Top-1 Acc可达79.72%。
 - 2020.09.07 添加 `HRNet_W18_C_ssld `模型，在ImageNet-1k上Top-1 Acc可达81.16%；添加 `MobileNetV3_small_x0_35_ssld `模型，在ImageNet-1k上Top-1 Acc可达55.55%。
 - 2020.07.14 添加 `Res2Net200_vd_26w_4s_ssld `模型，在ImageNet-1k上Top-1 Acc可达85.13%；添加 `Fix_ResNet50_vd_ssld_v2 `模型，在ImageNet-1k上Top-1 Acc可达84.0%。
-- 2020.06.17 添加英文文档。
-- 2020.06.12 添加对windows和CPU环境的训练与评估支持。
 - [more](./docs/zh_CN/update_history.md)
 
 

docs/en/models/SEResNext_and_Res2Net_en.md

@@ -30,8 +30,10 @@ At present, there are a total of 24 pretrained models of the three categories op
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | Res2Net50_26w_4s      | 0.793  | 0.946  | 0.780             | 0.936             | 8.520        | 25.700            |
 | Res2Net50_vd_26w_4s   | 0.798  | 0.949  |                   |                   | 8.370        | 25.060            |
+| Res2Net50_vd_26w_4s_ssld   | 0.831  | 0.966  |                   |                   | 8.370        | 25.060            |
 | Res2Net50_14w_8s      | 0.795  | 0.947  | 0.781             | 0.939             | 9.010        | 25.720            |
 | Res2Net101_vd_26w_4s  | 0.806  | 0.952  |                   |                   | 16.670       | 45.220            |
+| Res2Net101_vd_26w_4s_ssld  | 0.839  | 0.971  |                   |                   | 16.670       | 45.220            |
 | Res2Net200_vd_26w_4s  | 0.812  | 0.957  |                   |                   | 31.490       | 76.210            |
 | Res2Net200_vd_26w_4s_ssld  | **0.851**  | 0.974  |                   |                   | 31.490       | 76.210            |
 | ResNeXt50_32x4d       | 0.778  | 0.938  | 0.778             |                   | 8.020        | 23.640            |

docs/en/update_history_en.md

 # Release Notes
 
+* 2020.10.20
+    * Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 83.1%.
+    * Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 83.9%.
+
 - 2020.10.12
     * Add Paddle-Lite demo.
 

docs/zh_CN/models/SEResNext_and_Res2Net.md

@@ -29,8 +29,10 @@ Res2Net是2019年提出的一种全新的对ResNet的改进方案，该方案可
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | Res2Net50_26w_4s      | 0.793  | 0.946  | 0.780             | 0.936             | 8.520        | 25.700            |
 | Res2Net50_vd_26w_4s   | 0.798  | 0.949  |                   |                   | 8.370        | 25.060            |
+| Res2Net50_vd_26w_4s_ssld   | 0.831  | 0.966  |                   |                   | 8.370        | 25.060            |
 | Res2Net50_14w_8s      | 0.795  | 0.947  | 0.781             | 0.939             | 9.010        | 25.720            |
 | Res2Net101_vd_26w_4s  | 0.806  | 0.952  |                   |                   | 16.670       | 45.220            |
+| Res2Net101_vd_26w_4s_ssld  | 0.839  | 0.971  |                   |                   | 16.670       | 45.220            |
 | Res2Net200_vd_26w_4s  | 0.812  | 0.957  |                   |                   | 31.490       | 76.210            |
 | Res2Net200_vd_26w_4s_ssld  | **0.851**  | 0.974  |                   |                   | 31.490       | 76.210            |
 | ResNeXt50_32x4d       | 0.778  | 0.938  | 0.778             |                   | 8.020        | 23.640            |

docs/zh_CN/update_history.md

 # 更新日志
 
+- 2020.10.20
+    * 添加Res2Net50_vd_26w_4s_ssld模型，在ImageNet上Top-1 Acc可达0.831；添加Res2Net101_vd_26w_4s_ssld模型，在ImageNet上Top-1 Acc可达0.839。
+
 - 2020.10.12
     * 添加Paddle-Lite demo。
 

ppcls/modeling/architectures/res2net_vd.py

@@ -57,7 +57,6 @@ class ConvBNLayer(nn.Layer):
             stride=stride,
             padding=(filter_size - 1) // 2,
             groups=groups,
-            act=None,
             weight_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False)
         if name == "conv1":
@@ -111,8 +110,7 @@ class BottleneckBlock(nn.Layer):
                     act='relu',
                     name=name + '_branch2b_' + str(s + 1)))
             self.conv1_list.append(conv1)
-        self.pool2d_avg = AvgPool2d(
-            kernel_size=3, stride=stride, padding=1, ceil_mode=True)
+        self.pool2d_avg = AvgPool2d(kernel_size=3, stride=stride, padding=1)
 
         self.conv2 = ConvBNLayer(
             num_channels=num_filters,