From 91785a45e1940ac887f21b874e6e7dc2538debaa Mon Sep 17 00:00:00 2001
From: littletomatodonkey <2120160898@bit.edu.cn>
Date: Sun, 29 Nov 2020 18:10:20 +0800
Subject: [PATCH] fix dpn (#430)
* fix dpn
* fix se resnext101 metrics
---
README.md | 2 +-
README_cn.md | 2 +-
docs/en/models/SEResNext_and_Res2Net_en.md | 2 +-
docs/zh_CN/models/SEResNext_and_Res2Net.md | 2 +-
ppcls/modeling/architectures/dpn.py | 2 +-
5 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/README.md b/README.md
index 3248eaff..59c23c53 100644
--- a/README.md
+++ b/README.md
@@ -219,7 +219,7 @@ Accuracy and inference time metrics of SEResNeXt and Res2Net series models are s
| SE_ResNet50_vd | 0.7952 | 0.9475 | 4.28393 | 10.38846 | 8.67 | 28.09 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNet50_vd_pretrained.tar) |
| SE_ResNeXt50_
32x4d | 0.7844 | 0.9396 | 8.74121 | 13.563 | 8.02 | 26.16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) |
| SE_ResNeXt50_vd_
32x4d | 0.8024 | 0.9489 | 9.17134 | 14.76192 | 10.76 | 26.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_vd_32x4d_pretrained.tar) |
-| SE_ResNeXt101_
32x4d | 0.7912 | 0.9420 | 18.82604 | 25.31814 | 15.02 | 46.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) |
+| SE_ResNeXt101_
32x4d | 0.7939 | 0.9443 | 18.82604 | 25.31814 | 15.02 | 46.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) |
| SENet154_vd | 0.8140 | 0.9548 | 53.79794 | 66.31684 | 45.83 | 114.29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_pretrained.tar) |
diff --git a/README_cn.md b/README_cn.md
index 1027e42b..87281dfa 100644
--- a/README_cn.md
+++ b/README_cn.md
@@ -221,7 +221,7 @@ SEResNeXt与Res2Net系列模型的精度、速度指标如下表所示,更多
| SE_ResNet50_vd | 0.7952 | 0.9475 | 4.28393 | 10.38846 | 8.67 | 28.09 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNet50_vd_pretrained.tar) |
| SE_ResNeXt50_
32x4d | 0.7844 | 0.9396 | 8.74121 | 13.563 | 8.02 | 26.16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) |
| SE_ResNeXt50_vd_
32x4d | 0.8024 | 0.9489 | 9.17134 | 14.76192 | 10.76 | 26.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_vd_32x4d_pretrained.tar) |
-| SE_ResNeXt101_
32x4d | 0.7912 | 0.9420 | 18.82604 | 25.31814 | 15.02 | 46.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) |
+| SE_ResNeXt101_
32x4d | 0.7939 | 0.9443 | 18.82604 | 25.31814 | 15.02 | 46.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) |
| SENet154_vd | 0.8140 | 0.9548 | 53.79794 | 66.31684 | 45.83 | 114.29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_pretrained.tar) |
diff --git a/docs/en/models/SEResNext_and_Res2Net_en.md b/docs/en/models/SEResNext_and_Res2Net_en.md
index 6af2f33c..4ccbce59 100644
--- a/docs/en/models/SEResNext_and_Res2Net_en.md
+++ b/docs/en/models/SEResNext_and_Res2Net_en.md
@@ -53,7 +53,7 @@ At present, there are a total of 24 pretrained models of the three categories op
| SE_ResNet50_vd | 0.795 | 0.948 | | | 8.670 | 28.090 |
| SE_ResNeXt50_32x4d | 0.784 | 0.940 | 0.789 | 0.945 | 8.020 | 26.160 |
| SE_ResNeXt50_vd_32x4d | 0.802 | 0.949 | | | 10.760 | 26.280 |
-| SE_ResNeXt101_32x4d | 0.791 | 0.942 | 0.793 | 0.950 | 15.020 | 46.280 |
+| SE_ResNeXt101_32x4d | 0.7939 | 0.9443 | 0.793 | 0.950 | 15.020 | 46.280 |
| SENet154_vd | 0.814 | 0.955 | | | 45.830 | 114.290 |
diff --git a/docs/zh_CN/models/SEResNext_and_Res2Net.md b/docs/zh_CN/models/SEResNext_and_Res2Net.md
index 7754d2f7..f2fd1c8f 100644
--- a/docs/zh_CN/models/SEResNext_and_Res2Net.md
+++ b/docs/zh_CN/models/SEResNext_and_Res2Net.md
@@ -52,7 +52,7 @@ Res2Net是2019年提出的一种全新的对ResNet的改进方案,该方案可
| SE_ResNet50_vd | 0.795 | 0.948 | | | 8.670 | 28.090 |
| SE_ResNeXt50_32x4d | 0.784 | 0.940 | 0.789 | 0.945 | 8.020 | 26.160 |
| SE_ResNeXt50_vd_32x4d | 0.802 | 0.949 | | | 10.760 | 26.280 |
-| SE_ResNeXt101_32x4d | 0.791 | 0.942 | 0.793 | 0.950 | 15.020 | 46.280 |
+| SE_ResNeXt101_32x4d | 0.7939 | 0.9443 | 0.793 | 0.950 | 15.020 | 46.280 |
| SENet154_vd | 0.814 | 0.955 | | | 45.830 | 114.290 |
diff --git a/ppcls/modeling/architectures/dpn.py b/ppcls/modeling/architectures/dpn.py
index 7710a73b..bfdb99c1 100644
--- a/ppcls/modeling/architectures/dpn.py
+++ b/ppcls/modeling/architectures/dpn.py
@@ -232,7 +232,7 @@ class DPN(nn.Layer):
num_filters=init_num_filter,
filter_size=init_filter_size,
stride=2,
- pad=1,
+ pad=init_padding,
act='relu',
name="conv1")
--
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