From af98f013712450533c0d39df4c0e045513946819 Mon Sep 17 00:00:00 2001
From: wangna <50322361+wangnaa@users.noreply.github.com>
Date: Wed, 3 Feb 2021 11:25:55 +0800
Subject: [PATCH] add ugatit doc (#155)

* add ugatit doc
---
 docs/en_US/tutorials/ugatit.md | 61 ++++++++++++++++++++++++++++++++--
 1 file changed, 59 insertions(+), 2 deletions(-)

diff --git a/docs/en_US/tutorials/ugatit.md b/docs/en_US/tutorials/ugatit.md
index f32e16d..2735256 100644
--- a/docs/en_US/tutorials/ugatit.md
+++ b/docs/en_US/tutorials/ugatit.md
@@ -1,3 +1,60 @@
-### U-GAT-IT
+# 1 U-GAT-IT
+
+## 1.1 Principle
+
+  Similar to CycleGAN, [U-GAT-IT](https://arxiv.org/abs/1907.10830) uses unpaired pictures for image translation, input two different images with different styles, and automatically perform style transfer. Differently, U-GAT-IT is a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. 
+
+## 1.2 How to use  
+
+### 1.2.1 Prepare Datasets
+
+  Selfie2anime dataset used by U-GAT-IT can be download from [here](https://www.kaggle.com/arnaud58/selfie2anime). You can also use your own dataset.
+  The structure of dataset is as following:
+  ```
+    ├── dataset
+        └── YOUR_DATASET_NAME
+            ├── trainA
+            ├── trainB
+            ├── testA
+            └── testB
+  ```
+
+### 1.2.2 Train/Test
+
+  Datasets used in example is selfie2anime, you can change it to your own dataset in the config file.
+
+  Train a model:
+  ```
+     python -u tools/main.py --config-file configs/ugatit_selfie2anime_light.yaml
+  ```
+
+  Test the model:
+  ```
+     python tools/main.py --config-file configs/ugatit_selfie2anime_light.yaml --evaluate-only --load ${PATH_OF_WEIGHT}
+  ```
+
+## 1.3 Results
+
+![](../../imgs/ugatit.png)
+
+## 1.4 模型下载
+| 模型 | 数据集 | 下载地址 |
+|---|---|---|
+| ugatit_light  | selfie2anime | [ugatit_light](https://paddlegan.bj.bcebos.com/models/ugatit_light.pdparams)
+
+
+
+
+# References
+
+- 1. [U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation](https://arxiv.org/abs/1907.10830)
+
+  ```
+  @article{kim2019u,
+  title={U-GAT-IT: unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation},
+  author={Kim, Junho and Kim, Minjae and Kang, Hyeonwoo and Lee, Kwanghee},
+  journal={arXiv preprint arXiv:1907.10830},
+  year={2019}
+  }
+  ```
 
-To be added, you can refer to [Training/Evaluating/Testing basic usage](../get_started.md)
-- 
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