7b9afd43 · 7b9afd43 · 7b9afd43 · 7b9afd43 · 7b9afd43 · 7b9afd43
51 changed file
--- a/.gitignore
+++ b/.gitignore
@@ -142,6 +142,8 @@ test*
 video_tmp/
 result/
 nohup.out
+.vscode/
+
 #./
 /pix2pix
 /pix2pixHD
@@ -156,6 +158,7 @@ nohup.out
 /deepmosaic_window
 /sftp-config.json
 /exe
+
 #./make_datasets
 /make_datasets/video
 /make_datasets/tmp
@@ -171,6 +174,7 @@ nohup.out
 #mediafile
 *iter
 *.pth
+*.pt
 *.jpeg
 *.bmp
 *.mp4
@@ -183,4 +187,40 @@ nohup.out
 *.MP4
 *.JPEG
 *.exe
-*.npy
\ No newline at end of file
+*.npy
+*.psd
+
+
+##############################cpp###################################
+# Prerequisites
+*.d
+
+# Compiled Object files
+*.slo
+*.lo
+*.o
+*.obj
+
+# Precompiled Headers
+*.gch
+*.pch
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.dll
+
+# Fortran module files
+*.mod
+*.smod
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+*.lib
+
+# Executables
+*.exe
+*.out
+*.app
\ No newline at end of file
--- a/README.md
+++ b/README.md
-![image](./imgs/hand.gif)
-# <img src="./imgs/icon.jpg" width="48">DeepMosaics
-You can use it to automatically remove the mosaics in images and videos, or add mosaics to them.<br>
-This porject based on "semantic segmentation" and "Image-to-Image Translation".<br>
+<div align="center">
+  <img src="./imgs/logo.png" width="250"><br><br>
+  <img src="https://badgen.net/github/stars/hypox64/deepmosaics?icon=github&color=4ab8a1">&emsp;<img src="https://badgen.net/github/forks/hypox64/deepmosaics?icon=github&color=4ab8a1">&emsp;<a href="https://github.com/HypoX64/DeepMosaics/releases"><img src=https://img.shields.io/github/downloads/hypox64/deepmosaics/total></a>&emsp;<a href="https://github.com/HypoX64/DeepMosaics/releases"><img src=https://img.shields.io/github/v/release/hypox64/DeepMosaics></a>&emsp;<img src=https://img.shields.io/github/license/hypox64/deepmosaics>
+</div>

-* [中文版README](./README_CN.md)<br>
+# DeepMosaics
+**English | [中文](./README_CN.md)**<br>
+You can use it to automatically remove the mosaics in images and videos, or add mosaics to them.<br>This project is based on "semantic segmentation" and "Image-to-Image Translation".<br>Try it at this [website](http://118.89.27.46:5000/)!<br>

-### More example
+### Examples
+![image](./imgs/hand.gif)

 origin | auto add mosaic |  auto clean mosaic  
 :-:|:-:|:-:
@@ -28,24 +31,27 @@ origin | to Van Gogh | to winter
 An interesting example:[Ricardo Milos to cat](https://www.bilibili.com/video/BV1Q7411W7n6)

 ## Run DeepMosaics
-You can either run DeepMosaics via pre-built binary package or from source.<br>
+You can either run DeepMosaics via a pre-built binary package, or from source.<br>

+### Try it on web
+You can simply try to remove the mosaic on the **face** at this [website](http://118.89.27.46:5000/).<br>
 ### Pre-built binary package
-For windows, we bulid a GUI version for easy test.<br>
-Download this version and pre-trained model via [[Google Drive]](https://drive.google.com/open?id=1LTERcN33McoiztYEwBxMuRjjgxh4DEPs)  [[百度云,提取码1x0a]](https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ) <br>
+For Windows, we bulid a GUI version for easy testing.<br>
+Download this version, and a pre-trained model via [[Google Drive]](https://drive.google.com/open?id=1LTERcN33McoiztYEwBxMuRjjgxh4DEPs)  [[百度云,提取码1x0a]](https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ) <br>

-* [[How to use]](./docs/exe_help.md)<br>
+* [[Help document]](./docs/exe_help.md)<br>
+* Video tutorial => [[youtube]](https://www.youtube.com/watch?v=1kEmYawJ_vk) [[bilibili]](https://www.bilibili.com/video/BV1QK4y1a7Av)

 ![image](./imgs/GUI.png)<br>
 Attentions:<br>

-  - Require Windows_x86_64, Windows10 is better.<br>
+  - Requires Windows_x86_64, Windows10 is better.<br>
  - Different pre-trained models are suitable for different effects.[[Introduction to pre-trained models]](./docs/pre-trained_models_introduction.md)<br>
-  - Run time depends on computer performance(The current version does not support gpu, if you need to use gpu please run source).<br>
+  - Run time depends on computers performance (GPU version has better performance but requires CUDA to be installed).<br>
  - If output video cannot be played, you can try with [potplayer](https://daumpotplayer.com/download/).<br>
-  - GUI version update slower than source.<br>
+  - GUI version updates slower than source.<br>

-### Run from source
+### Run From Source
 #### Prerequisites
  - Linux, Mac OS, Windows
  - Python 3.6+
@@ -56,30 +62,29 @@ Attentions:<br>
 This code depends on opencv-python, torchvision available via pip install.
 #### Clone this repo
 ```bash
-git clone https://github.com/HypoX64/DeepMosaics
+git clone https://github.com/HypoX64/DeepMosaics.git
 cd DeepMosaics
 ```
-#### Get pre-trained models
+#### Get Pre-Trained Models
 You can download pre_trained models and put them into './pretrained_models'.<br>
 [[Google Drive]](https://drive.google.com/open?id=1LTERcN33McoiztYEwBxMuRjjgxh4DEPs)  [[百度云,提取码1x0a]](https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ)<br>
 [[Introduction to pre-trained models]](./docs/pre-trained_models_introduction.md)<br>

-#### Simple example
+#### Simple Example
 * Add Mosaic (output media will save in './result')<br>
 ```bash
-python3 deepmosaic.py --media_path ./imgs/ruoruo.jpg --model_path ./pretrained_models/mosaic/add_face.pth --use_gpu 0
+python deepmosaic.py --media_path ./imgs/ruoruo.jpg --model_path ./pretrained_models/mosaic/add_face.pth --gpu_id 0
 ```
 * Clean Mosaic (output media will save in './result')<br>
 ```bash
-python3 deepmosaic.py --media_path ./result/ruoruo_add.jpg --model_path ./pretrained_models/mosaic/clean_face_HD.pth --use_gpu 0
+python deepmosaic.py --media_path ./result/ruoruo_add.jpg --model_path ./pretrained_models/mosaic/clean_face_HD.pth --gpu_id 0
 ```
-#### More parameters
-If you want to test other image or video, please refer to this file.<br>
+#### More Parameters
+If you want to test other images or videos, please refer to this file.<br>
 [[options_introduction.md]](./docs/options_introduction.md) <br>

-## Training with your own dataset
+## Training With Your Own Dataset
 If you want to train with your own dataset, please refer to [training_with_your_own_dataset.md](./docs/training_with_your_own_dataset.md)

-## Acknowledgments
-This code borrows heavily from [[pytorch-CycleGAN-and-pix2pix]](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) [[Pytorch-UNet]](https://github.com/milesial/Pytorch-UNet) [[pix2pixHD]](https://github.com/NVIDIA/pix2pixHD) [[BiSeNet]](https://github.com/ooooverflow/BiSeNet).
-
+## Acknowledgements
+This code borrows heavily from [[pytorch-CycleGAN-and-pix2pix]](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) [[Pytorch-UNet]](https://github.com/milesial/Pytorch-UNet) [[pix2pixHD]](https://github.com/NVIDIA/pix2pixHD) [[BiSeNet]](https://github.com/ooooverflow/BiSeNet) [[DFDNet]](https://github.com/csxmli2016/DFDNet) [[GFRNet_pytorch_new]](https://github.com/sonack/GFRNet_pytorch_new).
--- a/README_CN.md
+++ b/README_CN.md
-![image](./imgs/hand.gif)
-# <img src="./imgs/icon.jpg" width="48">DeepMosaics
-这是一个通过深度学习自动的为图片/视频添加马赛克,或消除马赛克的项目.<br>它基于“语义分割”以及“图像翻译”.<br>
+<div align="center">
+  <img src="./imgs/logo.png" width="250"><br><br>
+  <img src="https://badgen.net/github/stars/hypox64/deepmosaics?icon=github&color=4ab8a1">&emsp;<img src="https://badgen.net/github/forks/hypox64/deepmosaics?icon=github&color=4ab8a1">&emsp;<a href="https://github.com/HypoX64/DeepMosaics/releases"><img src=https://img.shields.io/github/downloads/hypox64/deepmosaics/total></a>&emsp;<a href="https://github.com/HypoX64/DeepMosaics/releases"><img src=https://img.shields.io/github/v/release/hypox64/DeepMosaics></a>&emsp;<img src=https://img.shields.io/github/license/hypox64/deepmosaics>
+</div>
+
+# DeepMosaics
+**[English](./README.md) | 中文**<br>
+
+这是一个通过深度学习自动的为图片/视频添加马赛克,或消除马赛克的项目.<br>它基于“语义分割”以及“图像翻译”.<br>现在可以在这个[网站](http://118.89.27.46:5000/)尝试使用该项目清除马赛克!<br>

-### 更多例子
+### 例子
+![image](./imgs/hand.gif)

 原始 | 自动打码 |  自动去码  
 :-:|:-:|:-:
@@ -26,19 +33,20 @@

 ## 如何运行
 可以通过我们预编译好的二进制包或源代码运行.<br>
-
+### 在网页中运行
+打开[这个网站](http://118.89.27.46:5000/)上传照片，将获得去除马赛克后的结果，受限于当地法律，**目前只支持人脸**.<br>
 ### 预编译的程序包
 对于Windows用户,我们提供了包含GUI界面的免安装软件包.<br>
 可以通过下面两种方式进行下载: [[Google Drive]](https://drive.google.com/open?id=1LTERcN33McoiztYEwBxMuRjjgxh4DEPs)  [[百度云,提取码1x0a]](https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ) <br>

-* [[使用教程]](./docs/exe_help_CN.md)<br>
-
+* [[帮助文档]](./docs/exe_help_CN.md)<br>
+* [[视频教程]](https://www.bilibili.com/video/BV1QK4y1a7Av)<br>
 ![image](./imgs/GUI.png)<br>

 注意事项:<br>
  - 程序的运行要求在64位Windows操作系统,我仅在Windows10运行过,其他版本暂未经过测试<br>
  - 请根据需求选择合适的预训练模型进行测试，不同的预期训练模型具有不同的效果.[[预训练模型介绍]](./docs/pre-trained_models_introduction_CN.md)<br>
-  - 运行时间取决于电脑性能,对于视频文件,我们建议使用源码并在GPU上运行.<br>
+  - 运行时间取决于电脑性能,对于视频文件,我们建议在GPU上运行.<br>
  - 如果输出的视频无法播放,这边建议您尝试[potplayer](https://daumpotplayer.com/download/).<br>
  - 相比于源码,该版本的更新将会延后.

@@ -50,10 +58,10 @@
  - [Pytorch 1.0+](https://pytorch.org/) 
  - CPU or NVIDIA GPU + CUDA CuDNN<br>
 #### Python依赖项
-代码依赖于opencv-python以及 torchvision,可有通过pip install 进行安装.
+代码依赖于opencv-python以及 torchvision,可以通过pip install 进行安装.
 #### 克隆源代码
 ```bash
-git clone https://github.com/HypoX64/DeepMosaics
+git clone https://github.com/HypoX64/DeepMosaics.git
 cd DeepMosaics
 ```
 #### 下载预训练模型
@@ -62,13 +70,13 @@ cd DeepMosaics
 [[预训练模型介绍]](./docs/pre-trained_models_introduction_CN.md)<br>

 #### 简单的例子
-* 为视频添加马赛克,例子中认为脸是需要打码的区域 ,可以通过切换预训练模型切换自动打码区域(输出结果将储存到 './result')<br>
+* 为视频或照片添加马赛克,例子中认为脸是需要打码的区域 ,可以通过切换预训练模型切换自动打码区域(输出结果将储存到 './result')<br>
 ```bash
-python3 deepmosaic.py --media_path ./imgs/ruoruo.jpg --model_path ./pretrained_models/mosaic/add_face.pth --use_gpu 0
+python deepmosaic.py --media_path ./imgs/ruoruo.jpg --model_path ./pretrained_models/mosaic/add_face.pth --gpu_id 0
 ```
-* 将视频中的马赛克移除,对于不同的打码物体需要使用对应的预训练模型进行马赛克消除(输出结果将储存到  './result')<br>
+* 将视频或照片中的马赛克移除,对于不同的打码物体需要使用对应的预训练模型进行马赛克消除(输出结果将储存到  './result')<br>
 ```bash
-python3 deepmosaic.py --media_path ./result/ruoruo_add.jpg --model_path ./pretrained_models/mosaic/clean_face_HD.pth --use_gpu 0
+python deepmosaic.py --media_path ./result/ruoruo_add.jpg --model_path ./pretrained_models/mosaic/clean_face_HD.pth --gpu_id 0
 ```
 #### 更多的参数
 如果想要测试其他的图片或视频,请参照以下文件输入参数.<br>
@@ -78,5 +86,5 @@ python3 deepmosaic.py --media_path ./result/ruoruo_add.jpg --model_path ./pretra
 如果需要使用自己的数据训练模型，请参照 [training_with_your_own_dataset.md](./docs/training_with_your_own_dataset.md)

 ## 鸣谢
-代码大量的参考了以下项目:[[pytorch-CycleGAN-and-pix2pix]](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) [[Pytorch-UNet]](https://github.com/milesial/Pytorch-UNet) [[pix2pixHD]](https://github.com/NVIDIA/pix2pixHD) [[BiSeNet]](https://github.com/ooooverflow/BiSeNet).
+代码大量的参考了以下项目:[[pytorch-CycleGAN-and-pix2pix]](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) [[Pytorch-UNet]](https://github.com/milesial/Pytorch-UNet) [[pix2pixHD]](https://github.com/NVIDIA/pix2pixHD) [[BiSeNet]](https://github.com/ooooverflow/BiSeNet) [[DFDNet]](https://github.com/csxmli2016/DFDNet) [[GFRNet_pytorch_new]](https://github.com/sonack/GFRNet_pytorch_new).

--- a/cores/add.py
+++ b/cores/add.py
+import os
+from queue import Queue
+from threading import Thread
+import time
+import numpy as np
+import cv2
+from models import runmodel
+from util import mosaic,util,ffmpeg,filt
+from util import image_processing as impro
+from .init import video_init
+
+
+'''
+---------------------Add Mosaic---------------------
+'''
+def addmosaic_img(opt,netS):
+    path = opt.media_path
+    print('Add Mosaic:',path)
+    img = impro.imread(path)
+    mask = runmodel.get_ROI_position(img,netS,opt)[0]
+    img = mosaic.addmosaic(img,mask,opt)
+    impro.imwrite(os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_add.jpg'),img)
+
+def get_roi_positions(opt,netS,imagepaths,savemask=True):
+    # resume
+    continue_flag = False
+    if os.path.isfile(os.path.join(opt.temp_dir,'step.json')):
+        step = util.loadjson(os.path.join(opt.temp_dir,'step.json'))
+        resume_frame = int(step['frame'])
+        if int(step['step'])>2:
+            mask_index = np.load(os.path.join(opt.temp_dir,'mask_index.npy'))
+            return mask_index
+        if int(step['step'])>=2 and resume_frame>0:
+            pre_positions = np.load(os.path.join(opt.temp_dir,'roi_positions.npy'))
+            continue_flag = True
+            imagepaths = imagepaths[resume_frame:]
+            
+    positions = []
+    t1 = time.time()
+    if not opt.no_preview:
+        cv2.namedWindow('mask', cv2.WINDOW_NORMAL)
+    print('Step:2/4 -- Find mosaic location')
+
+    img_read_pool = Queue(4)
+    def loader(imagepaths):
+        for imagepath in imagepaths:
+            img_origin = impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepath))
+            img_read_pool.put(img_origin)
+    t = Thread(target=loader,args=(imagepaths,))
+    t.daemon = True
+    t.start()
+
+    for i,imagepath in enumerate(imagepaths,1):
+        img_origin = img_read_pool.get()
+        mask,x,y,size,area = runmodel.get_ROI_position(img_origin,netS,opt)
+        positions.append([x,y,area])  
+        if savemask:
+            t = Thread(target=cv2.imwrite,args=(os.path.join(opt.temp_dir+'/ROI_mask',imagepath), mask,))
+            t.start()
+        if i%1000==0:
+            save_positions = np.array(positions)
+            if continue_flag:
+                save_positions = np.concatenate((pre_positions,save_positions),axis=0)
+            np.save(os.path.join(opt.temp_dir,'roi_positions.npy'),save_positions)
+            step = {'step':2,'frame':i+resume_frame}
+            util.savejson(os.path.join(opt.temp_dir,'step.json'),step)
+
+        #preview result and print
+        if not opt.no_preview:
+            cv2.imshow('mask',mask)
+            cv2.waitKey(1) & 0xFF
+        t2 = time.time()
+        print('\r',str(i)+'/'+str(len(imagepaths)),util.get_bar(100*i/len(imagepaths),num=35),util.counttime(t1,t2,i,len(imagepaths)),end='')
+    
+    if not opt.no_preview:
+        cv2.destroyAllWindows()
+
+    print('\nOptimize ROI locations...')
+    if continue_flag:
+        positions = np.concatenate((pre_positions,positions),axis=0)
+    mask_index = filt.position_medfilt(np.array(positions), 7)
+    step = {'step':3,'frame':0}
+    util.savejson(os.path.join(opt.temp_dir,'step.json'),step)
+    np.save(os.path.join(opt.temp_dir,'roi_positions.npy'),positions)
+    np.save(os.path.join(opt.temp_dir,'mask_index.npy'),np.array(mask_index))
+
+    return mask_index
+
+def addmosaic_video(opt,netS):
+    path = opt.media_path
+    fps,imagepaths = video_init(opt,path)[:2]
+    length = len(imagepaths)
+    start_frame = int(imagepaths[0][7:13])
+    mask_index = get_roi_positions(opt,netS,imagepaths)[(start_frame-1):]
+
+    t1 = time.time()
+    if not opt.no_preview:
+        cv2.namedWindow('preview', cv2.WINDOW_NORMAL)
+
+    # add mosaic
+    print('Step:3/4 -- Add Mosaic:')
+    t1 = time.time()
+    # print(mask_index)
+    for i,imagepath in enumerate(imagepaths,1):
+        mask = impro.imread(os.path.join(opt.temp_dir+'/ROI_mask',imagepaths[np.clip(mask_index[i-1]-start_frame,0,1000000)]),'gray')
+        img = impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepath))
+        if impro.mask_area(mask)>100: 
+            try:#Avoid unknown errors
+                img = mosaic.addmosaic(img, mask, opt)
+            except Exception as e:
+                   print('Warning:',e)
+        t = Thread(target=cv2.imwrite,args=(os.path.join(opt.temp_dir+'/addmosaic_image',imagepath),img))
+        t.start()
+        os.remove(os.path.join(opt.temp_dir+'/video2image',imagepath))
+        
+        #preview result and print
+        if not opt.no_preview:
+            cv2.imshow('preview',img)
+            cv2.waitKey(1) & 0xFF
+        t2 = time.time()
+        print('\r',str(i)+'/'+str(length),util.get_bar(100*i/length,num=35),util.counttime(t1,t2,i,length),end='')
+    
+    print()
+    if not opt.no_preview:
+        cv2.destroyAllWindows()
+    print('Step:4/4 -- Convert images to video')
+    ffmpeg.image2video( fps,
+                        opt.temp_dir+'/addmosaic_image/output_%06d.'+opt.tempimage_type,
+                        opt.temp_dir+'/voice_tmp.mp3',
+                         os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_add.mp4'))
\ No newline at end of file
--- a/cores/core.py
+++ b/cores/core.py
@@ -2,150 +2,61 @@ import os
 import time
 import numpy as np
 import cv2
-
-from models import runmodel,loadmodel
-from util import mosaic,util,ffmpeg,filt,data
+import torch
+from models import runmodel
+from util import data,util,ffmpeg,filt
 from util import image_processing as impro
-
-'''
---------------------Video Init---------------------
-'''
-def video_init(opt,path):
-    util.clean_tempfiles(opt)
-    fps,endtime,height,width = ffmpeg.get_video_infos(path)
-    if opt.fps !=0:
-        fps = opt.fps
-    ffmpeg.video2voice(path,opt.temp_dir+'/voice_tmp.mp3',opt.start_time,opt.last_time)
-    ffmpeg.video2image(path,opt.temp_dir+'/video2image/output_%06d.'+opt.tempimage_type,fps,opt.start_time,opt.last_time)
-    imagepaths=os.listdir(opt.temp_dir+'/video2image')
-    imagepaths.sort()
-    return fps,imagepaths,height,width
-
-'''
---------------------Add Mosaic---------------------
-'''
-def addmosaic_img(opt,netS):
-    path = opt.media_path
-    print('Add Mosaic:',path)
-    img = impro.imread(path)
-    mask = runmodel.get_ROI_position(img,netS,opt)[0]
-    img = mosaic.addmosaic(img,mask,opt)
-    impro.imwrite(os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_add.jpg'),img)
-
-def addmosaic_video(opt,netS):
-    path = opt.media_path
-    fps,imagepaths = video_init(opt,path)[:2]
-    length = len(imagepaths)
-    # get position
-    positions = []
-    t1 = time.time()
-    if not opt.no_preview:
-        cv2.namedWindow('preview', cv2.WINDOW_NORMAL)
-    
-    print('Find ROI location:')
-    for i,imagepath in enumerate(imagepaths,1):
-        img = impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepath))
-        mask,x,y,size,area = runmodel.get_ROI_position(img,netS,opt)
-        positions.append([x,y,area])      
-        cv2.imwrite(os.path.join(opt.temp_dir+'/ROI_mask',imagepath),mask)     
-        
-        #preview result and print
-        if not opt.no_preview:
-            cv2.imshow('preview',mask)
-            cv2.waitKey(1) & 0xFF
-        t2 = time.time()
-        print('\r',str(i)+'/'+str(length),util.get_bar(100*i/length,num=35),util.counttime(t1,t2,i,length),end='')
-    
-    print('\nOptimize ROI locations...')
-    mask_index = filt.position_medfilt(np.array(positions), 7)
-
-    # add mosaic
-    print('Add Mosaic:')
-    t1 = time.time()
-    for i,imagepath in enumerate(imagepaths,1):
-        mask = impro.imread(os.path.join(opt.temp_dir+'/ROI_mask',imagepaths[mask_index[i-1]]),'gray')
-        img = impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepath))
-        if impro.mask_area(mask)>100: 
-            try:#Avoid unknown errors
-                img = mosaic.addmosaic(img, mask, opt)
-            except Exception as e:
-                   print('Warning:',e)
-        cv2.imwrite(os.path.join(opt.temp_dir+'/addmosaic_image',imagepath),img)
-        
-        #preview result and print
-        if not opt.no_preview:
-            cv2.imshow('preview',img)
-            cv2.waitKey(1) & 0xFF
-        t2 = time.time()
-        print('\r',str(i)+'/'+str(length),util.get_bar(100*i/length,num=35),util.counttime(t1,t2,i,length),end='')
-    
-    print()
-    if not opt.no_preview:
-        cv2.destroyAllWindows()
-    ffmpeg.image2video( fps,
-                        opt.temp_dir+'/addmosaic_image/output_%06d.'+opt.tempimage_type,
-                        opt.temp_dir+'/voice_tmp.mp3',
-                         os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_add.mp4'))
-
-'''
---------------------Style Transfer---------------------
-'''
-def styletransfer_img(opt,netG):
-    print('Style Transfer_img:',opt.media_path)
-    img = impro.imread(opt.media_path)
-    img = runmodel.run_styletransfer(opt, netG, img)
-    suffix = os.path.basename(opt.model_path).replace('.pth','').replace('style_','')
-    impro.imwrite(os.path.join(opt.result_dir,os.path.splitext(os.path.basename(opt.media_path))[0]+'_'+suffix+'.jpg'),img)
-
-def styletransfer_video(opt,netG):
-    path = opt.media_path
-    positions = []
-    fps,imagepaths = video_init(opt,path)[:2]
-    print('Transfer:')
-    t1 = time.time()
-    if not opt.no_preview:
-        cv2.namedWindow('preview', cv2.WINDOW_NORMAL)
-    length = len(imagepaths)
-
-    for i,imagepath in enumerate(imagepaths,1):
-        img = impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepath))
-        img = runmodel.run_styletransfer(opt, netG, img)
-        cv2.imwrite(os.path.join(opt.temp_dir+'/style_transfer',imagepath),img)
-        
-        #preview result and print
-        if not opt.no_preview:
-            cv2.imshow('preview',img)
-            cv2.waitKey(1) & 0xFF
-        t2 = time.time()
-        print('\r',str(i)+'/'+str(length),util.get_bar(100*i/length,num=35),util.counttime(t1,t2,i,len(imagepaths)),end='')
-    
-    print()
-    if not opt.no_preview:
-        cv2.destroyAllWindows()
-    suffix = os.path.basename(opt.model_path).replace('.pth','').replace('style_','')
-    ffmpeg.image2video( fps,
-                opt.temp_dir+'/style_transfer/output_%06d.'+opt.tempimage_type,
-                opt.temp_dir+'/voice_tmp.mp3',
-                 os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_'+suffix+'.mp4'))  
+from .init import video_init
+from multiprocessing import Queue, Process
+from threading import Thread

 '''
 ---------------------Clean Mosaic---------------------
 '''
 def get_mosaic_positions(opt,netM,imagepaths,savemask=True):
-    # get mosaic position
+    # resume
+    continue_flag = False
+    if os.path.isfile(os.path.join(opt.temp_dir,'step.json')):
+        step = util.loadjson(os.path.join(opt.temp_dir,'step.json'))
+        resume_frame = int(step['frame'])
+        if int(step['step'])>2:
+            pre_positions = np.load(os.path.join(opt.temp_dir,'mosaic_positions.npy'))
+            return pre_positions
+        if int(step['step'])>=2 and resume_frame>0:
+            pre_positions = np.load(os.path.join(opt.temp_dir,'mosaic_positions.npy'))
+            continue_flag = True
+            imagepaths = imagepaths[resume_frame:]
+            
    positions = []
    t1 = time.time()
    if not opt.no_preview:
        cv2.namedWindow('mosaic mask', cv2.WINDOW_NORMAL)
+    print('Step:2/4 -- Find mosaic location')
+
+    img_read_pool = Queue(4)
+    def loader(imagepaths):
+        for imagepath in imagepaths:
+            img_origin = impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepath))
+            img_read_pool.put(img_origin)
+    t = Thread(target=loader,args=(imagepaths,))
+    t.setDaemon(True)
+    t.start()

-    print('Find mosaic location:')
    for i,imagepath in enumerate(imagepaths,1):
-        img_origin = impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepath))
+        img_origin = img_read_pool.get()
        x,y,size,mask = runmodel.get_mosaic_position(img_origin,netM,opt)
        positions.append([x,y,size])
        if savemask:
-            cv2.imwrite(os.path.join(opt.temp_dir+'/mosaic_mask',imagepath), mask)
-        
+            t = Thread(target=cv2.imwrite,args=(os.path.join(opt.temp_dir+'/mosaic_mask',imagepath), mask,))
+            t.start()
+        if i%1000==0:
+            save_positions = np.array(positions)
+            if continue_flag:
+                save_positions = np.concatenate((pre_positions,save_positions),axis=0)
+            np.save(os.path.join(opt.temp_dir,'mosaic_positions.npy'),save_positions)
+            step = {'step':2,'frame':i+resume_frame}
+            util.savejson(os.path.join(opt.temp_dir,'step.json'),step)
+
        #preview result and print
        if not opt.no_preview:
            cv2.imshow('mosaic mask',mask)
@@ -157,7 +68,12 @@ def get_mosaic_positions(opt,netM,imagepaths,savemask=True):
        cv2.destroyAllWindows()
    print('\nOptimize mosaic locations...')
    positions =np.array(positions)
+    if continue_flag:
+        positions = np.concatenate((pre_positions,positions),axis=0)
    for i in range(3):positions[:,i] = filt.medfilt(positions[:,i],opt.medfilt_num)
+    step = {'step':3,'frame':0}
+    util.savejson(os.path.join(opt.temp_dir,'step.json'),step)
+    np.save(os.path.join(opt.temp_dir,'mosaic_positions.npy'),positions)

    return positions

@@ -167,7 +83,7 @@ def cleanmosaic_img(opt,netG,netM):
    print('Clean Mosaic:',path)
    img_origin = impro.imread(path)
    x,y,size,mask = runmodel.get_mosaic_position(img_origin,netM,opt)
-    cv2.imwrite('./mask/'+os.path.basename(path), mask)
+    #cv2.imwrite('./mask/'+os.path.basename(path), mask)
    img_result = img_origin.copy()
    if size > 100 :
        img_mosaic = img_origin[y-size:y+size,x-size:x+size]
@@ -180,16 +96,30 @@ def cleanmosaic_img(opt,netG,netM):
        print('Do not find mosaic')
    impro.imwrite(os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_clean.jpg'),img_result)

+def cleanmosaic_img_server(opt,img_origin,netG,netM):
+    x,y,size,mask = runmodel.get_mosaic_position(img_origin,netM,opt)
+    img_result = img_origin.copy()
+    if size > 100 :
+        img_mosaic = img_origin[y-size:y+size,x-size:x+size]
+        if opt.traditional:
+            img_fake = runmodel.traditional_cleaner(img_mosaic,opt)
+        else:
+            img_fake = runmodel.run_pix2pix(img_mosaic,netG,opt)
+        img_result = impro.replace_mosaic(img_origin,img_fake,mask,x,y,size,opt.no_feather)
+    return img_result
+
 def cleanmosaic_video_byframe(opt,netG,netM):
    path = opt.media_path
-    fps,imagepaths = video_init(opt,path)[:2]
-    positions = get_mosaic_positions(opt,netM,imagepaths,savemask=True)
+    fps,imagepaths,height,width = video_init(opt,path)
+    start_frame = int(imagepaths[0][7:13])
+    positions = get_mosaic_positions(opt,netM,imagepaths,savemask=True)[(start_frame-1):]
+
    t1 = time.time()
    if not opt.no_preview:
        cv2.namedWindow('clean', cv2.WINDOW_NORMAL)

    # clean mosaic
-    print('Clean Mosaic:')
+    print('Step:3/4 -- Clean Mosaic:')
    length = len(imagepaths)
    for i,imagepath in enumerate(imagepaths,0):
        x,y,size = positions[i][0],positions[i][1],positions[i][2]
@@ -206,7 +136,9 @@ def cleanmosaic_video_byframe(opt,netG,netM):
                img_result = impro.replace_mosaic(img_origin,img_fake,mask,x,y,size,opt.no_feather)
            except Exception as e:
                print('Warning:',e)
-        cv2.imwrite(os.path.join(opt.temp_dir+'/replace_mosaic',imagepath),img_result)
+        t = Thread(target=cv2.imwrite,args=(os.path.join(opt.temp_dir+'/replace_mosaic',imagepath), img_result,))
+        t.start()
+        os.remove(os.path.join(opt.temp_dir+'/video2image',imagepath))
        
        #preview result and print
        if not opt.no_preview:
@@ -217,6 +149,7 @@ def cleanmosaic_video_byframe(opt,netG,netM):
    print()
    if not opt.no_preview:
        cv2.destroyAllWindows()
+    print('Step:4/4 -- Convert images to video')
    ffmpeg.image2video( fps,
                opt.temp_dir+'/replace_mosaic/output_%06d.'+opt.tempimage_type,
                opt.temp_dir+'/voice_tmp.mp3',
@@ -224,62 +157,93 @@ def cleanmosaic_video_byframe(opt,netG,netM):

 def cleanmosaic_video_fusion(opt,netG,netM):
    path = opt.media_path
-    N = 25
-    if 'HD' in os.path.basename(opt.model_path):
-        INPUT_SIZE = 256
-    else:
-        INPUT_SIZE = 128
+    N,T,S = 2,5,3
+    LEFT_FRAME = (N*S)
+    POOL_NUM = LEFT_FRAME*2+1
+    INPUT_SIZE = 256
+    FRAME_POS = np.linspace(0, (T-1)*S,T,dtype=np.int64)
+    img_pool = []
+    previous_frame = None
+    init_flag = True
+    
    fps,imagepaths,height,width = video_init(opt,path)
-    positions = get_mosaic_positions(opt,netM,imagepaths,savemask=True)
+    start_frame = int(imagepaths[0][7:13])
+    positions = get_mosaic_positions(opt,netM,imagepaths,savemask=True)[(start_frame-1):]
    t1 = time.time()
    if not opt.no_preview:
        cv2.namedWindow('clean', cv2.WINDOW_NORMAL)
    
    # clean mosaic
-    print('Clean Mosaic:')
+    print('Step:3/4 -- Clean Mosaic:')
    length = len(imagepaths)
-    img_pool = np.zeros((height,width,3*N), dtype='uint8')
-    mosaic_input = np.zeros((INPUT_SIZE,INPUT_SIZE,3*N+1), dtype='uint8')
+    write_pool = Queue(4)
+    show_pool = Queue(4)
+    def write_result():
+        while True:
+            save_ori,imagepath,img_origin,img_fake,x,y,size = write_pool.get()
+            if save_ori:
+                img_result = img_origin
+            else:
+                mask = cv2.imread(os.path.join(opt.temp_dir+'/mosaic_mask',imagepath),0)
+                img_result = impro.replace_mosaic(img_origin,img_fake,mask,x,y,size,opt.no_feather)
+            if not opt.no_preview:
+                show_pool.put(img_result.copy())
+            cv2.imwrite(os.path.join(opt.temp_dir+'/replace_mosaic',imagepath),img_result)
+            os.remove(os.path.join(opt.temp_dir+'/video2image',imagepath))
+    t = Thread(target=write_result,args=())
+    t.setDaemon(True)
+    t.start()
+
    for i,imagepath in enumerate(imagepaths,0):
        x,y,size = positions[i][0],positions[i][1],positions[i][2]
-        
+        input_stream = []
        # image read stream
-        mask = cv2.imread(os.path.join(opt.temp_dir+'/mosaic_mask',imagepath),0)
-        if i==0 :
-            for j in range(0,N):
-                img_pool[:,:,j*3:(j+1)*3] = impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepaths[np.clip(i+j-12,0,len(imagepaths)-1)]))
-        else:
-            img_pool[:,:,0:(N-1)*3] = img_pool[:,:,3:N*3]
-            img_pool[:,:,(N-1)*3:] = impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepaths[np.clip(i+12,0,len(imagepaths)-1)]))
-        img_origin = img_pool[:,:,int((N-1)/2)*3:(int((N-1)/2)+1)*3]
-        img_result = img_origin.copy()
+        if i==0 :# init
+            for j in range(POOL_NUM):
+                img_pool.append(impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepaths[np.clip(i+j-LEFT_FRAME,0,len(imagepaths)-1)])))
+        else: # load next frame
+            img_pool.pop(0)
+            img_pool.append(impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepaths[np.clip(i+LEFT_FRAME,0,len(imagepaths)-1)])))
+        img_origin = img_pool[LEFT_FRAME]
+
+        # preview result and print
+        if not opt.no_preview:
+            if show_pool.qsize()>3:   
+                cv2.imshow('clean',show_pool.get())
+                cv2.waitKey(1) & 0xFF

-        if size>100:
+        if size>50:
            try:#Avoid unknown errors
-                #reshape to network input shape
+                for pos in FRAME_POS:
+                    input_stream.append(impro.resize(img_pool[pos][y-size:y+size,x-size:x+size], INPUT_SIZE,interpolation=cv2.INTER_CUBIC)[:,:,::-1])
+                if init_flag:
+                    init_flag = False
+                    previous_frame = input_stream[N]
+                    previous_frame = data.im2tensor(previous_frame,bgr2rgb=True,gpu_id=opt.gpu_id)
                
-                mosaic_input[:,:,0:N*3] = impro.resize(img_pool[y-size:y+size,x-size:x+size,:], INPUT_SIZE)
-                mask_input = impro.resize(mask,np.min(img_origin.shape[:2]))[y-size:y+size,x-size:x+size]
-                mosaic_input[:,:,-1] = impro.resize(mask_input, INPUT_SIZE)
-
-                mosaic_input_tensor = data.im2tensor(mosaic_input,bgr2rgb=False,use_gpu=opt.use_gpu,use_transform = False,is0_1 = False)
-                unmosaic_pred = netG(mosaic_input_tensor)
-                img_fake = data.tensor2im(unmosaic_pred,rgb2bgr = False ,is0_1 = False)
-                img_result = impro.replace_mosaic(img_origin,img_fake,mask,x,y,size,opt.no_feather)        
+                input_stream = np.array(input_stream).reshape(1,T,INPUT_SIZE,INPUT_SIZE,3).transpose((0,4,1,2,3))
+                input_stream = data.to_tensor(data.normalize(input_stream),gpu_id=opt.gpu_id)
+                with torch.no_grad():
+                    unmosaic_pred = netG(input_stream,previous_frame)
+                img_fake = data.tensor2im(unmosaic_pred,rgb2bgr = True)
+                previous_frame = unmosaic_pred
+                write_pool.put([False,imagepath,img_origin.copy(),img_fake.copy(),x,y,size])
            except Exception as e:
-                print('Warning:',e)
-        cv2.imwrite(os.path.join(opt.temp_dir+'/replace_mosaic',imagepath),img_result)           
+                init_flag = True
+                print('Error:',e)
+        else:
+            write_pool.put([True,imagepath,img_origin.copy(),-1,-1,-1,-1])
+            init_flag = True
        
-        #preview result and print
-        if not opt.no_preview:
-            cv2.imshow('clean',img_result)
-            cv2.waitKey(1) & 0xFF
        t2 = time.time()
        print('\r',str(i+1)+'/'+str(length),util.get_bar(100*i/length,num=35),util.counttime(t1,t2,i+1,len(imagepaths)),end='')
    print()
+    write_pool.close()
+    show_pool.close()
    if not opt.no_preview:
        cv2.destroyAllWindows()
+    print('Step:4/4 -- Convert images to video')
    ffmpeg.image2video( fps,
                opt.temp_dir+'/replace_mosaic/output_%06d.'+opt.tempimage_type,
                opt.temp_dir+'/voice_tmp.mp3',
-                 os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_clean.mp4'))        
\ No newline at end of file
+                 os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_clean.mp4')) 
\ No newline at end of file
--- a/cores/init.py
+++ b/cores/init.py
+import os
+from util import util,ffmpeg
+
+'''
+---------------------Video Init---------------------
+'''
+def video_init(opt,path):
+    fps,endtime,height,width = ffmpeg.get_video_infos(path)
+    if opt.fps !=0:
+        fps = opt.fps
+
+    # resume
+    if os.path.isfile(os.path.join(opt.temp_dir,'step.json')):
+        step = util.loadjson(os.path.join(opt.temp_dir,'step.json'))
+        if int(step['step'])>=1:
+            choose = input('There is an unfinished video. Continue it? [y/n] ')
+            if choose.lower() =='yes' or choose.lower() == 'y':
+                imagepaths = os.listdir(opt.temp_dir+'/video2image')
+                imagepaths.sort()
+                return fps,imagepaths,height,width
+    
+    print('Step:1/4 -- Convert video to images')
+    util.file_init(opt)
+    ffmpeg.video2voice(path,opt.temp_dir+'/voice_tmp.mp3',opt.start_time,opt.last_time)
+    ffmpeg.video2image(path,opt.temp_dir+'/video2image/output_%06d.'+opt.tempimage_type,fps,opt.start_time,opt.last_time)
+    imagepaths = os.listdir(opt.temp_dir+'/video2image')
+    imagepaths.sort()
+    step = {'step':2,'frame':0}
+    util.savejson(os.path.join(opt.temp_dir,'step.json'),step)
+
+    return fps,imagepaths,height,width
\ No newline at end of file
--- a/cores/options.py
+++ b/cores/options.py
 import argparse
 import os
+import sys


 class Options():
@@ -10,10 +11,11 @@ class Options():
    def initialize(self):

        #base
-        self.parser.add_argument('--use_gpu', type=int,default=0, help='if -1, use cpu')
+        self.parser.add_argument('--debug', action='store_true', help='if specified, start debug mode')
+        self.parser.add_argument('--gpu_id', type=str,default='0', help='if -1, use cpu')
        self.parser.add_argument('--media_path', type=str, default='./imgs/ruoruo.jpg',help='your videos or images path')
        self.parser.add_argument('-ss', '--start_time', type=str, default='00:00:00',help='start position of video, default is the beginning of video')
-        self.parser.add_argument('-t', '--last_time', type=str, default='00:00:00',help='limit the duration of the video, default is the entire video')
+        self.parser.add_argument('-t', '--last_time', type=str, default='00:00:00',help='duration of the video, default is the entire video')
        self.parser.add_argument('--mode', type=str, default='auto',help='Program running mode. auto | add | clean | style')
        self.parser.add_argument('--model_path', type=str, default='./pretrained_models/mosaic/add_face.pth',help='pretrained model path')
        self.parser.add_argument('--result_dir', type=str, default='./result',help='output media will be saved here')
@@ -24,7 +26,7 @@ class Options():
        self.parser.add_argument('--fps', type=int, default=0,help='read and output fps, if 0-> origin')
        self.parser.add_argument('--no_preview', action='store_true', help='if specified,do not preview images when processing video. eg.(when run it on server)')
        self.parser.add_argument('--output_size', type=int, default=0,help='size of output media, if 0 -> origin')
-        self.parser.add_argument('--mask_threshold', type=int, default=64,help='threshold of recognize clean or add mosaic position 0~255')
+        self.parser.add_argument('--mask_threshold', type=int, default=64,help='Mosaic detection threshold (0~255). The smaller is it, the more likely judged as a mosaic area.')

        #AddMosaic
        self.parser.add_argument('--mosaic_mod', type=str, default='squa_avg',help='type of mosaic -> squa_avg | squa_random | squa_avg_circle_edge | rect_avg | random')
@@ -58,62 +60,71 @@ class Options():
        model_name = os.path.basename(self.opt.model_path)
        self.opt.temp_dir = os.path.join(self.opt.temp_dir, 'DeepMosaics_temp')

-        os.environ["CUDA_VISIBLE_DEVICES"] = str(self.opt.use_gpu)
-        import torch
-        if torch.cuda.is_available() and self.opt.use_gpu > -1:
-            pass
-        else:
-            self.opt.use_gpu = -1
+        if self.opt.gpu_id != '-1':
+            os.environ["CUDA_VISIBLE_DEVICES"] = str(self.opt.gpu_id)
+            import torch
+            if not torch.cuda.is_available():
+                self.opt.gpu_id = '-1'
+        # else:
+        #     self.opt.gpu_id = '-1'

        if test_flag:
            if not os.path.exists(self.opt.media_path):
-                print('Error: Bad media path!')
+                print('Error: Media does not exist!')
                input('Please press any key to exit.\n')
-                exit(0)
-
-        if self.opt.mode == 'auto':
-            if 'clean' in model_name or self.opt.traditional:
-                self.opt.mode = 'clean'
-            elif 'add' in model_name:
-                self.opt.mode = 'add'
-            elif 'style' in model_name or 'edges' in model_name:
-                self.opt.mode = 'style'
-            else:
-                print('Please input running model!')
-                input('Please press any key to exit.\n')
-                exit(0)
-
-        if self.opt.output_size == 0 and self.opt.mode == 'style':
-            self.opt.output_size = 512
-
-        if 'edges' in model_name or 'edges' in self.opt.preprocess:
-            self.opt.edges = True
-
-        if self.opt.netG == 'auto' and self.opt.mode =='clean':
-            if 'unet_128' in model_name:
-                self.opt.netG = 'unet_128'
-            elif 'resnet_9blocks' in model_name:
-                self.opt.netG = 'resnet_9blocks'
-            elif 'HD' in model_name and 'video' not in model_name:
-                self.opt.netG = 'HD'
-            elif 'video' in model_name:
-                self.opt.netG = 'video'
-            else:
-                print('Type of Generator error!')
+                sys.exit(0)
+            if not os.path.exists(self.opt.model_path):
+                print('Error: Model does not exist!')
                input('Please press any key to exit.\n')
-                exit(0)
-
-        if self.opt.ex_mult == 'auto':
-            if 'face' in model_name:
-                self.opt.ex_mult = 1.1
+                sys.exit(0)
+
+            if self.opt.mode == 'auto':
+                if 'clean' in model_name or self.opt.traditional:
+                    self.opt.mode = 'clean'
+                elif 'add' in model_name:
+                    self.opt.mode = 'add'
+                elif 'style' in model_name or 'edges' in model_name:
+                    self.opt.mode = 'style'
+                else:
+                    print('Please check model_path!')
+                    input('Please press any key to exit.\n')
+                    sys.exit(0)
+
+            if self.opt.output_size == 0 and self.opt.mode == 'style':
+                self.opt.output_size = 512
+
+            if 'edges' in model_name or 'edges' in self.opt.preprocess:
+                self.opt.edges = True
+
+            if self.opt.netG == 'auto' and self.opt.mode =='clean':
+                if 'unet_128' in model_name:
+                    self.opt.netG = 'unet_128'
+                elif 'resnet_9blocks' in model_name:
+                    self.opt.netG = 'resnet_9blocks'
+                elif 'HD' in model_name and 'video' not in model_name:
+                    self.opt.netG = 'HD'
+                elif 'video' in model_name:
+                    self.opt.netG = 'video'
+                else:
+                    print('Type of Generator error!')
+                    input('Please press any key to exit.\n')
+                    sys.exit(0)
+
+            if self.opt.ex_mult == 'auto':
+                if 'face' in model_name:
+                    self.opt.ex_mult = 1.1
+                else:
+                    self.opt.ex_mult = 1.5
            else:
-                self.opt.ex_mult = 1.5
-        else:
-            self.opt.ex_mult = float(self.opt.ex_mult)
-
-        if self.opt.mosaic_position_model_path == 'auto':
-            _path = os.path.join(os.path.split(self.opt.model_path)[0],'mosaic_position.pth')
-            self.opt.mosaic_position_model_path = _path
-            # print(self.opt.mosaic_position_model_path)
+                self.opt.ex_mult = float(self.opt.ex_mult)
+
+            if self.opt.mosaic_position_model_path == 'auto' and self.opt.mode == 'clean':
+                _path = os.path.join(os.path.split(self.opt.model_path)[0],'mosaic_position.pth')
+                if os.path.isfile(_path):
+                    self.opt.mosaic_position_model_path = _path
+                else:
+                    input('Please check mosaic_position_model_path!')
+                    input('Please press any key to exit.\n')
+                    sys.exit(0)

        return self.opt
\ No newline at end of file
--- a/cores/style.py
+++ b/cores/style.py
+import os
+import time
+import numpy as np
+import cv2
+from models import runmodel
+from util import mosaic,util,ffmpeg,filt
+from util import image_processing as impro
+from .init import video_init
+
+'''
+---------------------Style Transfer---------------------
+'''
+def styletransfer_img(opt,netG):
+    print('Style Transfer_img:',opt.media_path)
+    img = impro.imread(opt.media_path)
+    img = runmodel.run_styletransfer(opt, netG, img)
+    suffix = os.path.basename(opt.model_path).replace('.pth','').replace('style_','')
+    impro.imwrite(os.path.join(opt.result_dir,os.path.splitext(os.path.basename(opt.media_path))[0]+'_'+suffix+'.jpg'),img)
+
+def styletransfer_video(opt,netG):
+    path = opt.media_path
+    fps,imagepaths = video_init(opt,path)[:2]
+    print('Step:2/4 -- Transfer')
+    t1 = time.time()
+    if not opt.no_preview:
+        cv2.namedWindow('preview', cv2.WINDOW_NORMAL)
+    length = len(imagepaths)
+
+    for i,imagepath in enumerate(imagepaths,1):
+        img = impro.imread(os.path.join(opt.temp_dir+'/video2image',imagepath))
+        img = runmodel.run_styletransfer(opt, netG, img)
+        cv2.imwrite(os.path.join(opt.temp_dir+'/style_transfer',imagepath),img)
+        os.remove(os.path.join(opt.temp_dir+'/video2image',imagepath))
+        
+        #preview result and print
+        if not opt.no_preview:
+            cv2.imshow('preview',img)
+            cv2.waitKey(1) & 0xFF
+        t2 = time.time()
+        print('\r',str(i)+'/'+str(length),util.get_bar(100*i/length,num=35),util.counttime(t1,t2,i,len(imagepaths)),end='')
+    
+    print()
+    if not opt.no_preview:
+        cv2.destroyAllWindows()
+    suffix = os.path.basename(opt.model_path).replace('.pth','').replace('style_','')
+    print('Step:4/4 -- Convert images to video')
+    ffmpeg.image2video( fps,
+                opt.temp_dir+'/style_transfer/output_%06d.'+opt.tempimage_type,
+                opt.temp_dir+'/voice_tmp.mp3',
+                 os.path.join(opt.result_dir,os.path.splitext(os.path.basename(path))[0]+'_'+suffix+'.mp4')) 
\ No newline at end of file
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
+cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
+set(CMAKE_CXX_STANDARD 14)
+
+project(DeepMosaics)
+set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/lib) #链接库路径
+
+set(Torch_DIR /home/hypo/libtorch/share/cmake/Torch)
+find_package(Torch REQUIRED)
+
+set(OpenCV_DIR /home/hypo/opencv-4.4.0)
+find_package(OpenCV REQUIRED)
+
+# Add sub directories
+add_subdirectory(example)
+add_subdirectory(utils)
+
+# set_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD 14)
+# cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
+# project(main)
+# set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/lib) #链接库路径
+
+# set(Torch_DIR /home/hypo/libtorch/share/cmake/Torch)
+# find_package(Torch REQUIRED)
+
+# set(OpenCV_DIR /home/hypo/opencv-4.4.0)
+# find_package(OpenCV REQUIRED)
+
+# # 查找当前目录下的所有源文件
+# # 并将名称保存到 DIR_SRCS 变量
+# # aux_source_directory(. DIR_SRCS)
+# add_subdirectory(utils)
+
+# add_executable(main main.cpp)
+# # target_link_libraries(main )
+# # include_directories( "${OpenCV_INCLUDE_DIRS}" )
+# target_link_libraries( main "${TORCH_LIBRARIES}" "${OpenCV_LIBS}" utils)
+
+# set_property(TARGET main PROPERTY CXX_STANDARD 14)
--- a/cpp/README.md
+++ b/cpp/README.md
+### C++ version for DeepMosaics
+* I am learning c++ through this project...
+* It is under development...
\ No newline at end of file
--- a/cpp/example/CMakeLists.txt
+++ b/cpp/example/CMakeLists.txt
+# project(example)
+# add_executable("${PROJECT_NAME}" deepmosaic.cpp)
+# target_link_libraries(  "${PROJECT_NAME}" 
+#                         "${TORCH_LIBRARIES}" 
+#                         "${OpenCV_LIBS}"
+#                         utils)
+
+file(GLOB_RECURSE srcs RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "${CMAKE_CURRENT_SOURCE_DIR}/*.cpp")
+foreach(sourcefile IN LISTS srcs)
+    string( REPLACE ".cpp" "" binname ${sourcefile})
+    add_executable( ${binname} ${sourcefile} )
+    target_link_libraries(  ${binname} 
+                            "${TORCH_LIBRARIES}" 
+                            "${OpenCV_LIBS}"
+                            utils)
+    # set_property(TARGET ${binname} PROPERTY CXX_STANDARD 14)
+endforeach()
\ No newline at end of file
--- a/cpp/example/deepmosaic.cpp
+++ b/cpp/example/deepmosaic.cpp
+#include <stdio.h>
+#include <string.h>
+#include <unistd.h>
+#include <iostream>
+#include <list>
+#include <vector>
+
+#include <torch/script.h>
+#include <torch/torch.h>
+#include <opencv2/opencv.hpp>
+
+#include "data.hpp"
+#include "util.hpp"
+
+int main() {
+    std::string path = util::current_path();
+
+    std::string net_path = "../res/models/mosaic_position.pth";
+    std::string img_path = "../res/test_media/face/d.jpg";
+
+    cv::Mat img = cv::imread(img_path);
+    cv::resize(img, img, cv::Size(360, 360), 2);
+    // img.convertTo(img, CV_32F);
+    torch::Tensor img_tensor =
+        torch::from_blob(img.data, {1, img.rows, img.cols, 3}, torch::kByte);
+    img_tensor = img_tensor.permute({0, 3, 1, 2});
+    img_tensor = img_tensor.toType(torch::kFloat);
+    img_tensor = img_tensor.div(255);
+    std::cout << img_tensor.sizes() << "\n";
+
+    // end = clock();
+    // dur = (double)(end - start);
+    // printf("Use Time:%f\n", (dur / CLOCKS_PER_SEC));
+
+    // std::string net_path = "../res/models/mosaic_position.pt";
+    // torch::jit::script::Module net;
+    // try{
+    //     // if (!isfile(net_path)){
+    //     //     std::cerr<<"model does not exist\n";
+    //     // }
+
+    //     net = torch::jit::load(net_path);
+    // }
+    // catch(const std::exception& e){
+    //     std::cerr << "error loading the model\n";
+    //     return -1;
+    // }
+
+    // torch::Tensor example = torch::ones({1,3,360,360});
+    // torch::Tensor output = net.forward({example}).toTensor();
+    // std::cout<<"ok"<<std::endl;
+}
\ No newline at end of file
--- a/cpp/utils/CMakeLists.txt
+++ b/cpp/utils/CMakeLists.txt
+# Set the project name
+project (utils)
+aux_source_directory(./src DIR_LIB_SRCS)
+
+add_library(${PROJECT_NAME} SHARED ${DIR_LIB_SRCS})
+
+# 添加.h文件查找路径
+target_include_directories( ${PROJECT_NAME}
+    PUBLIC ${PROJECT_SOURCE_DIR}/include
+)
+
+# 添加掉用的第三方库
+target_link_libraries(  "${PROJECT_NAME}" 
+                        "${OpenCV_LIBS}")
\ No newline at end of file
--- a/cpp/utils/include/data.hpp
+++ b/cpp/utils/include/data.hpp
+#ifndef DATA_H
+#define DATA_H
+#include <opencv2/opencv.hpp>
+
+namespace data {
+void normalize(cv::Mat& matrix, double mean = 0.5, double std = 0.5);
+
+}  // namespace data
+
+#endif
\ No newline at end of file
--- a/cpp/utils/include/util.hpp
+++ b/cpp/utils/include/util.hpp
+#ifndef UTIL_H
+#define UTIL_H
+#include <iostream>
+#include <list>
+namespace util {
+
+class Timer {
+   private:
+    clock_t tstart, tend;
+
+   public:
+    void start();
+    void end();
+};
+
+// std::string path = util::current_path();
+std::string current_path();
+
+// std::string out = util::pathjoin({path, "b", "c"});
+std::string pathjoin(const std::list<std::string>& strs);
+
+bool isfile(const std::string& name);
+
+}  // namespace util
+
+#endif
\ No newline at end of file
--- a/cpp/utils/src/data.cpp
+++ b/cpp/utils/src/data.cpp
+#include "data.hpp"
+#include <opencv2/opencv.hpp>
+
+namespace data {
+void normalize(cv::Mat& matrix, double mean, double std) {
+    // matrix = (matrix / 255.0 - mean) / std;
+    matrix = matrix / (255.0 * std) - mean / std;
+}
+
+}  // namespace data
\ No newline at end of file
--- a/cpp/utils/src/util.cpp
+++ b/cpp/utils/src/util.cpp
+#include "util.hpp"
+#include <stdio.h>
+#include <sys/stat.h>
+#include <unistd.h>
+#include <iostream>
+#include <list>
+#include <vector>
+
+namespace util {
+
+void Timer::start() {
+    tstart = clock();
+}
+void Timer::end() {
+    tend = clock();
+    double dur;
+    dur = (double)(tend - tstart);
+    std::cout << "Cost Time:" << (dur / CLOCKS_PER_SEC) << "\n";
+}
+
+std::string current_path() {
+    char* buffer;
+    buffer = getcwd(NULL, 0);
+    return buffer;
+}
+
+std::string pathjoin(const std::list<std::string>& strs) {
+    std::string res = "";
+    int cnt = 0;
+    for (std::string s : strs) {
+        if (cnt == 0) {
+            res += s;
+        } else {
+            if (s[0] != '/') {
+                res += ("/" + s);
+            } else {
+                res += s;
+            }
+        }
+        cnt++;
+    }
+    return res;
+}
+
+bool isfile(const std::string& name) {
+    struct stat buffer;
+    return (stat(name.c_str(), &buffer) == 0);
+}
+}  // namespace util
\ No newline at end of file
--- a/deepmosaic.py
+++ b/deepmosaic.py
 import os
 import sys
 import traceback
-from cores import Options,core
-from util import util
-from models import loadmodel
+try:
+    from cores import Options,add,clean,style
+    from util import util
+    from models import loadmodel
+except Exception as e:
+    print(e)
+    input('Please press any key to exit.\n')
+    sys.exit(0)

 opt = Options().getparse(test_flag = True)
-util.file_init(opt)
+if not os.path.isdir(opt.temp_dir):
+    util.file_init(opt)

 def main():
    
@@ -19,11 +25,13 @@ def main():
        for file in files:
            opt.media_path = file
            if util.is_img(file):
-                core.addmosaic_img(opt,netS)
+                add.addmosaic_img(opt,netS)
            elif util.is_video(file):
-                core.addmosaic_video(opt,netS)
+                add.addmosaic_video(opt,netS)
+                util.clean_tempfiles(opt, tmp_init = False)
            else:
                print('This type of file is not supported')
+            util.clean_tempfiles(opt, tmp_init = False)

    elif opt.mode == 'clean':
        netM = loadmodel.bisenet(opt,'mosaic')
@@ -37,12 +45,13 @@ def main():
        for file in files:
            opt.media_path = file
            if util.is_img(file):
-                core.cleanmosaic_img(opt,netG,netM)
+                clean.cleanmosaic_img(opt,netG,netM)
            elif util.is_video(file):
                if opt.netG == 'video' and not opt.traditional:            
-                    core.cleanmosaic_video_fusion(opt,netG,netM)
+                    clean.cleanmosaic_video_fusion(opt,netG,netM)
                else:
-                    core.cleanmosaic_video_byframe(opt,netG,netM)
+                    clean.cleanmosaic_video_byframe(opt,netG,netM)
+                util.clean_tempfiles(opt, tmp_init = False)
            else:
                print('This type of file is not supported')

@@ -51,20 +60,35 @@ def main():
        for file in files:
            opt.media_path = file
            if util.is_img(file):
-                core.styletransfer_img(opt,netG)
+                style.styletransfer_img(opt,netG)
            elif util.is_video(file):
-                core.styletransfer_video(opt,netG)
+                style.styletransfer_video(opt,netG)
+                util.clean_tempfiles(opt, tmp_init = False)
            else:
                print('This type of file is not supported')

    util.clean_tempfiles(opt, tmp_init = False)
-        
+
 if __name__ == '__main__':
+    if opt.debug:
+        main()
+        sys.exit(0)
    try:
        main()
        print('Finished!')
    except Exception as ex:
        print('--------------------ERROR--------------------')
+        print('--------------Environment--------------')
+        print('DeepMosaics: 0.5.1')
+        print('Python:',sys.version)
+        import torch
+        print('Pytorch:',torch.__version__)
+        import cv2
+        print('OpenCV:',cv2.__version__)
+        import platform
+        print('Platform:',platform.platform())
+
+        print('--------------BUG--------------')
        ex_type, ex_val, ex_stack = sys.exc_info()
        print('Error Type:',ex_type)
        print(ex_val)
@@ -72,5 +96,4 @@ if __name__ == '__main__':
            print(stack)
        input('Please press any key to exit.\n')
        #util.clean_tempfiles(tmp_init = False)
-        exit(0)
-
+        sys.exit(0)
\ No newline at end of file
--- a/docs/Release_notes.txt
+++ b/docs/Release_notes.txt
-DeepMosaics V0.3.0
-Core program building with windows10_1703_x86_64 
- + python 3.68  
- + pyinstaller 3.5
+DeepMosaics: 0.5.1
+Core building with:
+    Python: 3.7.3 (default, Apr 24 2019, 15:29:51) [MSC v.1915 64 bit (AMD64)]
+    Pytorch: 1.7.1
+    OpenCV: 4.1.2
+    Platform: Windows-10-10.0.19041-SP0
+    Driver Version: 461.40
+    CUDA:11.0
 GUI building with C#
 For more detail, please view on github: https://github.com/HypoX64/DeepMosaics

 Releases History
- V0.3.0
-   1. Support BiSeNet(Better recognition of mosaics).
-   2. New videoHD model.
-   3. Better feathering method.
- V0.2.0
-   1. Add video model.
-   2. Now you can input chinese path
-   3. Support style transfer
-   4. Support fps limit
- V0.1.2
-   1. Support pix2pixHD model
- V0.1.1
-   1. Check path, can't input illegal path
- V0.1.0
-   1. Initial release.
\ No newline at end of file
+  V0.5.1
+    Fix:
+      1.Fix Some BUGs when restore unfinished tasks.
+      2.Fix that audio and video are not synchronized when the video is too long.
+    New:
+      1.Speed up video processing by Asynchronous.
+  V0.5.0
+    1.New video model (Perform better)
+  V0.4.1
+    1.Allow unfinished tasks to be restored.
+    2.Clean cache during processing.
+    3.Support CUDA 11.0.
+  V0.4.0
+    1.Support GPU.
+    2.Preview images when processing video.
+    3.Choose start position of video.
+  V0.3.0
+    1. Support BiSeNet(Better recognition of mosaics).
+    2. New videoHD model.
+    3. Better feathering method.
+  V0.2.0
+    1. Add video model.
+    2. Now you can input chinese path
+    3. Support style transfer
+    4. Support fps limit
+  V0.1.2
+    1. Support pix2pixHD model
+  V0.1.1
+    1. Check path, can't input illegal path
+  V0.1.0
+    1. Initial release.
\ No newline at end of file
--- a/docs/exe_help.md
+++ b/docs/exe_help.md
 ## DeepMosaics.exe  Instructions
-[[中文版]](./exe_help_CN.md)
+**[[中文版]](./exe_help_CN.md)**
 This is a GUI version compiled in Windows.<br>
 Download this version and pre-trained model via [[Google Drive]](https://drive.google.com/open?id=1LTERcN33McoiztYEwBxMuRjjgxh4DEPs)  [[百度云,提取码1x0a]](https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ) <br>
+Video tutorial => [[youtube]](https://www.youtube.com/watch?v=1kEmYawJ_vk) [[bilibili]](https://www.bilibili.com/video/BV1QK4y1a7Av)<br>
+
 Attentions:<br>

  - Require Windows_x86_64, Windows10 is better.<br>
@@ -9,11 +11,29 @@ Attentions:<br>
  - Run time depends on computer performance.<br>
  - If output video cannot be played, you can try with [potplayer](https://daumpotplayer.com/download/).<br>
  - GUI version update slower than source.<br>
+
+### How to install
+#### CPU version
+* 1.Download and install Microsoft Visual C++
+  https://aka.ms/vs/16/release/vc_redist.x64.exe
+#### GPU version
+Only suppport NVidia GPU above gtx1060(Driver:above 460 & CUDA:11.0)
+* 1.Download and install Microsoft Visual C++
+  https://aka.ms/vs/16/release/vc_redist.x64.exe
+* 2.Update your gpu drive to 460(or above)
+  https://www.nvidia.com/en-us/geforce/drivers/
+* 3.Download and install CUDA 11.0:
+  https://developer.nvidia.com/cuda-toolkit-archive
+
+You can also download them on BaiduNetdisk
+https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ
+Password: 1x0a
+
 ### How to use
 * step 1: Choose image or video.
 * step 2: Choose model(Different pre-trained models are suitable for different effects)
-* step3:  Run program and wait.
-* step4:  Cheek reult in './result'.
+* step 3: Run program and wait.
+* step 4: Cheek reult in './result'.

 ### Introduction to pre-trained models
 * Mosaic
@@ -22,10 +42,10 @@ Attentions:<br>
 | :------------------------------: | :---------------------------------------------------------: |
 |           add_face.pth           |          Add mosaic to all faces in images/videos.          |
 |        clean_face_HD.pth         | Clean mosaic to all faces in images/video.<br>(RAM > 8GB).  |
-|         add_youknow.pth          |      Add mosaic to all (FBI Warning) in images/videos.      |
-| clean_youknow_resnet_9blocks.pth |     Clean mosaic to all (FBI Warning) in images/videos.     |
-|     clean_youknow_video.pth      |        Clean mosaic to all (FBI Warning) in videos.         |
-|    clean_youknow_video_HD.pth    | Clean mosaic to all (FBI Warning) in videos.<br>(RAM > 8GB) |
+|         add_youknow.pth          |      Add mosaic to ... in images/videos.      |
+| clean_youknow_resnet_9blocks.pth |     Clean mosaic to ... in images/videos.     |
+|     clean_youknow_video.pth      |        Clean mosaic to ... in videos. It is better for processing video mosaics         |
+

 *  Style Transfer

@@ -50,8 +70,8 @@ Attentions:<br>
 * 7. More options can be input.
 * 8. Run program.
 * 9. Open help file.
-* 10.  Sponsor our project.
-* 11.  Version information.
+* 10. Sponsor our project.
+* 11. Version information.
 * 12. Open the URL on github.

 ### Introduction to options
@@ -60,7 +80,7 @@ If you need more effects,  use '--option your-parameters' to enter what you need

 |    Option    |                Description                 |                 Default                 |
 | :----------: | :----------------------------------------: | :-------------------------------------: |
-|  --use_gpu   |           if -1, do not use gpu            |                    0                    |
+|  --gpu_id   |           if -1, do not use gpu            |                    0                    |
 | --media_path |         your videos or images path         |            ./imgs/ruoruo.jpg            |
 |    --mode    | program running mode(auto/clean/add/style) |                 'auto'                  |
 | --model_path |           pretrained model path            | ./pretrained_models/mosaic/add_face.pth |

--- a/docs/exe_help_CN.md
+++ b/docs/exe_help_CN.md
 ## DeepMosaics.exe  使用说明
 下载程序以及预训练模型 [[Google Drive]](https://drive.google.com/open?id=1LTERcN33McoiztYEwBxMuRjjgxh4DEPs)  [[百度云,提取码1x0a]](https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ) <br>
+[视频教程](https://www.bilibili.com/video/BV1QK4y1a7Av)<br>
+
 注意事项:<br>

-  - 程序的运行要求在64位Windows操作系统,我仅在Windows10运行过,其他版本暂未经过测试<br>
+
+  - 程序的运行要求在64位Windows操作系统,我们仅在Windows10运行过,其他版本暂未经过测试<br>
  - 请根据需求选择合适的预训练模型进行测试<br>
-  - 运行时间取决于电脑性能,对于视频文件,我们建议使用源码以及GPU运行<br>
+  - 运行时间取决于电脑性能,对于视频文件,我们建议使用GPU运行<br>
  - 如果输出的视频无法播放,这边建议您尝试[potplayer](https://daumpotplayer.com/download/).<br>
  - 相比于源码,该版本的更新将会延后.

+### 如何安装
+#### CPU version
+* 1.下载安装 Microsoft Visual C++
+  https://aka.ms/vs/16/release/vc_redist.x64.exe
+#### GPU version
+仅支持gtx1060及以上的NVidia显卡(要求460版本以上的驱动以及11.0版本的CUDA, 注意只能是11.0)
+* 1.Download and install Microsoft Visual C++
+  https://aka.ms/vs/16/release/vc_redist.x64.exe
+* 2.Update your gpu drive to 460(or above)
+  https://www.nvidia.com/en-us/geforce/drivers/
+* 3.Download and install CUDA 11.0:
+  https://developer.nvidia.com/cuda-toolkit-archive
+
+当然这些也能在百度云上下载
+https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ
+提取码: 1x0a
+
 ### 如何使用
+
 * step 1: 选择需要处理的图片或视频
 * step 2: 选择预训练模型(不同的预训练模型有不同的效果)
-* step3:  运行程序并等待
-* step4:  查看结果(储存在result文件夹下)
+* step 3: 运行程序并等待
+* step 4: 查看结果(储存在result文件夹下)

 ## 预训练模型说明
 当前的预训练模型分为两类——添加/移除马赛克以及风格转换.
@@ -23,10 +44,10 @@
 | :------------------------------: | :-------------------------------------------: |
 |           add_face.pth           |           对图片或视频中的脸部打码            |
 |        clean_face_HD.pth         | 对图片或视频中的脸部去码<br>(要求内存 > 8GB). |
-|         add_youknow.pth          |        对图片或视频中的十八禁内容打码         |
-| clean_youknow_resnet_9blocks.pth |        对图片或视频中的十八禁内容去码         |
-|     clean_youknow_video.pth      |           对视频中的十八禁内容去码            |
-|    clean_youknow_video_HD.pth    | 对视频中的十八禁内容去码<br>(要求内存 > 8GB)  |
+|         add_youknow.pth          |        对图片或视频中的...内容打码         |
+| clean_youknow_resnet_9blocks.pth |        对图片或视频中的...内容去码         |
+|     clean_youknow_video.pth      |           对视频中的...内容去码,推荐使用带有'video'的模型去除视频中的马赛克            |
+

 * 风格转换

@@ -52,8 +73,8 @@
 * 7. 自行输入更多参数，详见下文
 * 8. 运行
 * 9. 打开帮助文件
-* 10.  支持我们
-* 11.  版本信息
+* 10. 支持我们
+* 11. 版本信息
 * 12. 打开项目的github页面

 ### 参数说明
@@ -62,7 +83,7 @@

 |    选项    |        描述         |                 默认                 |
 | :----------: | :------------------------: | :-------------------------------------: |
-|  --use_gpu   |   if -1, do not use gpu    |                    0                    |
+|  --gpu_id   |   if -1, do not use gpu    |                    0                    |
 | --media_path | 需要处理的视频或者照片的路径 |            ./imgs/ruoruo.jpg            |
 |    --mode    |    运行模式(auto/clean/add/style)    |                 'auto'                  |
 | --model_path |   预训练模型的路径    | ./pretrained_models/mosaic/add_face.pth |
@@ -75,7 +96,7 @@
 | --mosaic_mod | 马赛克类型 -> squa_avg/ squa_random/ squa_avg_circle_edge/ rect_avg/random |                    squa_avg                    |
 | --mosaic_size | 马赛克大小，０则为自动 |            0            |
 |    --mask_extend    |    拓展马赛克区域    |         10  |
-| --mask_threshold | 马赛克区域识别阈值 0~255 | 64 |
+| --mask_threshold | 马赛克区域识别阈值 0~255,越小越容易被判断为马赛克区域 | 64 |

 * 去除马赛克


--- a/docs/options_introduction.md
+++ b/docs/options_introduction.md
@@ -5,7 +5,7 @@ If you need more effects,  use '--option your-parameters' to enter what you need

 |    Option    |        Description         |                 Default                 |
 | :----------: | :------------------------: | :-------------------------------------: |
-|  --use_gpu   |   if -1, do not use gpu    |                    0                    |
+|  --gpu_id   |   if -1, do not use gpu    |                    0                    |
 | --media_path | your videos or images path |            ./imgs/ruoruo.jpg            |
 | --start_time | start position of video, default is the beginning of video | '00:00:00' |
 | --last_time | limit the duration of the video, default is the entire video | '00:00:00' |

--- a/docs/options_introduction_CN.md
+++ b/docs/options_introduction_CN.md
@@ -5,7 +5,7 @@

 |    选项    |        描述         |                 默认                 |
 | :----------: | :------------------------: | :-------------------------------------: |
-|  --use_gpu   |   if -1, do not use gpu    |                    0                    |
+|  --gpu_id   |   if -1, do not use gpu    |                    0                    |
 | --media_path | 需要处理的视频或者照片的路径 |            ./imgs/ruoruo.jpg            |
 | --start_time | 视频开始处理的位置，默认从头开始 | '00:00:00' |
 | --last_time | 处理的视频时长，默认是整个视频 | '00:00:00' |

--- a/docs/pre-trained_models_introduction.md
+++ b/docs/pre-trained_models_introduction.md
@@ -10,8 +10,8 @@ Download  pre-trained model via [[Google Drive]](https://drive.google.com/open?i
 |        clean_face_HD.pth         | Clean mosaic to  faces in images/video.<br>(RAM > 8GB). |
 |         add_youknow.pth          |          Add mosaic to  ... in images/videos.           |
 | clean_youknow_resnet_9blocks.pth |         Clean mosaic to  ... in images/videos.          |
-|     clean_youknow_video.pth      |             Clean mosaic to  ... in videos.             |
-|    clean_youknow_video_HD.pth    |     Clean mosaic to  ... in videos.<br>(RAM > 8GB)      |
+|     clean_youknow_video.pth      |             Clean mosaic to  ... in videos. It is better for processing video mosaics              |
+

 ### Style Transfer


--- a/docs/pre-trained_models_introduction_CN.md
+++ b/docs/pre-trained_models_introduction_CN.md
@@ -10,8 +10,8 @@
 |        clean_face_HD.pth         | 对图片或视频中的脸部去码<br>(要求内存 > 8GB). |
 |         add_youknow.pth          |          对图片或视频中的...内容打码          |
 | clean_youknow_resnet_9blocks.pth |          对图片或视频中的...内容去码          |
-|     clean_youknow_video.pth      |             对视频中的...内容去码             |
-|    clean_youknow_video_HD.pth    |   对视频中的...内容去码<br>(要求内存 > 8GB)   |
+|     clean_youknow_video.pth      |             对视频中的...内容去码,推荐使用带有'video'的模型去除视频中的马赛克               |
+

 ### 风格转换


--- a/docs/training_with_your_own_dataset.md
+++ b/docs/training_with_your_own_dataset.md
@@ -10,7 +10,11 @@ We will make "face" as an example. If you don't have any picture, you can downlo
  - [Pytorch 1.0+](https://pytorch.org/)
  - NVIDIA GPU(with more than 6G memory) + CUDA CuDNN<br>
 #### Dependencies
-This code depends on opencv-python, torchvision, matplotlib available via pip install.
+This code depends on opencv-python, torchvision, matplotlib, tensorboardX, scikit-image available via conda install.
+```bash
+# or
+pip install -r requirements.txt
+```
 #### Clone this repo
 ```bash
 git clone https://github.com/HypoX64/DeepMosaics
@@ -32,31 +36,31 @@ python draw_mask.py --datadir 'dir for your pictures' --savedir ../datasets/draw
 python get_image_from_video.py --datadir 'dir for your videos' --savedir ../datasets/video2image --fps 1
 ```
 ### Clean mosaic dataset
-We provide several methods for generating clean mosaic datasets. However, for better effect, we recommend train a addmosaic model in a small data  first and use it to automatically generate datasets in a big data.(recommend: Method 2(for image) & Method 4(for video))
-* Method 1: Use drawn mask to make pix2pix(HD) datasets(Require``` origin_image``` and ```mask```)
+We provide several methods for generating clean mosaic datasets. However, for better effect, we recommend train a addmosaic model in a small data  first and use it to automatically generate datasets in a big data. (recommend: Method 2(for image) & Method 4(for video))
+* Method 1: Use drawn mask to make pix2pix(HD) datasets   (Require``` origin_image``` and ```mask```)
 ```bash
 python make_pix2pix_dataset.py --datadir ../datasets/draw/face --hd --outsize 512 --fold 1 --name face --savedir ../datasets/pix2pix/face --mod drawn --minsize 128 --square
 ```
-* Method 2: Use addmosaic model to make pix2pix(HD) datasets(Require addmosaic pre-trained model)
+* Method 2: Use addmosaic model to make pix2pix(HD) datasets (Require addmosaic pre-trained model)
 ```bash
 python make_pix2pix_dataset.py --datadir 'dir for your pictures' --hd --outsize 512 --fold 1 --name face --savedir ../datasets/pix2pix/face --mod network --model_path ../pretrained_models/mosaic/add_face.pth --minsize 128 --square --mask_threshold 128
 ```
-* Method 3: Use Irregular Masks to make pix2pix(HD) datasets(Require [Irregular Masks](https://nv-adlr.github.io/publication/partialconv-inpainting))
+* Method 3: Use Irregular Masks to make pix2pix(HD) datasets (Require [Irregular Masks](https://nv-adlr.github.io/publication/partialconv-inpainting))
 ```bash
 python make_pix2pix_dataset.py --datadir 'dir for your pictures' --hd --outsize 512 --fold 1 --name face --savedir ../datasets/pix2pix/face --mod irregular --irrholedir ../datasets/Irregular_Holes_mask --square
 ```
-* Method 4: Use addmosaic model to make video datasets(Require addmosaic pre-trained model. This is better for processing video mosaics)
+* Method 4: Use addmosaic model to make video datasets (Require addmosaic pre-trained model. This is better for processing video mosaics)
 ```bash
-python make_video_dataset.py --datadir 'dir for your videos' --model_path ../pretrained_models/mosaic/add_face.pth --mask_threshold 96 --savedir ../datasets/video/face
+python make_video_dataset.py --model_path ../pretrained_models/mosaic/add_face.pth --gpu_id 0 --datadir 'dir for your videos' --savedir ../datasets/video/face
 ```
 ## Training
 ### Add
 ```bash
 cd train/add
-python train.py --use_gpu 0 --dataset ../../datasets/draw/face --savename face --loadsize 512 --finesize 360 --batchsize 16
+python train.py --gpu_id 0 --dataset ../../datasets/draw/face --savename face --loadsize 512 --finesize 360 --batchsize 16
 ```
 ### Clean
-* For image datasets(generated by ```make_pix2pix_dataset.py```)
+* For image datasets (generated by ```make_pix2pix_dataset.py```)
 We use [pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) or [pix2pixHD](https://github.com/NVIDIA/pix2pixHD) to train model. We just take pix2pixHD as an example.
 ```bash
 git clone https://github.com/NVIDIA/pix2pixHD
@@ -64,10 +68,10 @@ cd pix2pixHD
 pip install dominate
 python train.py --name face --resize_or_crop resize_and_crop --loadSize 563 --fineSize 512 --label_nc 0 --no_instance --dataroot ../datasets/pix2pix/face
 ```
-* For video datasets(generated by ```make_video_dataset.py```)
+* For video datasets (generated by ```make_video_dataset.py```)
 ```bash
 cd train/clean
-python train.py --dataset ../../datasets/video/face --savename face --savefreq 100000 --gan --hd --lr 0.0002 --lambda_gan 1 --use_gpu 0
+python train.py --dataset ../../datasets/video/face --savename face --n_blocks 4 --lambda_GAN 0.01 --loadsize 286 --finesize 256 --batchsize 16 --n_layers_D 2 --num_D 3 --n_epoch 200 --gpu_id 4,5,6,7 --load_thread 16
 ```
 ## Testing
-Put saved network to ```./pretrained_models/mosaic/``` and rename it as ```add_face.pth``` or ```clean_face_HD.pth``` or ```clean_face_video_HD.pth```
+Put saved network to ```./pretrained_models/mosaic/``` and rename it as ```add_face.pth``` or ```clean_face_HD.pth``` or ```clean_face_video_HD.pth```and then run ```deepmosaic.py --model_path ./pretrained_models/mosaic/your_model_name```
--- a/imgs/icon.jpg
+++ b/imgs/icon.jpg
--- a/imgs/logo.ico
+++ b/imgs/logo.ico
--- a/imgs/logo.png
+++ b/imgs/logo.png
--- a/imgs/logo_withwords.png
+++ b/imgs/logo_withwords.png
--- a/make_datasets/csv/stars_name.csv
+++ b/make_datasets/csv/stars_name.csv
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华,张钧甯,林子闳,马振桓,宋芸桦,邵雨薇,任言恺,欧阳娜娜,宇宙,连晨翔,瑞莎,昆凌,杜天皓,芮娜,苏笠汶,李玉玺,李威,明道,梁又琳,游智翔,寇世勋,雨婷,沈建宏,姚蜜,萧敬腾,向以丞,黄心娣,柯佳嬿,殷悦,邵崇柏,余晋,高以翔,黄郁婷,谢依霖,潘仪君,苏见信,张熙恩,石知田,柯宇纶,寇家瑞,陈威翰,柯家豪,康茵茵,康康,柯雅馨,王耀庆,王大陆,郭采洁,文雨非,蔡芷纭,陈奕,简远信,文淇,李凯馨,陈佩骐,黄维德,张轩睿,蔡颐榛,徐洁儿,王雅慧,陈柏融,王陈怡娴,邱胜翊,陈楚河,戴君竹,Teddy,锦荣,黄伟晋,张震,金士杰,吴中天,陈柏霖,郭雪芙,张庭,凤小岳,方妍心,林柏宏,欧阳妮妮,李鸿其,谢翔雅,杜妍,刘德凯,江语晨,安钧璨,李立群,白歆惠,陈薇,黄柏钧,许名杰,田中千绘,郑靓歆,那维勋,程席榆,纪欣妤,胡因梦,田丽,何海东,林佑威,陈怡真,陈盈燕,郭品超,黄文豪,小甜甜,吴若瑄,邱心志,林宥嘉,安心亚,韩忠羽,倪安东,修杰楷,常铖,禾浩辰,王轶玲,陈德修,倪齐民,易柏辰,丁文琪,李又麟,官鸿,洪小铃,王以纶,李程彬,屈中恒,王阳明,王棠云,葛蕾,郝劭文,邱昊奇,李天柱,狄志杰,陈亦飞,林韦君,温升豪,桂纶镁,张皓明,郑开元,黄少祺,陈敬宣,何恋慈,潘迎紫,阿本,方芳,张立昂,庄濠全,归亚蕾,王思懿,李毓芬,杜姸,张天霖,许玮甯,包小柏,关颖,苟芸慧,简廷芮,杨丽菁,陈庭妮,陈匡怡,魏蔓,张玉嬿,谢欣颖,陈博正,徐嘉苇,邓美恩,陈艾熙,郭书瑶,高凌风,谢和弦,李铨,徐贵樱,许富翔,张榕容,陈玺安,赖煜哲,金燕玲,亚里,连静雯,张国柱,许雅钧,白梓轩,杨世瀚,刘以豪,戴立忍,谢祖武,张孝全,夏若妍,唐国忠,陈乃荣,易恩,秦杨,王宥胜,蔡淑臻,高捷,小煜,刘乐妍,王维琳,刘瑞琪,张睿家,林逸欣,曾佩瑜,郭思辰,安娜,杜孟竹,吴可熙,陈汉典,黄瀞怡,蔡黄汝,叶全真,安晨芯,秦汉,纪亚文,吴心缇,陈尚泽,曲澔浚,李依瑾,陈艾琳,陈语安,纪培慧,立威廉,猪哥亮,杨青倩,陈建豪,是元介,刘冠毅,谢盈萱,郭鑫,孙克杰,张勋杰,张静懿,陈庭萱,俞小凡,詹子晴,郑暐达,风田,赖宗赢,曹华恩,张行,蔡旻佑,张景岚,江宏恩,孟庭丽,宋新妮,刘奕儿,王诗安,海狗,宋达民,吕孔维,蔡维泽,杨谨华,周凯文,卓毓彤,朱主爱,庹宗华,姚凤凤,刘国劭,孙兴,王心嫚,田家达,夏台凤,姚元浩,萧蔷,席惟伦,陈羽凡,陈昭荣,周子瑜,金超群,谢坤达,傅雷,姬天语,费翔,阿喜,周丹莉,李国超,沈彦廷,沈海蓉,曾子余,潘慧如,李志希,黄牛,黄丽玲,陶嫚曼,蓝心湄,纪言恺,刘俊纬,黄立行,方志友,吴思贤,宫以腾,包小松,徐佳莹,杨千霈,陈景炀,胡玮杰,龙隆,李运庆,关诗敏,刘泯廷,许韶洋,谢语恩,施羽,王家梁,谭艾珍,王彩桦,龚继安,艾伟,邓丽君,徐敏,钱韦杉,吴辰君,杨贵媚,安东尼,王灿,林若亚,Selina,张瑀希,周绍栋,刘汉强,古斌,阿雅,郭源元,贺一航,吕雪凤,唐治平,林美秀,莫允雯,李元太,游艾迪,安哲,张佩华,夏靖庭,唐德惠,侯彦西,李罗,周俐葳,陈冠霖,龚海铭,张芯瑜,许雅涵,刘芳芸,BY2,蔡健雅,李维维,李沛旭,许光汉,简嫚书,陈为民,李国毅,林妍柔,涂世旻,林予晞,杨升达,辰烨,李佳颖,周孝安,林秀君,秦风,邓九云,谢雅琳,舒子晨,翁卡特,夏语心,傅传杰,黄腾浩,胡睿儿,陈若萍,张雁名,阿信,李妍憬,翁滋蔓,大飞,江淑娜,蓝正龙,林筱筠,梁如瑄,阿Ken,盛鉴,赵擎,素珠,郑家榆,郑人硕,李康宜,林芯蕾,周丹薇,杨琪,安佑德,黄裕翔,周咏轩,蔡昌宪,钟欣凌,安程希,张立威,郎祖筠,刘谦,司徒颖霜,丁当,陈武康,喻虹渊,王者心,卫子云,迪丽热巴,成毅,陈学冬,安悦溪,姜妍,杨紫,郑爽,关晓彤,何明翰,徐梵溪,李沁,宋轶,乔欣,王鸥,古力娜扎,张馨予,麦迪娜,张铭恩,张檬,吴优,尹正,罗云熙,陈瑶,侯明昊,蒋欣,张云龙,杨烁,胡冰卿,靳东,牟星,袁姗姗,张彬彬,罗晋,秦俊杰,赵圆瑗,唐艺昕,李纯,梁振伦,吴倩,米露,王子文,TFBOYS,陈晓,张若昀,晁然,陈梦希,乔任梁,孙佳雨,李宏毅,林秋楠,茅子俊,唐娜,赖雨蒙,马思纯,于朦胧,徐璐,宋茜,杨菲洋,阚清子,孙耀琦,张萌,田倚凡,徐海乔,王妍之,戴向宇,刘芊含,熊梓淇,蔡文静,杨蓉,王珊,李心艾,何杜娟,王洋,井柏然,张雪迎,赵韩樱子,曹曦月,姜潮,孙怡,周冬雨,米勒,焦俊艳,杨子姗,逯恣祯,李溪芮,刘美含,刘萌萌,景甜,李一桐,杨旭文,梁洁,柳希龙,郑业成,李晟,陈小纭,黄礼格,林允,徐冬冬,宋威龙,袁冰妍,付辛博,沈梦辰,陈钰琪,方文强,鄂博,阎汶宣,牛骏峰,李小萌,白雪,张哲瀚,鞠婧祎,吕佳容,张浩,施诗,刘敏涛,王亭文,范世錡,海陆,马薇,邢菲,郭俊辰,孔垂楠,施予斐,穆婷婷,盛一伦,任天野,杨舒,刘庭羽,严屹宽,金晨,王晓晨,李砚,尹淇,韩栋,康杰,韩东君,于晓光,陈洁,张维娜,杨之楹,郑恺,蒋蕊泽,林源,张芷溪,苏青,蓝波,杨志刚,白百何,李佳奇,晏紫东,李健,高伟光,李进荣,张子枫,孙骁骁,米热,于震,宋小宝,刘芮麟,宋祖儿,李兰迪,康磊,叶祖新,于毅,蒋依依,李林娟,刘波,胡一天,石悦安鑫,娄清,刘娜萍,秦语,刘楚恬,王千源,阮圣文,孙雪宁,李倩,祝绪丹,母其弥雅,刘雅瑟,应昊茗,刘启恒,李梦,宋妍霏,金瀚,尹智玄,虞朗,袁志博,刘敏,任重,吴昊宸,孙艺洲,刘笑歌,毛林林,何泓姗,李郁瞳,袁泉,宋佳,马蜀君,尤靖茹,聂子皓,尚语贤,俞灏明,黄轩,吕卓燃,李飞,周奇奇,张予曦,刘文曲,刘秋实,李蓓蕾,潘之琳,梁超,泓萱,苗驰,卢宇静,童菲,王美人,刘颖伦,熊乃瑾,张碧晨,刘黛希,马秋子,刘烨,宋家腾,买红妹,祖峰,瑛子,江珊,杨雨婷,刘成瑞,李金铭,柯蓝,艾晓琪,刘馨棋,孙铱,白宇,杜淳,马春瑞,雨婷儿,杨帆,隋雨蒙,李颖,何晟铭,梁亦芸,陆子艺,张雨绮,于月仙,孙坚,岳丽娜,袁文康,梁浩,盛冠森,李春嫒,阳蕾,刘恩佑,杨梓墨,李子峰,吴谨言,张睿,孙茜,李茂,荣梓杉,陆妍淇,荣蓉,任帅,黄一琳,原雨,殷桃,辛芷蕾,翟天临,李欣聪,张晓谦,李雨轩,王珂,刘智扬,张楷依,于函冰,柴碧云,刘倩文,于小彤,尹铸胜,于笑,吉克隽逸,贾乃亮,童苡萱,宋奕星,刘雨欣,刘琳,姜鸿,安泳畅,王乐君,杨曦,于越,张晓晨,袁雨萱,马丽,林思意,陆昱霖,宋伊人,甘婷婷,刘天佐,李程,林静,李明德,林永健,冉旭,苏倩薇,叶峰,任柯诺,刘芸,叶青,马程程,卢杉,刘恬汝,丁婷婷,梁晶晶,李乃文,季晨,梁林琳,林江国,杀漠,李帅,张梦露,张峻宁,刘冠翔,刘丛丹,尹馨梓,路晨,米紫安,杨昆,李呈媛,徐申东,虞书欣,余心恬,梁爱琪,申奥,林千鹿,李昕岳,李萌萌,沈航,刘潺,穆乐恩,王彦霖,周雨彤,侍宣如,李嘉雯,王闯,李泽,余皑磊,苑子艺,赵予熙,李芯逸,刘钇彤,侯佩杉,侯梦瑶,姚奕辰,石安妮,施潮锦子,井星文,马晓伟,马灿灿,隋咏良,楼佳悦,陈思成,岳旸,马靓,陈都灵,李茜,卢星宇,李一情,何穗,李超,张炯敏,沙溢,王挺,SNH48,陆骏瑶,陈雅婷,李依伊
--- a/make_datasets/make_pix2pix_dataset.py
+++ b/make_datasets/make_pix2pix_dataset.py
@@ -16,7 +16,7 @@ import torch

 from models import runmodel,loadmodel
 import util.image_processing as impro
-from util import util,mosaic,data
+from util import degradater, util,mosaic,data


 opt.parser.add_argument('--datadir',type=str,default='../datasets/draw/face', help='')
@@ -87,11 +87,11 @@ for fold in range(opt.fold):
                mask = mask_drawn
            if 'irregular' in opt.mod:
                mask_irr = impro.imread(irrpaths[random.randint(0,12000-1)],'gray')
-                mask_irr = data.random_transform_single(mask_irr, (img.shape[0],img.shape[1]))
+                mask_irr = data.random_transform_single_mask(mask_irr, (img.shape[0],img.shape[1]))
                mask = mask_irr
            if 'network' in opt.mod:
                mask_net = runmodel.get_ROI_position(img,net,opt,keepsize=True)[0]
-                if opt.use_gpu != -1:
+                if opt.gpu_id != -1:
                    torch.cuda.empty_cache()
                if not opt.all_mosaic_area:
                    mask_net = impro.find_mostlikely_ROI(mask_net)
@@ -107,11 +107,11 @@ for fold in range(opt.fold):

                saveflag = True
                if opt.mod == ['drawn','irregular']:
-                    x,y,size,area = impro.boundingSquare(mask_drawn, random.uniform(1.2,1.6))
+                    x,y,size,area = impro.boundingSquare(mask_drawn, random.uniform(1.1,1.6))
                elif opt.mod == ['network','irregular']:
-                    x,y,size,area = impro.boundingSquare(mask_net, random.uniform(1.2,1.6))
+                    x,y,size,area = impro.boundingSquare(mask_net, random.uniform(1.1,1.6))
                else:
-                    x,y,size,area = impro.boundingSquare(mask, random.uniform(1.2,1.6))
+                    x,y,size,area = impro.boundingSquare(mask, random.uniform(1.1,1.6))

                if area < 1000:
                    saveflag = False
@@ -130,11 +130,15 @@ for fold in range(opt.fold):
                if saveflag:
                    # add mosaic
                    img_mosaic = mosaic.addmosaic_random(img, mask)
-                    # random blur
+                    # random degradater
                    if random.random()>0.5:
-                        Q = random.randint(1,15)
-                        img = impro.dctblur(img,Q)
-                        img_mosaic = impro.dctblur(img_mosaic,Q)
+                        degradate_params = degradater.get_random_degenerate_params(mod='weaker_2')
+                        img = degradater.degradate(img,degradate_params)
+                        img_mosaic = degradater.degradate(img_mosaic,degradate_params)
+                    # if random.random()>0.5:
+                    #     Q = random.randint(1,15)
+                    #     img = impro.dctblur(img,Q)
+                    #     img_mosaic = impro.dctblur(img_mosaic,Q)

                    savecnt += 1


--- a/make_datasets/make_video_dataset.py
+++ b/make_datasets/make_video_dataset.py
@@ -14,7 +14,7 @@ import torch

 from models import runmodel,loadmodel
 import util.image_processing as impro
-from util import util,mosaic,data,ffmpeg
+from util import filt, util,mosaic,data,ffmpeg


 opt.parser.add_argument('--datadir',type=str,default='your video dir', help='')
@@ -56,6 +56,7 @@ for videopath in videopaths:
                ffmpeg.video2image(videopath, opt.temp_dir+'/video2image/%05d.'+opt.tempimage_type,fps=1,
                    start_time = util.second2stamp(cut_point*opt.interval),last_time = util.second2stamp(opt.time))
                imagepaths = util.Traversal(opt.temp_dir+'/video2image')
+                imagepaths = sorted(imagepaths)
                cnt = 0 
                for i in range(opt.time):
                    img = impro.imread(imagepaths[i])
@@ -92,30 +93,65 @@ for videopath in videopaths:
            imagepaths = util.Traversal(opt.temp_dir+'/video2image')
            imagepaths = sorted(imagepaths)
            imgs=[];masks=[]
-            mask_flag = False
-
+            # mask_flag = False
+            # for imagepath in imagepaths:
+            #     img = impro.imread(imagepath)
+            #     mask = runmodel.get_ROI_position(img,net,opt,keepsize=True)[0]
+            #     imgs.append(img)
+            #     masks.append(mask)
+            #     if not mask_flag:
+            #         mask_avg = mask.astype(np.float64)
+            #         mask_flag = True
+            #     else:
+            #         mask_avg += mask.astype(np.float64)
+
+            # mask_avg = np.clip(mask_avg/len(imagepaths),0,255).astype('uint8')
+            # mask_avg = impro.mask_threshold(mask_avg,20,64)
+            # if not opt.all_mosaic_area:
+            #     mask_avg = impro.find_mostlikely_ROI(mask_avg)
+            # x,y,size,area = impro.boundingSquare(mask_avg,Ex_mul=random.uniform(1.1,1.5))
+            
+            # for i in range(len(imagepaths)):
+            #     img = impro.resize(imgs[i][y-size:y+size,x-size:x+size],opt.outsize,interpolation=cv2.INTER_CUBIC) 
+            #     mask = impro.resize(masks[i][y-size:y+size,x-size:x+size],opt.outsize,interpolation=cv2.INTER_CUBIC)
+            #     impro.imwrite(os.path.join(origindir,'%05d'%(i+1)+'.jpg'), img)
+            #     impro.imwrite(os.path.join(maskdir,'%05d'%(i+1)+'.png'), mask)
+            ex_mul = random.uniform(1.2,1.7)
+            positions = []
            for imagepath in imagepaths:
                img = impro.imread(imagepath)
                mask = runmodel.get_ROI_position(img,net,opt,keepsize=True)[0]
                imgs.append(img)
                masks.append(mask)
-                if not mask_flag:
-                    mask_avg = mask.astype(np.float64)
-                    mask_flag = True
-                else:
-                    mask_avg += mask.astype(np.float64)
-
-            mask_avg = np.clip(mask_avg/len(imagepaths),0,255).astype('uint8')
-            mask_avg = impro.mask_threshold(mask_avg,20,64)
-            if not opt.all_mosaic_area:
-                mask_avg = impro.find_mostlikely_ROI(mask_avg)
-            x,y,size,area = impro.boundingSquare(mask_avg,Ex_mul=random.uniform(1.1,1.5))
-            
-            for i in range(len(imagepaths)):
-                img = impro.resize(imgs[i][y-size:y+size,x-size:x+size],opt.outsize,interpolation=cv2.INTER_CUBIC) 
+                x,y,size,area = impro.boundingSquare(mask,Ex_mul=ex_mul)
+                positions.append([x,y,size])
+            positions =np.array(positions)
+            for i in range(3):positions[:,i] = filt.medfilt(positions[:,i],opt.medfilt_num)
+
+            for i,imagepath in enumerate(imagepaths):
+                x,y,size = positions[i][0],positions[i][1],positions[i][2]
+                tmp_cnt = i
+                while size<opt.minsize//2:
+                    tmp_cnt = tmp_cnt-1
+                    x,y,size = positions[tmp_cnt][0],positions[tmp_cnt][1],positions[tmp_cnt][2]
+                img = impro.resize(imgs[i][y-size:y+size,x-size:x+size],opt.outsize,interpolation=cv2.INTER_CUBIC)
                mask = impro.resize(masks[i][y-size:y+size,x-size:x+size],opt.outsize,interpolation=cv2.INTER_CUBIC)
                impro.imwrite(os.path.join(origindir,'%05d'%(i+1)+'.jpg'), img)
                impro.imwrite(os.path.join(maskdir,'%05d'%(i+1)+'.png'), mask)
+                # x_tmp,y_tmp,size_tmp
+
+            # for imagepath in imagepaths:
+            #     img = impro.imread(imagepath)
+            #     mask,x,y,halfsize,area = runmodel.get_ROI_position(img,net,opt,keepsize=True)
+            #     if halfsize>opt.minsize//4:
+            #         if not opt.all_mosaic_area:
+            #             mask_avg = impro.find_mostlikely_ROI(mask_avg)
+            #         x,y,size,area = impro.boundingSquare(mask_avg,Ex_mul=ex_mul)
+            #     img = impro.resize(imgs[i][y-size:y+size,x-size:x+size],opt.outsize,interpolation=cv2.INTER_CUBIC)
+            #     mask = impro.resize(masks[i][y-size:y+size,x-size:x+size],opt.outsize,interpolation=cv2.INTER_CUBIC)
+            #     impro.imwrite(os.path.join(origindir,'%05d'%(i+1)+'.jpg'), img)
+            #     impro.imwrite(os.path.join(maskdir,'%05d'%(i+1)+'.png'), mask)
+

            result_cnt+=1

@@ -124,5 +160,5 @@ for videopath in videopaths:
        util.writelog(os.path.join(opt.savedir,'opt.txt'), 
              videopath+'\n'+str(result_cnt)+'\n'+str(e))
    video_cnt +=1
-    if opt.use_gpu != -1:
+    if opt.gpu_id != '-1':
        torch.cuda.empty_cache()
--- a/models/BVDNet.py
+++ b/models/BVDNet.py
+import torch
+import torch.nn as nn
+from .pix2pixHD_model import *
+from .model_util import *
+from models import model_util
+
+class UpBlock(nn.Module):
+    def __init__(self, in_channel, out_channel, kernel_size=3, padding=1):
+        super().__init__()
+
+        self.convup = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
+                nn.ReflectionPad2d(padding),
+                # EqualConv2d(out_channel, out_channel, kernel_size, padding=padding),
+                SpectralNorm(nn.Conv2d(in_channel, out_channel, kernel_size)),
+                nn.LeakyReLU(0.2),
+                # Blur(out_channel),
+            )
+
+    def forward(self, input):
+        outup = self.convup(input)
+        return outup
+
+class Encoder2d(nn.Module):
+    def __init__(self, input_nc, ngf=64, n_downsampling=3, activation = nn.LeakyReLU(0.2)):
+        super(Encoder2d, self).__init__()        
+   
+        model = [nn.ReflectionPad2d(3), SpectralNorm(nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0)), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [  nn.ReflectionPad2d(1),
+                        SpectralNorm(nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=0)), 
+                        activation]
+
+        self.model = nn.Sequential(*model)
+
+    def forward(self, input):
+        return self.model(input)
+
+class Encoder3d(nn.Module):
+    def __init__(self, input_nc, ngf=64, n_downsampling=3, activation = nn.LeakyReLU(0.2)):
+        super(Encoder3d, self).__init__()        
+               
+        model = [SpectralNorm(nn.Conv3d(input_nc, ngf, kernel_size=3, padding=1)), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [  SpectralNorm(nn.Conv3d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1)),
+                         activation]
+        self.model = nn.Sequential(*model)
+
+    def forward(self, input):
+        return self.model(input)
+
+class BVDNet(nn.Module):
+    def __init__(self, N=2, n_downsampling=3, n_blocks=4, input_nc=3, output_nc=3,activation=nn.LeakyReLU(0.2)):
+        super(BVDNet, self).__init__()
+        ngf = 64
+        padding_type = 'reflect'
+        self.N = N
+
+        ### encoder
+        self.encoder3d = Encoder3d(input_nc,64,n_downsampling,activation)
+        self.encoder2d = Encoder2d(input_nc,64,n_downsampling,activation)
+
+        ### resnet blocks
+        self.blocks = []
+        mult = 2**n_downsampling
+        for i in range(n_blocks):
+            self.blocks += [ResnetBlockSpectralNorm(ngf * mult, padding_type=padding_type, activation=activation)]
+        self.blocks = nn.Sequential(*self.blocks)
+
+        ### decoder
+        self.decoder = []        
+        for i in range(n_downsampling):
+            mult = 2**(n_downsampling - i)
+            self.decoder += [UpBlock(ngf * mult, int(ngf * mult / 2))]
+        self.decoder += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]        
+        self.decoder = nn.Sequential(*self.decoder)
+        self.limiter = nn.Tanh()
+
+    def forward(self, stream, previous):
+        this_shortcut = stream[:,:,self.N]
+        stream = self.encoder3d(stream)
+        stream = stream.reshape(stream.size(0),stream.size(1),stream.size(3),stream.size(4))
+        previous = self.encoder2d(previous)
+        x = stream + previous
+        x = self.blocks(x)
+        x = self.decoder(x)
+        x = x+this_shortcut
+        x = self.limiter(x)
+        return x
+
+def define_G(N=2, n_blocks=1, gpu_id='-1'):
+    netG = BVDNet(N = N, n_blocks=n_blocks)
+    netG = model_util.todevice(netG,gpu_id)
+    netG.apply(model_util.init_weights)
+    return netG
+
+################################Discriminator################################
+def define_D(input_nc=6, ndf=64, n_layers_D=1, use_sigmoid=False, num_D=3, gpu_id='-1'):          
+    netD = MultiscaleDiscriminator(input_nc, ndf, n_layers_D, use_sigmoid, num_D)
+    netD = model_util.todevice(netD,gpu_id)
+    netD.apply(model_util.init_weights)
+    return netD
+
+class MultiscaleDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, n_layers=3, use_sigmoid=False, num_D=3):
+        super(MultiscaleDiscriminator, self).__init__()
+        self.num_D = num_D
+        self.n_layers = n_layers
+
+        for i in range(num_D):
+            netD = NLayerDiscriminator(input_nc, ndf, n_layers, use_sigmoid)
+            setattr(self, 'layer'+str(i), netD.model)
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+    def singleD_forward(self, model, input):
+        return [model(input)]
+
+    def forward(self, input):        
+        num_D = self.num_D
+        result = []
+        input_downsampled = input
+        for i in range(num_D):
+            model = getattr(self, 'layer'+str(num_D-1-i))
+            result.append(self.singleD_forward(model, input_downsampled))
+            if i != (num_D-1):
+                input_downsampled = self.downsample(input_downsampled)
+        return result
+        
+# Defines the PatchGAN discriminator with the specified arguments.
+class NLayerDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, n_layers=3, use_sigmoid=False):
+        super(NLayerDiscriminator, self).__init__()
+        self.n_layers = n_layers
+
+        kw = 4
+        padw = int(np.ceil((kw-1.0)/2))
+        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2)]]
+
+        nf = ndf
+        for n in range(1, n_layers):
+            nf_prev = nf
+            nf = min(nf * 2, 512)
+            sequence += [[
+                SpectralNorm(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw)),
+                nn.LeakyReLU(0.2)
+            ]]
+
+        nf_prev = nf
+        nf = min(nf * 2, 512)
+        sequence += [[
+            SpectralNorm(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw)),
+            nn.LeakyReLU(0.2)
+        ]]
+
+        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
+
+        if use_sigmoid:
+            sequence += [[nn.Sigmoid()]]
+
+        sequence_stream = []
+        for n in range(len(sequence)):
+            sequence_stream += sequence[n]
+        self.model = nn.Sequential(*sequence_stream)
+
+    def forward(self, input):
+        return self.model(input)        
+
+class GANLoss(nn.Module):
+    def __init__(self, mode='D'):
+        super(GANLoss, self).__init__()
+        if mode == 'D':
+            self.lossf = model_util.HingeLossD()
+        elif mode == 'G':
+            self.lossf = model_util.HingeLossG()
+        self.mode = mode
+    
+    def forward(self, dis_fake = None, dis_real = None):
+        if isinstance(dis_fake, list):
+            if self.mode == 'D':
+                loss = 0
+                for i in range(len(dis_fake)):
+                    loss += self.lossf(dis_fake[i][-1],dis_real[i][-1])
+            elif self.mode =='G':
+                loss = 0
+                weight = 2**len(dis_fake)
+                for i in range(len(dis_fake)):
+                    weight = weight/2
+                    loss += weight*self.lossf(dis_fake[i][-1])
+            return loss
+        else:
+            if self.mode == 'D':
+                return self.lossf(dis_fake[-1],dis_real[-1])
+            elif self.mode =='G':
+                return self.lossf(dis_fake[-1])
--- a/models/BiSeNet_model.py
+++ b/models/BiSeNet_model.py
@@ -2,7 +2,7 @@
 import torch.nn as nn
 import torch
 import torch.nn.functional as F
-from . import components
+from . import model_util
 import warnings
 warnings.filterwarnings(action='ignore')

@@ -43,7 +43,7 @@ class DiceLoss(nn.Module):
 class resnet18(torch.nn.Module):
    def __init__(self, pretrained=True):
        super().__init__()
-        self.features = components.resnet18(pretrained=pretrained)
+        self.features = model_util.resnet18(pretrained=pretrained)
        self.conv1 = self.features.conv1
        self.bn1 = self.features.bn1
        self.relu = self.features.relu
@@ -70,7 +70,7 @@ class resnet18(torch.nn.Module):
 class resnet101(torch.nn.Module):
    def __init__(self, pretrained=True):
        super().__init__()
-        self.features = components.resnet101(pretrained=pretrained)
+        self.features = model_util.resnet101(pretrained=pretrained)
        self.conv1 = self.features.conv1
        self.bn1 = self.features.bn1
        self.relu = self.features.relu

--- a/models/loadmodel.py
+++ b/models/loadmodel.py
 import torch
-from .pix2pix_model import define_G
-from .pix2pixHD_model import define_G as define_G_HD
-from .unet_model import UNet
-from .video_model import MosaicNet
-from .videoHD_model import MosaicNet as MosaicNet_HD
+from . import model_util
+from .pix2pix_model import define_G as pix2pix_G
+from .pix2pixHD_model import define_G as pix2pixHD_G
+# from .video_model import MosaicNet
+# from .videoHD_model import MosaicNet as MosaicNet_HD
 from .BiSeNet_model import BiSeNet
+from .BVDNet import define_G as video_G

 def show_paramsnumber(net,netname='net'):
    parameters = sum(param.numel() for param in net.parameters())
    parameters = round(parameters/1e6,2)
    print(netname+' parameters: '+str(parameters)+'M')

-def __patch_instance_norm_state_dict(state_dict, module, keys, i=0):
-    """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
-    key = keys[i]
-    if i + 1 == len(keys):  # at the end, pointing to a parameter/buffer
-        if module.__class__.__name__.startswith('InstanceNorm') and \
-                (key == 'running_mean' or key == 'running_var'):
-            if getattr(module, key) is None:
-                state_dict.pop('.'.join(keys))
-        if module.__class__.__name__.startswith('InstanceNorm') and \
-           (key == 'num_batches_tracked'):
-            state_dict.pop('.'.join(keys))
-    else:
-        __patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
-
 def pix2pix(opt):
    # print(opt.model_path,opt.netG)
    if opt.netG == 'HD':
-        netG = define_G_HD(3, 3, 64, 'global' ,4)
+        netG = pix2pixHD_G(3, 3, 64, 'global' ,4)
    else:
-        netG = define_G(3, 3, 64, opt.netG, norm='batch',use_dropout=True, init_type='normal', gpu_ids=[])
+        netG = pix2pix_G(3, 3, 64, opt.netG, norm='batch',use_dropout=True, init_type='normal', gpu_ids=[])
    show_paramsnumber(netG,'netG')
    netG.load_state_dict(torch.load(opt.model_path))
+    netG = model_util.todevice(netG,opt.gpu_id)
    netG.eval()
-    if opt.use_gpu != -1:
-        netG.cuda()
    return netG


 def style(opt):
    if opt.edges:
-        netG = define_G(1, 3, 64, 'resnet_9blocks', norm='instance',use_dropout=True, init_type='normal', gpu_ids=[])
+        netG = pix2pix_G(1, 3, 64, 'resnet_9blocks', norm='instance',use_dropout=True, init_type='normal', gpu_ids=[])
    else:
-        netG = define_G(3, 3, 64, 'resnet_9blocks', norm='instance',use_dropout=False, init_type='normal', gpu_ids=[])
+        netG = pix2pix_G(3, 3, 64, 'resnet_9blocks', norm='instance',use_dropout=False, init_type='normal', gpu_ids=[])

    #in other to load old pretrain model
    #https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/models/base_model.py
@@ -57,23 +43,19 @@ def style(opt):

    # patch InstanceNorm checkpoints prior to 0.4
    for key in list(state_dict.keys()):  # need to copy keys here because we mutate in loop
-        __patch_instance_norm_state_dict(state_dict, netG, key.split('.'))
+        model_util.patch_instance_norm_state_dict(state_dict, netG, key.split('.'))
    netG.load_state_dict(state_dict)

-    if opt.use_gpu != -1:
-        netG.cuda()
+    netG = model_util.todevice(netG,opt.gpu_id)
+    netG.eval()
    return netG

 def video(opt):
-    if 'HD' in opt.model_path:
-        netG = MosaicNet_HD(3*25+1, 3, norm='instance')
-    else:
-        netG = MosaicNet(3*25+1, 3,norm = 'batch')
+    netG = video_G(N=2,n_blocks=4,gpu_id=opt.gpu_id)
    show_paramsnumber(netG,'netG')
    netG.load_state_dict(torch.load(opt.model_path))
+    netG = model_util.todevice(netG,opt.gpu_id)
    netG.eval()
-    if opt.use_gpu != -1:
-        netG.cuda()
    return netG

 def bisenet(opt,type='roi'):
@@ -86,7 +68,6 @@ def bisenet(opt,type='roi'):
        net.load_state_dict(torch.load(opt.model_path))
    elif type == 'mosaic':
        net.load_state_dict(torch.load(opt.mosaic_position_model_path))
+    net = model_util.todevice(net,opt.gpu_id)
    net.eval()
-    if opt.use_gpu != -1:
-        net.cuda()
    return net
--- a/models/components.py
+++ b/models/components.py
-import torch.nn as nn
-import torch.utils.model_zoo as model_zoo
-
-
-__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
-           'resnet152']
-
-
-model_urls = {
-    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
-    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
-    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
-    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
-    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
-}
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=None):
-        super(BasicBlock, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = norm_layer(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = norm_layer(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=None):
-        super(Bottleneck, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv1x1(inplanes, planes)
-        self.bn1 = norm_layer(planes)
-        self.conv2 = conv3x3(planes, planes, stride)
-        self.bn2 = norm_layer(planes)
-        self.conv3 = conv1x1(planes, planes * self.expansion)
-        self.bn3 = norm_layer(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-
-class ResNet(nn.Module):
-
-    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, norm_layer=None):
-        super(ResNet, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        self.inplanes = 64
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
-                               bias=False)
-        self.bn1 = norm_layer(64)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer)
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2, norm_layer=norm_layer)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2, norm_layer=norm_layer)
-        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-        self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-        # Zero-initialize the last BN in each residual branch,
-        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
-        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
-        if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, Bottleneck):
-                    nn.init.constant_(m.bn3.weight, 0)
-                elif isinstance(m, BasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1, norm_layer=None):
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                norm_layer(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample, norm_layer))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(block(self.inplanes, planes, norm_layer=norm_layer))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-
-        return x
-
-
-def resnet18(pretrained=False, **kwargs):
-    """Constructs a ResNet-18 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
-    return model
-
-
-def resnet34(pretrained=False, **kwargs):
-    """Constructs a ResNet-34 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
-    return model
-
-
-def resnet50(pretrained=False, **kwargs):
-    """Constructs a ResNet-50 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
-    return model
-
-
-def resnet101(pretrained=False, **kwargs):
-    """Constructs a ResNet-101 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
-    return model
-
-
-def resnet152(pretrained=False, **kwargs):
-    """Constructs a ResNet-152 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
-    return model
\ No newline at end of file
+import functools
+from math import exp
+
+import torch
+import torch.nn as nn
+from torch.nn import init
+from torch.autograd import Variable
+import torch.nn.functional as F
+import torch.nn.utils.spectral_norm as SpectralNorm
+from torchvision import models
+import torch.utils.model_zoo as model_zoo
+
+################################## IO ##################################
+def save(net,path,gpu_id):
+    if isinstance(net, nn.DataParallel):
+        torch.save(net.module.cpu().state_dict(),path)
+    else:
+        torch.save(net.cpu().state_dict(),path) 
+    if gpu_id != '-1':
+        net.cuda()
+
+def todevice(net,gpu_id):
+    if gpu_id != '-1' and len(gpu_id) == 1:
+        net.cuda()
+    elif gpu_id != '-1' and len(gpu_id) > 1:
+        net = nn.DataParallel(net)
+        net.cuda()
+    return net
+
+# patch InstanceNorm checkpoints prior to 0.4
+def patch_instance_norm_state_dict(state_dict, module, keys, i=0):
+    """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
+    key = keys[i]
+    if i + 1 == len(keys):  # at the end, pointing to a parameter/buffer
+        if module.__class__.__name__.startswith('InstanceNorm') and \
+                (key == 'running_mean' or key == 'running_var'):
+            if getattr(module, key) is None:
+                state_dict.pop('.'.join(keys))
+        if module.__class__.__name__.startswith('InstanceNorm') and \
+           (key == 'num_batches_tracked'):
+            state_dict.pop('.'.join(keys))
+    else:
+        patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
+
+################################## initialization ##################################
+def get_norm_layer(norm_type='instance',mod = '2d'):
+    if norm_type == 'batch':
+        if mod == '2d':
+            norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
+        elif mod == '3d':
+            norm_layer = functools.partial(nn.BatchNorm3d, affine=True)
+    elif norm_type == 'instance':
+        if mod == '2d':
+            norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=True)
+        elif mod =='3d':
+            norm_layer = functools.partial(nn.InstanceNorm3d, affine=False, track_running_stats=True)
+    elif norm_type == 'none':
+        norm_layer = None
+    else:
+        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
+
+    return norm_layer
+
+def init_weights(net, init_type='normal', gain=0.02):
+    def init_func(m):
+        classname = m.__class__.__name__
+        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
+            if init_type == 'normal':
+                init.normal_(m.weight.data, 0.0, gain)
+            elif init_type == 'xavier':
+                init.xavier_normal_(m.weight.data, gain=gain)
+            elif init_type == 'kaiming':
+                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
+            elif init_type == 'orthogonal':
+                init.orthogonal_(m.weight.data, gain=gain)
+            else:
+                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
+            if hasattr(m, 'bias') and m.bias is not None:
+                init.constant_(m.bias.data, 0.0)
+        elif classname.find('BatchNorm2d') != -1:
+            init.normal_(m.weight.data, 1.0, gain)
+            init.constant_(m.bias.data, 0.0)
+
+    # print('initialize network with %s' % init_type)
+    net.apply(init_func)
+
+################################## Network structure ##################################
+################################## ResnetBlock ##################################
+class ResnetBlockSpectralNorm(nn.Module):
+    def __init__(self, dim, padding_type, activation=nn.LeakyReLU(0.2), use_dropout=False):
+        super(ResnetBlockSpectralNorm, self).__init__()
+        self.conv_block = self.build_conv_block(dim, padding_type, activation, use_dropout)
+
+    def build_conv_block(self, dim, padding_type, activation, use_dropout):
+        conv_block = []
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+
+        conv_block += [SpectralNorm(nn.Conv2d(dim, dim, kernel_size=3, padding=p)),
+                       activation]
+        if use_dropout:
+            conv_block += [nn.Dropout(0.5)]
+
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv_block += [SpectralNorm(nn.Conv2d(dim, dim, kernel_size=3, padding=p))]
+
+        return nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        out = x + self.conv_block(x)
+        return out
+
+################################## Resnet ##################################
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=None):
+        super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+class Bottleneck(nn.Module):
+    expansion = 4
+    def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=None):
+        super(Bottleneck, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv1x1(inplanes, planes)
+        self.bn1 = norm_layer(planes)
+        self.conv2 = conv3x3(planes, planes, stride)
+        self.bn2 = norm_layer(planes)
+        self.conv3 = conv1x1(planes, planes * self.expansion)
+        self.bn3 = norm_layer(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, norm_layer=None):
+        super(ResNet, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        self.inplanes = 64
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = norm_layer(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer)
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2, norm_layer=norm_layer)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2, norm_layer=norm_layer)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, Bottleneck):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, norm_layer=None):
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, norm_layer))
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(block(self.inplanes, planes, norm_layer=norm_layer))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
+
+def resnet18(pretrained=False, **kwargs):
+    """Constructs a ResNet-18 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
+    return model
+
+def resnet101(pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
+    return model
+
+################################## Loss function ##################################
+class HingeLossD(nn.Module):
+    def __init__(self):
+        super(HingeLossD, self).__init__()
+
+    def forward(self, dis_fake, dis_real):
+        loss_real = torch.mean(F.relu(1. - dis_real))
+        loss_fake = torch.mean(F.relu(1. + dis_fake))
+        return loss_real + loss_fake
+
+class HingeLossG(nn.Module):
+    def __init__(self):
+        super(HingeLossG, self).__init__()
+
+    def forward(self, dis_fake):
+        loss_fake = -torch.mean(dis_fake)
+        return loss_fake
+
+class VGGLoss(nn.Module):
+    def __init__(self, gpu_id):
+        super(VGGLoss, self).__init__()  
+
+        self.vgg = Vgg19()
+        if gpu_id != '-1' and len(gpu_id) == 1:
+            self.vgg.cuda()
+        elif gpu_id != '-1' and len(gpu_id) > 1:
+            self.vgg = nn.DataParallel(self.vgg)
+            self.vgg.cuda()
+
+        self.criterion = nn.MSELoss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]        
+
+    def forward(self, x, y):              
+        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())        
+        return loss
+
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(21, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)        
+        h_relu3 = self.slice3(h_relu2)        
+        h_relu4 = self.slice4(h_relu3)        
+        h_relu5 = self.slice5(h_relu4)                
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+
+################################## Evaluation ##################################
+'''https://github.com/Po-Hsun-Su/pytorch-ssim
+
+img1 = Variable(torch.rand(1, 1, 256, 256))
+img2 = Variable(torch.rand(1, 1, 256, 256))
+
+if torch.cuda.is_available():
+    img1 = img1.cuda()
+    img2 = img2.cuda()
+
+print(pytorch_ssim.ssim(img1, img2))
+
+ssim_loss = pytorch_ssim.SSIM(window_size = 11)
+
+print(ssim_loss(img1, img2))
+'''
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
+    return gauss/gauss.sum()
+
+def create_window(window_size, channel):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
+    return window
+
+def _ssim(img1, img2, window, window_size, channel, size_average = True):
+    mu1 = F.conv2d(img1, window, padding = window_size//2, groups = channel)
+    mu2 = F.conv2d(img2, window, padding = window_size//2, groups = channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1*mu2
+
+    sigma1_sq = F.conv2d(img1*img1, window, padding = window_size//2, groups = channel) - mu1_sq
+    sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = channel) - mu2_sq
+    sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = channel) - mu1_mu2
+
+    C1 = 0.01**2
+    C2 = 0.03**2
+
+    ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
+
+    if size_average:
+        return ssim_map.mean()
+    else:
+        return ssim_map.mean(1).mean(1).mean(1)
+
+class SSIM(torch.nn.Module):
+    def __init__(self, window_size = 11, size_average = True):
+        super(SSIM, self).__init__()
+        self.window_size = window_size
+        self.size_average = size_average
+        self.channel = 1
+        self.window = create_window(window_size, self.channel)
+
+    def forward(self, img1, img2):
+        (_, channel, _, _) = img1.size()
+
+        if channel == self.channel and self.window.data.type() == img1.data.type():
+            window = self.window
+        else:
+            window = create_window(self.window_size, channel)
+            
+            if img1.is_cuda:
+                window = window.cuda(img1.get_device())
+            window = window.type_as(img1)
+            
+            self.window = window
+            self.channel = channel
+
+
+        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
+
+def ssim(img1, img2, window_size = 11, size_average = True):
+    (_, channel, _, _) = img1.size()
+    window = create_window(window_size, channel)
+    
+    if img1.is_cuda:
+        window = window.cuda(img1.get_device())
+    window = window.type_as(img1)
+    
+    return _ssim(img1, img2, window, window_size, channel, size_average)
--- a/models/runmodel.py
+++ b/models/runmodel.py
@@ -7,11 +7,11 @@ from util import data
 import torch
 import numpy as np

-def run_segment(img,net,size = 360,use_gpu = 0):
+def run_segment(img,net,size = 360,gpu_id = '-1'):
    img = impro.resize(img,size)
-    img = data.im2tensor(img,use_gpu = use_gpu,  bgr2rgb = False,use_transform = False , is0_1 = True)
+    img = data.im2tensor(img,gpu_id = gpu_id, bgr2rgb = False, is0_1 = True)
    mask = net(img)
-    mask = data.tensor2im(mask, gray=True,rgb2bgr = False, is0_1 = True)
+    mask = data.tensor2im(mask, gray=True, is0_1 = True)
    return mask

 def run_pix2pix(img,net,opt):
@@ -19,7 +19,7 @@ def run_pix2pix(img,net,opt):
        img = impro.resize(img,512)
    else:
        img = impro.resize(img,128)
-    img = data.im2tensor(img,use_gpu=opt.use_gpu)
+    img = data.im2tensor(img,gpu_id=opt.gpu_id)
    img_fake = net(img)
    img_fake = data.tensor2im(img_fake)
    return img_fake
@@ -50,18 +50,18 @@ def run_styletransfer(opt, net, img):
        else:
            canny_low = opt.canny-int(opt.canny/2)
            canny_high = opt.canny+int(opt.canny/2)
-        img = cv2.Canny(img,opt.canny-50,opt.canny+50)
+        img = cv2.Canny(img,canny_low,canny_high)
        if opt.only_edges:
            return img
-        img = data.im2tensor(img,use_gpu=opt.use_gpu,gray=True,use_transform = False,is0_1 = False)
+        img = data.im2tensor(img,gpu_id=opt.gpu_id,gray=True)
    else:    
-        img = data.im2tensor(img,use_gpu=opt.use_gpu,gray=False,use_transform = True)
+        img = data.im2tensor(img,gpu_id=opt.gpu_id)
    img = net(img)
    img = data.tensor2im(img)
    return img

 def get_ROI_position(img,net,opt,keepsize=True):
-    mask = run_segment(img,net,size=360,use_gpu = opt.use_gpu)
+    mask = run_segment(img,net,size=360,gpu_id = opt.gpu_id)
    mask = impro.mask_threshold(mask,opt.mask_extend,opt.mask_threshold)
    if keepsize:
        mask = impro.resize_like(mask, img)
@@ -70,7 +70,7 @@ def get_ROI_position(img,net,opt,keepsize=True):

 def get_mosaic_position(img_origin,net_mosaic_pos,opt):
    h,w = img_origin.shape[:2]
-    mask = run_segment(img_origin,net_mosaic_pos,size=360,use_gpu = opt.use_gpu)
+    mask = run_segment(img_origin,net_mosaic_pos,size=360,gpu_id = opt.gpu_id)
    # mask_1 = mask.copy()
    mask = impro.mask_threshold(mask,ex_mun=int(min(h,w)/20),threshold=opt.mask_threshold)
    if not opt.all_mosaic_area:

--- a/models/videoHD_model.py
+++ b/models/videoHD_model.py
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from .pix2pixHD_model import *
-
-
-class encoder_2d(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, 
-                 padding_type='reflect'):
-        assert(n_blocks >= 0)
-        super(encoder_2d, self).__init__()        
-        activation = nn.ReLU(True)        
-
-        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2**i
-            model += [nn.ReflectionPad2d(1),nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=0),
-                      norm_layer(ngf * mult * 2), activation]
-
-        self.model = nn.Sequential(*model)
-    def forward(self, input):
-        return self.model(input)  
-
-class decoder_2d(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, 
-                 padding_type='reflect'):
-        assert(n_blocks >= 0)
-        super(decoder_2d, self).__init__()        
-        activation = nn.ReLU(True)        
-
-        model = []
-
-        ### resnet blocks
-        mult = 2**n_downsampling
-        for i in range(n_blocks):
-            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer)]
-        
-        ### upsample         
-        for i in range(n_downsampling):
-            mult = 2**(n_downsampling - i)
-
-            # model += [  nn.Upsample(scale_factor = 2, mode='nearest'),
-            # nn.ReflectionPad2d(1),
-            # nn.Conv2d(ngf * mult, int(ngf * mult / 2),kernel_size=3, stride=1, padding=0),
-            # norm_layer(int(ngf * mult / 2)),
-            # nn.ReLU(True)]
-            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1),
-                       norm_layer(int(ngf * mult / 2)), activation]
-        model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]        
-        self.model = nn.Sequential(*model)
-            
-    def forward(self, input):
-        return self.model(input)
-
-
-class conv_3d(nn.Module):
-    def __init__(self,inchannel,outchannel,kernel_size=3,stride=2,padding=1,norm_layer_3d=nn.BatchNorm3d,use_bias=True):
-        super(conv_3d, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv3d(inchannel, outchannel, kernel_size=kernel_size, stride=stride, padding=padding, bias=use_bias),
-            norm_layer_3d(outchannel),
-            nn.ReLU(inplace=True),
-        )
-
-    def forward(self, x):
-        x = self.conv(x)
-        return x
-
-class conv_2d(nn.Module):
-    def __init__(self,inchannel,outchannel,kernel_size=3,stride=1,padding=1,norm_layer_2d=nn.BatchNorm2d,use_bias=True):
-        super(conv_2d, self).__init__()
-        self.conv = nn.Sequential(
-            nn.ReflectionPad2d(padding),
-            nn.Conv2d(inchannel, outchannel, kernel_size=kernel_size, stride=stride, padding=0, bias=use_bias),
-            norm_layer_2d(outchannel),
-            nn.ReLU(inplace=True),
-        )
-
-    def forward(self, x):
-        x = self.conv(x)
-        return x
-
-
-class encoder_3d(nn.Module):
-    def __init__(self,in_channel,norm_layer_2d,norm_layer_3d,use_bias):
-        super(encoder_3d, self).__init__()
-        self.inconv = conv_3d(1, 64, 7, 2, 3,norm_layer_3d,use_bias)
-        self.down1 = conv_3d(64, 128, 3, 2, 1,norm_layer_3d,use_bias)
-        self.down2 = conv_3d(128, 256, 3, 2, 1,norm_layer_3d,use_bias)
-        self.down3 = conv_3d(256, 512, 3, 2, 1,norm_layer_3d,use_bias)
-        self.down4 = conv_3d(512, 1024, 3, 1, 1,norm_layer_3d,use_bias)
-        self.pool = nn.AvgPool3d((5,1,1))
-        # self.conver2d = nn.Sequential(
-        #     nn.Conv2d(256*int(in_channel/4), 256, kernel_size=3, stride=1, padding=1, bias=use_bias),
-        #     norm_layer_2d(256),
-        #     nn.ReLU(inplace=True),
-        # )
-
-
-    def forward(self, x):
-
-        x = x.view(x.size(0),1,x.size(1),x.size(2),x.size(3))
-        x = self.inconv(x)
-        x = self.down1(x)
-        x = self.down2(x)
-        x = self.down3(x)
-        x = self.down4(x)
-        #print(x.size())
-        x = self.pool(x)
-        #print(x.size())
-        # torch.Size([1, 1024, 16, 16])
-        # torch.Size([1, 512, 5, 16, 16])
-
-
-        x = x.view(x.size(0),x.size(1),x.size(3),x.size(4))
-
-       # x = self.conver2d(x)
-
-        return x
-
-    # def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, 
-    #              padding_type='reflect')
-
-class ALL(nn.Module):
-    def __init__(self, in_channel, out_channel,norm_layer_2d,norm_layer_3d,use_bias):
-        super(ALL, self).__init__()
-
-        self.encoder_2d = encoder_2d(4,3,64,4,norm_layer=norm_layer_2d,padding_type='reflect')
-        self.encoder_3d = encoder_3d(in_channel,norm_layer_2d,norm_layer_3d,use_bias)
-        self.decoder_2d = decoder_2d(4,3,64,4,norm_layer=norm_layer_2d,padding_type='reflect')
-        # self.shortcut_cov = conv_2d(3,64,7,1,3,norm_layer_2d,use_bias)
-        self.merge1 = conv_2d(2048,1024,3,1,1,norm_layer_2d,use_bias)
-        # self.merge2 = nn.Sequential(
-        #     conv_2d(128,64,3,1,1,norm_layer_2d,use_bias),
-        #     nn.ReflectionPad2d(3),
-        #     nn.Conv2d(64, out_channel, kernel_size=7, padding=0),
-        #     nn.Tanh()
-        # )
-
-    def forward(self, x):
-
-        N = int((x.size()[1])/3)
-        x_2d = torch.cat((x[:,int((N-1)/2)*3:(int((N-1)/2)+1)*3,:,:], x[:,N-1:N,:,:]), 1)
-        #shortcut_2d = x[:,int((N-1)/2)*3:(int((N-1)/2)+1)*3,:,:]
-
-        x_2d = self.encoder_2d(x_2d)
-        x_3d = self.encoder_3d(x)
-        #x = x_2d + x_3d
-        x = torch.cat((x_2d,x_3d),1)
-        x = self.merge1(x)
-
-        x = self.decoder_2d(x)
-        #shortcut_2d = self.shortcut_cov(shortcut_2d)
-        #x = torch.cat((x,shortcut_2d),1)
-        #x = self.merge2(x)
-
-        return x
-
-def MosaicNet(in_channel, out_channel, norm='batch'):
-
-    if norm == 'batch':
-        # norm_layer_2d = nn.BatchNorm2d
-        # norm_layer_3d = nn.BatchNorm3d
-        norm_layer_2d = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
-        norm_layer_3d = functools.partial(nn.BatchNorm3d, affine=True, track_running_stats=True)
-        use_bias = False
-    elif norm == 'instance':
-        norm_layer_2d = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
-        norm_layer_3d = functools.partial(nn.InstanceNorm3d, affine=False, track_running_stats=False)
-        use_bias = True
-
-    return ALL(in_channel, out_channel, norm_layer_2d, norm_layer_3d, use_bias)
--- a/models/video_model.py
+++ b/models/video_model.py
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from .pix2pix_model import *
-
-
-class encoder_2d(nn.Module):
-    """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
-
-    We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
-    """
-
-    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
-        """Construct a Resnet-based generator
-
-        Parameters:
-            input_nc (int)      -- the number of channels in input images
-            output_nc (int)     -- the number of channels in output images
-            ngf (int)           -- the number of filters in the last conv layer
-            norm_layer          -- normalization layer
-            use_dropout (bool)  -- if use dropout layers
-            n_blocks (int)      -- the number of ResNet blocks
-            padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero
-        """
-        assert(n_blocks >= 0)
-        super(encoder_2d, self).__init__()
-        if type(norm_layer) == functools.partial:
-            use_bias = norm_layer.func == nn.InstanceNorm2d
-        else:
-            use_bias = norm_layer == nn.InstanceNorm2d
-
-        model = [nn.ReflectionPad2d(3),
-                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
-                 norm_layer(ngf),
-                 nn.ReLU(True)]
-
-        n_downsampling = 2
-        for i in range(n_downsampling):  # add downsampling layers
-            mult = 2 ** i
-            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
-                      norm_layer(ngf * mult * 2),
-                      nn.ReLU(True)]
-        #torch.Size([1, 256, 32, 32])
-
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        """Standard forward"""
-        return self.model(input)
-
-
-class decoder_2d(nn.Module):
-    """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
-
-    We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
-    """
-
-    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
-        """Construct a Resnet-based generator
-
-        Parameters:
-            input_nc (int)      -- the number of channels in input images
-            output_nc (int)     -- the number of channels in output images
-            ngf (int)           -- the number of filters in the last conv layer
-            norm_layer          -- normalization layer
-            use_dropout (bool)  -- if use dropout layers
-            n_blocks (int)      -- the number of ResNet blocks
-            padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero
-        """
-        super(decoder_2d, self).__init__()
-        if type(norm_layer) == functools.partial:
-            use_bias = norm_layer.func == nn.InstanceNorm2d
-        else:
-            use_bias = norm_layer == nn.InstanceNorm2d
-
-        model = []
-
-        n_downsampling = 2
-        mult = 2 ** n_downsampling
-        for i in range(n_blocks):       # add ResNet blocks
-            model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
-        #torch.Size([1, 256, 32, 32])
-
-        for i in range(n_downsampling):  # add upsampling layers
-            mult = 2 ** (n_downsampling - i)
-            # model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
-            #                              kernel_size=3, stride=2,
-            #                              padding=1, output_padding=1,
-            #                              bias=use_bias),
-            #           norm_layer(int(ngf * mult / 2)),
-            #           nn.ReLU(True)]
-            #https://distill.pub/2016/deconv-checkerboard/
-            #https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/190
-
-            model += [  nn.Upsample(scale_factor = 2, mode='nearest'),
-                        nn.ReflectionPad2d(1),
-                        nn.Conv2d(ngf * mult, int(ngf * mult / 2),kernel_size=3, stride=1, padding=0),
-                        norm_layer(int(ngf * mult / 2)),
-                        nn.ReLU(True)]
-        # model += [nn.ReflectionPad2d(3)]
-        # model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-        # model += [nn.Tanh()]
-        # model += [nn.Sigmoid()]
-
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        """Standard forward"""
-        return self.model(input)
-
-
-
-class conv_3d(nn.Module):
-    def __init__(self,inchannel,outchannel,kernel_size=3,stride=2,padding=1,norm_layer_3d=nn.BatchNorm3d,use_bias=True):
-        super(conv_3d, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv3d(inchannel, outchannel, kernel_size=kernel_size, stride=stride, padding=padding, bias=use_bias),
-            norm_layer_3d(outchannel),
-            nn.ReLU(inplace=True),
-        )
-
-    def forward(self, x):
-        x = self.conv(x)
-        return x
-
-class conv_2d(nn.Module):
-    def __init__(self,inchannel,outchannel,kernel_size=3,stride=1,padding=1,norm_layer_2d=nn.BatchNorm2d,use_bias=True):
-        super(conv_2d, self).__init__()
-        self.conv = nn.Sequential(
-            nn.ReflectionPad2d(padding),
-            nn.Conv2d(inchannel, outchannel, kernel_size=kernel_size, stride=stride, padding=0, bias=use_bias),
-            norm_layer_2d(outchannel),
-            nn.ReLU(inplace=True),
-        )
-
-    def forward(self, x):
-        x = self.conv(x)
-        return x
-
-
-class encoder_3d(nn.Module):
-    def __init__(self,in_channel,norm_layer_2d,norm_layer_3d,use_bias):
-        super(encoder_3d, self).__init__()
-        self.down1 = conv_3d(1, 64, 3, 2, 1,norm_layer_3d,use_bias)
-        self.down2 = conv_3d(64, 128, 3, 2, 1,norm_layer_3d,use_bias)
-        self.down3 = conv_3d(128, 256, 3, 1, 1,norm_layer_3d,use_bias)
-        self.conver2d = nn.Sequential(
-            nn.Conv2d(256*int(in_channel/4), 256, kernel_size=3, stride=1, padding=1, bias=use_bias),
-            norm_layer_2d(256),
-            nn.ReLU(inplace=True),
-        )
-
-
-    def forward(self, x):
-
-        x = x.view(x.size(0),1,x.size(1),x.size(2),x.size(3))
-        x = self.down1(x)
-        x = self.down2(x)
-        x = self.down3(x)
-
-        x = x.view(x.size(0),x.size(1)*x.size(2),x.size(3),x.size(4))
-
-        x = self.conver2d(x)
-
-        return x
-
-
-
-class ALL(nn.Module):
-    def __init__(self, in_channel, out_channel,norm_layer_2d,norm_layer_3d,use_bias):
-        super(ALL, self).__init__()
-
-        self.encoder_2d = encoder_2d(4,-1,64,norm_layer=norm_layer_2d,n_blocks=9)
-        self.encoder_3d = encoder_3d(in_channel,norm_layer_2d,norm_layer_3d,use_bias)
-        self.decoder_2d = decoder_2d(4,3,64,norm_layer=norm_layer_2d,n_blocks=9)
-        self.shortcut_cov = conv_2d(3,64,7,1,3,norm_layer_2d,use_bias)
-        self.merge1 = conv_2d(512,256,3,1,1,norm_layer_2d,use_bias)
-        self.merge2 = nn.Sequential(
-            conv_2d(128,64,3,1,1,norm_layer_2d,use_bias),
-            nn.ReflectionPad2d(3),
-            nn.Conv2d(64, out_channel, kernel_size=7, padding=0),
-            nn.Tanh()
-        )
-
-    def forward(self, x):
-
-        N = int((x.size()[1])/3)
-        x_2d = torch.cat((x[:,int((N-1)/2)*3:(int((N-1)/2)+1)*3,:,:], x[:,N-1:N,:,:]), 1)
-        shortcut_2d = x[:,int((N-1)/2)*3:(int((N-1)/2)+1)*3,:,:]
-
-        x_2d = self.encoder_2d(x_2d)
-
-        x_3d = self.encoder_3d(x)
-        x = torch.cat((x_2d,x_3d),1)
-        x = self.merge1(x)
-        x = self.decoder_2d(x)
-        shortcut_2d = self.shortcut_cov(shortcut_2d)
-        x = torch.cat((x,shortcut_2d),1)
-        x = self.merge2(x)
-
-        return x
-
-def MosaicNet(in_channel, out_channel, norm='batch'):
-
-    if norm == 'batch':
-        # norm_layer_2d = nn.BatchNorm2d
-        # norm_layer_3d = nn.BatchNorm3d
-        norm_layer_2d = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
-        norm_layer_3d = functools.partial(nn.BatchNorm3d, affine=True, track_running_stats=True)
-        use_bias = False
-    elif norm == 'instance':
-        norm_layer_2d = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
-        norm_layer_3d = functools.partial(nn.InstanceNorm3d, affine=False, track_running_stats=False)
-        use_bias = True
-
-    return ALL(in_channel, out_channel, norm_layer_2d, norm_layer_3d, use_bias)
--- a/requirements.txt
+++ b/requirements.txt
+opencv_python==4.5.1.48
+numpy==1.19.2
+torchvision==0.8.2
+torch==1.7.1
+matplotlib==3.3.2
+tensorboardX==2.2
+scikit-image==0.17.2
\ No newline at end of file
--- a/tools/server.py
+++ b/tools/server.py
+import os
+import sys
+import traceback
+import cv2
+import numpy as np
+try:
+    from cores import Options,clean
+    from util import util
+    from util import image_processing as impro
+    from models import loadmodel
+except Exception as e:
+    print(e)
+    input('Please press any key to exit.\n')
+    sys.exit(0)
+
+# python server.py --gpu_id 0 --model_path ./pretrained_models/mosaic/clean_face_HD.pth
+opt = Options()
+opt.parser.add_argument('--port',type=int,default=4000, help='')
+opt = opt.getparse(True)
+netM = loadmodel.bisenet(opt,'mosaic')
+netG = loadmodel.pix2pix(opt)
+
+from flask import Flask, request
+import base64
+import shutil
+
+app = Flask(__name__)
+
+@app.route("/handle", methods=["POST"])
+def handle():
+    result = {}
+    # to opencv img
+    try:
+        imgRec = request.form['img']
+        imgByte = base64.b64decode(imgRec)
+        img_np_arr = np.frombuffer(imgByte, np.uint8)
+        img = cv2.imdecode(img_np_arr, cv2.IMREAD_COLOR)
+    except Exception as e:
+        result['img'] = imgRec
+        result['info'] = 'readfailed'
+        return result
+
+    # run model
+    try:
+        if max(img.shape)>1080:
+            img = impro.resize(img,720,interpolation=cv2.INTER_CUBIC)
+        img = clean.cleanmosaic_img_server(opt,img,netG,netM)
+    except Exception as e:
+        result['img'] = imgRec
+        result['info'] = 'procfailed'
+        return result
+
+    # return
+    imgbytes = cv2.imencode('.jpg', img)[1]
+    imgString = base64.b64encode(imgbytes).decode('utf-8')
+    result['img'] = imgString
+    result['info'] = 'ok'
+    return result
+
+app.run("0.0.0.0", port= opt.port, debug=opt.debug)
\ No newline at end of file
--- a/tools/trace_model.py
+++ b/tools/trace_model.py
+import os
+import sys
+import traceback
+sys.path.append("..")
+from util import mosaic
+import torch
+
+try:
+    from cores import Options,add,clean,style
+    from util import util
+    from models import loadmodel
+except Exception as e:
+    print(e)
+    input('Please press any key to exit.\n')
+    sys.exit(0)
+
+opt = Options().getparse(test_flag = False)
+if not os.path.isdir(opt.temp_dir):
+    util.file_init(opt)
+
+def saveScriptModel(model,example,savepath):
+    model.cpu()
+    traced_script_module = torch.jit.trace(model, example)
+    # try ScriptModel
+    output = traced_script_module(example)
+    print(output)
+    traced_script_module.save(savepath)
+
+savedir = '../cpp/res/models/'
+util.makedirs(savedir)
+
+opt.mosaic_position_model_path = '../pretrained_models/mosaic/mosaic_position.pth'
+model = loadmodel.bisenet(opt,'mosaic')
+example = torch.ones((1,3,360,360))
+saveScriptModel(model,example,os.path.join(savedir,'mosaic_position.pt'))
+
+
+
+# def main():
+    
+#     if os.path.isdir(opt.media_path):
+#         files = util.Traversal(opt.media_path)
+#     else:
+#         files = [opt.media_path]        
+#     if opt.mode == 'add':
+#         netS = loadmodel.bisenet(opt,'roi')
+#         for file in files:
+#             opt.media_path = file
+#             if util.is_img(file):
+#                 add.addmosaic_img(opt,netS)
+#             elif util.is_video(file):
+#                 add.addmosaic_video(opt,netS)
+#                 util.clean_tempfiles(opt, tmp_init = False)
+#             else:
+#                 print('This type of file is not supported')
+#             util.clean_tempfiles(opt, tmp_init = False)
+
+#     elif opt.mode == 'clean':
+#         netM = loadmodel.bisenet(opt,'mosaic')
+#         if opt.traditional:
+#             netG = None
+#         elif opt.netG == 'video':
+#             netG = loadmodel.video(opt)
+#         else:
+#             netG = loadmodel.pix2pix(opt)
+        
+#         for file in files:
+#             opt.media_path = file
+#             if util.is_img(file):
+#                 clean.cleanmosaic_img(opt,netG,netM)
+#             elif util.is_video(file):
+#                 if opt.netG == 'video' and not opt.traditional:            
+#                     clean.cleanmosaic_video_fusion(opt,netG,netM)
+#                 else:
+#                     clean.cleanmosaic_video_byframe(opt,netG,netM)
+#                 util.clean_tempfiles(opt, tmp_init = False)
+#             else:
+#                 print('This type of file is not supported')
+
+#     elif opt.mode == 'style':
+#         netG = loadmodel.style(opt)
+#         for file in files:
+#             opt.media_path = file
+#             if util.is_img(file):
+#                 style.styletransfer_img(opt,netG)
+#             elif util.is_video(file):
+#                 style.styletransfer_video(opt,netG)
+#                 util.clean_tempfiles(opt, tmp_init = False)
+#             else:
+#                 print('This type of file is not supported')
+
+#     util.clean_tempfiles(opt, tmp_init = False)
+
+# if __name__ == '__main__':
+#     main()
\ No newline at end of file
--- a/train/add/train.py
+++ b/train/add/train.py
@@ -54,10 +54,10 @@ util.makedirs(dir_checkpoint)
 util.writelog(os.path.join(dir_checkpoint,'loss.txt'), 
              str(time.asctime(time.localtime(time.time())))+'\n'+util.opt2str(opt))

-def Totensor(img,use_gpu=True):
+def Totensor(img,gpu_id=True):
    size=img.shape[0]
    img = torch.from_numpy(img).float()
-    if opt.use_gpu != -1:
+    if opt.gpu_id != -1:
        img = img.cuda()
    return img

@@ -68,11 +68,11 @@ def loadimage(imagepaths,maskpaths,opt,test_flag = False):
    for i in range(len(imagepaths)):
        img = impro.resize(impro.imread(imagepaths[i]),opt.loadsize)
        mask = impro.resize(impro.imread(maskpaths[i],mod = 'gray'),opt.loadsize)      
-        img,mask = data.random_transform_image(img, mask, opt.finesize, test_flag)
+        img,mask = data.random_transform_pair_image(img, mask, opt.finesize, test_flag)
        images[i] = (img.transpose((2, 0, 1))/255.0)
        masks[i] = (mask.reshape(1,1,opt.finesize,opt.finesize)/255.0)
-    images = Totensor(images,opt.use_gpu)
-    masks = Totensor(masks,opt.use_gpu)
+    images = data.to_tensor(images,opt.gpu_id)
+    masks = data.to_tensor(masks,opt.gpu_id)

    return images,masks

@@ -111,7 +111,7 @@ if opt.continue_train:
    f = open(os.path.join(dir_checkpoint,'epoch_log.txt'),'r')
    opt.startepoch = int(f.read())
    f.close()
-if opt.use_gpu != -1:
+if opt.gpu_id != -1:
    net.cuda()
    cudnn.benchmark = True

@@ -135,7 +135,7 @@ for epoch in range(opt.startepoch,opt.maxepoch):
    starttime = datetime.datetime.now()
    util.writelog(os.path.join(dir_checkpoint,'loss.txt'),'Epoch {}/{}.'.format(epoch + 1, opt.maxepoch),True)
    net.train()
-    if opt.use_gpu != -1:
+    if opt.gpu_id != -1:
        net.cuda()
    epoch_loss = 0
    for i in range(int(img_num*0.8/opt.batchsize)):

--- a/train/clean/train.py
+++ b/train/clean/train.py
-import os
-import sys
-sys.path.append("..")
-sys.path.append("../..")
-from cores import Options
-opt = Options()
-
-import numpy as np
-import cv2
-import random
-import torch
-import torch.nn as nn
-import time
-from multiprocessing import Process, Queue
-
-from util import mosaic,util,ffmpeg,filt,data
-from util import image_processing as impro
-from models import pix2pix_model,pix2pixHD_model,video_model,unet_model,loadmodel,videoHD_model
-import matplotlib
-matplotlib.use('Agg')
-from matplotlib import pyplot as plt
-import torch.backends.cudnn as cudnn
-
-'''
--------------------------Get options--------------------------
-'''
-opt.parser.add_argument('--N',type=int,default=25, help='')
-opt.parser.add_argument('--lr',type=float,default=0.0002, help='')
-opt.parser.add_argument('--beta1',type=float,default=0.5, help='')
-opt.parser.add_argument('--gan', action='store_true', help='if specified, use gan')
-opt.parser.add_argument('--l2', action='store_true', help='if specified, use L2 loss')
-opt.parser.add_argument('--hd', action='store_true', help='if specified, use HD model')
-opt.parser.add_argument('--lambda_L1',type=float,default=100, help='')
-opt.parser.add_argument('--lambda_gan',type=float,default=1, help='')
-opt.parser.add_argument('--finesize',type=int,default=256, help='')
-opt.parser.add_argument('--loadsize',type=int,default=286, help='')
-opt.parser.add_argument('--batchsize',type=int,default=1, help='')
-opt.parser.add_argument('--norm',type=str,default='instance', help='')
-opt.parser.add_argument('--num_D', type=int, default=2, help='number of discriminators to use')
-opt.parser.add_argument('--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers')
-opt.parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss') 
-opt.parser.add_argument('--image_pool',type=int,default=8, help='number of image load pool')
-opt.parser.add_argument('--load_process',type=int,default=4, help='number of process for loading data')
-
-opt.parser.add_argument('--dataset',type=str,default='./datasets/face/', help='')
-opt.parser.add_argument('--maxiter',type=int,default=10000000, help='')
-opt.parser.add_argument('--savefreq',type=int,default=10000, help='')
-opt.parser.add_argument('--startiter',type=int,default=0, help='')
-opt.parser.add_argument('--continue_train', action='store_true', help='')
-opt.parser.add_argument('--savename',type=str,default='face', help='')
-
-
-'''
--------------------------Init--------------------------
-'''
-opt = opt.getparse()
-dir_checkpoint = os.path.join('checkpoints/',opt.savename)
-util.makedirs(dir_checkpoint)
-util.writelog(os.path.join(dir_checkpoint,'loss.txt'), 
-              str(time.asctime(time.localtime(time.time())))+'\n'+util.opt2str(opt))
-cudnn.benchmark = True
-
-N = opt.N
-loss_sum = [0.,0.,0.,0.,0.,0]
-loss_plot = [[],[],[],[]]
-item_plot = []
-
-# list video dir 
-videonames = os.listdir(opt.dataset)
-videonames.sort()
-lengths = [];tmp = []
-print('Check dataset...')
-for video in videonames:
-    if video != 'opt.txt':
-        video_images = os.listdir(os.path.join(opt.dataset,video,'origin_image'))
-        lengths.append(len(video_images))
-        tmp.append(video)
-videonames = tmp
-video_num = len(videonames)
-
-#--------------------------Init network--------------------------
-print('Init network...')
-if opt.hd:
-    netG = videoHD_model.MosaicNet(3*N+1, 3, norm=opt.norm)
-else:
-    netG = video_model.MosaicNet(3*N+1, 3, norm=opt.norm)
-netG.cuda()
-loadmodel.show_paramsnumber(netG,'netG')
-
-if opt.gan:
-    if opt.hd:
-        netD = pix2pixHD_model.define_D(6, 64, opt.n_layers_D, norm = opt.norm, use_sigmoid=False, num_D=opt.num_D,getIntermFeat=True)    
-    else:
-        netD = pix2pix_model.define_D(3*2, 64, 'basic', norm = opt.norm)
-    netD.cuda()
-    netD.train()
-
-#--------------------------continue train--------------------------
-if opt.continue_train:
-    if not os.path.isfile(os.path.join(dir_checkpoint,'last_G.pth')):
-        opt.continue_train = False
-        print('can not load last_G, training on init weight.')
-if opt.continue_train:     
-    netG.load_state_dict(torch.load(os.path.join(dir_checkpoint,'last_G.pth')))
-    if opt.gan:
-        netD.load_state_dict(torch.load(os.path.join(dir_checkpoint,'last_D.pth')))
-    f = open(os.path.join(dir_checkpoint,'iter'),'r')
-    opt.startiter = int(f.read())
-    f.close()
-
-#--------------------------optimizer & loss--------------------------
-optimizer_G = torch.optim.Adam(netG.parameters(), lr=opt.lr,betas=(opt.beta1, 0.999))
-criterion_L1 = nn.L1Loss()
-criterion_L2 = nn.MSELoss()
-if opt.gan:
-    optimizer_D = torch.optim.Adam(netD.parameters(), lr=opt.lr,betas=(opt.beta1, 0.999))
-    if opt.hd:
-        criterionGAN = pix2pixHD_model.GANLoss(tensor=torch.cuda.FloatTensor).cuda() 
-        criterionFeat = pix2pixHD_model.GAN_Feat_loss(opt)
-        criterionVGG = pix2pixHD_model.VGGLoss([opt.use_gpu])
-    else:
-        criterionGAN = pix2pix_model.GANLoss(gan_mode='lsgan').cuda()   
-
-'''
--------------------------preload data & data pool--------------------------
-'''
-print('Preloading data, please wait...')
-def preload(pool):
-    cnt = 0
-    input_imgs = torch.rand(opt.batchsize,N*3+1,opt.finesize,opt.finesize)
-    ground_trues = torch.rand(opt.batchsize,3,opt.finesize,opt.finesize)
-    while 1:
-        try:
-            for i in range(opt.batchsize):
-                video_index = random.randint(0,video_num-1)
-                videoname = videonames[video_index]
-                img_index = random.randint(int(N/2)+1,lengths[video_index]- int(N/2)-1)
-                input_imgs[i],ground_trues[i] = data.load_train_video(videoname,img_index,opt)
-            cnt += 1
-            pool.put([input_imgs,ground_trues])
-        except Exception as e:
-            print("Error:",videoname,e)
-pool = Queue(opt.image_pool)
-for i in range(opt.load_process):
-    p = Process(target=preload,args=(pool,))
-    p.daemon = True
-    p.start()
-
-'''
--------------------------train--------------------------
-'''
-util.copyfile('./train.py', os.path.join(dir_checkpoint,'train.py'))
-util.copyfile('../../models/videoHD_model.py', os.path.join(dir_checkpoint,'model.py'))
-netG.train()
-time_start=time.time()
-print("Begin training...")
-for iter in range(opt.startiter+1,opt.maxiter):
-
-    inputdata,target = pool.get()
-    inputdata,target = inputdata.cuda(),target.cuda()
-
-    if opt.gan:
-        # compute fake images: G(A)
-        pred = netG(inputdata)
-        real_A = inputdata[:,int((N-1)/2)*3:(int((N-1)/2)+1)*3,:,:]
-        
-        # --------------------update D--------------------
-        pix2pix_model.set_requires_grad(netD,True)
-        optimizer_D.zero_grad()
-        # Fake
-        fake_AB = torch.cat((real_A, pred), 1)
-        pred_fake = netD(fake_AB.detach())
-        loss_D_fake = criterionGAN(pred_fake, False)
-        # Real
-        real_AB = torch.cat((real_A, target), 1)
-        pred_real = netD(real_AB)
-        loss_D_real = criterionGAN(pred_real, True)
-        # combine loss and calculate gradients
-        loss_D = (loss_D_fake + loss_D_real) * 0.5
-        loss_sum[4] += loss_D_fake.item()
-        loss_sum[5] += loss_D_real.item()
-        # udpate D's weights
-        loss_D.backward()
-        optimizer_D.step()
-
-        # --------------------update G--------------------
-        pix2pix_model.set_requires_grad(netD,False)
-        optimizer_G.zero_grad()
-
-        # First, G(A) should fake the discriminator
-        fake_AB = torch.cat((real_A, pred), 1)
-        pred_fake = netD(fake_AB)
-        loss_G_GAN = criterionGAN(pred_fake, True)*opt.lambda_gan
-            
-        # combine loss and calculate gradients
-        if opt.l2:
-            loss_G_L1 = (criterion_L1(pred, target)+criterion_L2(pred, target)) * opt.lambda_L1
-        else:
-            loss_G_L1 = criterion_L1(pred, target) * opt.lambda_L1
-
-        if opt.hd:
-            real_AB = torch.cat((real_A, target), 1)
-            pred_real = netD(real_AB)
-            loss_G_GAN_Feat = criterionFeat(pred_fake,pred_real)
-            loss_VGG = criterionVGG(pred, target) * opt.lambda_feat
-            loss_G = loss_G_GAN + loss_G_L1 + loss_G_GAN_Feat + loss_VGG
-        else:
-            loss_G = loss_G_GAN + loss_G_L1
-        loss_sum[0] += loss_G_L1.item()
-        loss_sum[1] += loss_G_GAN.item()
-        loss_sum[2] += loss_G_GAN_Feat.item()
-        loss_sum[3] += loss_VGG.item()
-
-        # udpate G's weights
-        loss_G.backward()
-        optimizer_G.step()
-
-    else:
-        pred = netG(inputdata)
-        if opt.l2:
-            loss_G_L1 = (criterion_L1(pred, target)+criterion_L2(pred, target)) * opt.lambda_L1
-        else:
-            loss_G_L1 = criterion_L1(pred, target) * opt.lambda_L1
-        loss_sum[0] += loss_G_L1.item()
-
-        optimizer_G.zero_grad()
-        loss_G_L1.backward()
-        optimizer_G.step()
-
-    # save train result
-    if (iter+1)%1000 == 0:
-        try:
-            data.showresult(inputdata[:,int((N-1)/2)*3:(int((N-1)/2)+1)*3,:,:],
-                target, pred, os.path.join(dir_checkpoint,'result_train.jpg'))
-        except Exception as e:
-            print(e)
-
-    # plot
-    if (iter+1)%1000 == 0:
-        time_end = time.time()
-        #if opt.gan:
-        savestr ='iter:{0:d} L1_loss:{1:.3f} GAN_loss:{2:.3f} Feat:{3:.3f} VGG:{4:.3f} time:{5:.2f}'.format(
-            iter+1,loss_sum[0]/1000,loss_sum[1]/1000,loss_sum[2]/1000,loss_sum[3]/1000,(time_end-time_start)/1000)
-        util.writelog(os.path.join(dir_checkpoint,'loss.txt'), savestr,True)
-        if (iter+1)/1000 >= 10:
-            for i in range(4):loss_plot[i].append(loss_sum[i]/1000)
-            item_plot.append(iter+1)
-            try:
-                labels = ['L1_loss','GAN_loss','GAN_Feat_loss','VGG_loss']
-                for i in range(4):plt.plot(item_plot,loss_plot[i],label=labels[i])     
-                plt.xlabel('iter')
-                plt.legend(loc=1)
-                plt.savefig(os.path.join(dir_checkpoint,'loss.jpg'))
-                plt.close()
-            except Exception as e:
-                print("error:",e)
-
-        loss_sum = [0.,0.,0.,0.,0.,0.]
-        time_start=time.time()
-
-    # save network
-    if (iter+1)%(opt.savefreq//10) == 0:
-        torch.save(netG.cpu().state_dict(),os.path.join(dir_checkpoint,'last_G.pth'))
-        if opt.gan:
-            torch.save(netD.cpu().state_dict(),os.path.join(dir_checkpoint,'last_D.pth'))
-        if opt.use_gpu !=-1 :
-            netG.cuda()
-            if opt.gan:
-                netD.cuda()
-        f = open(os.path.join(dir_checkpoint,'iter'),'w+')
-        f.write(str(iter+1))
-        f.close()
-
-    if (iter+1)%opt.savefreq == 0:
-        os.rename(os.path.join(dir_checkpoint,'last_G.pth'),os.path.join(dir_checkpoint,str(iter+1)+'G.pth'))
-        if opt.gan:
-            os.rename(os.path.join(dir_checkpoint,'last_D.pth'),os.path.join(dir_checkpoint,str(iter+1)+'D.pth'))
-        print('network saved.')
-
-    #test
-    if (iter+1)%opt.savefreq == 0:
-        if os.path.isdir('./test'):  
-            netG.eval()
-            
-            test_names = os.listdir('./test')
-            test_names.sort()
-            result = np.zeros((opt.finesize*2,opt.finesize*len(test_names),3), dtype='uint8')
-
-            for cnt,test_name in enumerate(test_names,0):
-                img_names = os.listdir(os.path.join('./test',test_name,'image'))
-                img_names.sort()
-                inputdata = np.zeros((opt.finesize,opt.finesize,3*N+1), dtype='uint8')
-                for i in range(0,N):
-                    img = impro.imread(os.path.join('./test',test_name,'image',img_names[i]))
-                    img = impro.resize(img,opt.finesize)
-                    inputdata[:,:,i*3:(i+1)*3] = img
-
-                mask = impro.imread(os.path.join('./test',test_name,'mask.png'),'gray')
-                mask = impro.resize(mask,opt.finesize)
-                mask = impro.mask_threshold(mask,15,128)
-                inputdata[:,:,-1] = mask
-                result[0:opt.finesize,opt.finesize*cnt:opt.finesize*(cnt+1),:] = inputdata[:,:,int((N-1)/2)*3:(int((N-1)/2)+1)*3]
-                inputdata = data.im2tensor(inputdata,bgr2rgb=False,use_gpu=opt.use_gpu,use_transform = False,is0_1 = False)
-                pred = netG(inputdata)
-     
-                pred = data.tensor2im(pred,rgb2bgr = False, is0_1 = False)
-                result[opt.finesize:opt.finesize*2,opt.finesize*cnt:opt.finesize*(cnt+1),:] = pred
-
-            cv2.imwrite(os.path.join(dir_checkpoint,str(iter+1)+'_test.jpg'), result)
-            netG.train()
\ No newline at end of file
+import os
+import sys
+sys.path.append("..")
+sys.path.append("../..")
+from cores import Options
+opt = Options()
+
+import numpy as np
+import cv2
+import random
+import torch
+import torch.nn as nn
+import time
+
+from util import util,data,dataloader
+from util import image_processing as impro
+from models import BVDNet,model_util
+from skimage.metrics import structural_similarity
+from tensorboardX import SummaryWriter
+
+'''
+--------------------------Get options--------------------------
+'''
+opt.parser.add_argument('--N',type=int,default=2, help='The input tensor shape is H×W×T×C, T = 2N+1')
+opt.parser.add_argument('--S',type=int,default=3, help='Stride of 3 frames')
+# opt.parser.add_argument('--T',type=int,default=7, help='T = 2N+1')
+opt.parser.add_argument('--M',type=int,default=100, help='How many frames read from each videos')
+opt.parser.add_argument('--lr',type=float,default=0.0002, help='')
+opt.parser.add_argument('--beta1',type=float,default=0.9, help='')
+opt.parser.add_argument('--beta2',type=float,default=0.999, help='')
+opt.parser.add_argument('--finesize',type=int,default=256, help='')
+opt.parser.add_argument('--loadsize',type=int,default=286, help='')
+opt.parser.add_argument('--batchsize',type=int,default=1, help='')
+opt.parser.add_argument('--no_gan', action='store_true', help='if specified, do not use gan')
+opt.parser.add_argument('--n_blocks',type=int,default=4, help='')
+opt.parser.add_argument('--n_layers_D',type=int,default=2, help='')
+opt.parser.add_argument('--num_D',type=int,default=3, help='')
+opt.parser.add_argument('--lambda_L2',type=float,default=100, help='')
+opt.parser.add_argument('--lambda_VGG',type=float,default=1, help='')
+opt.parser.add_argument('--lambda_GAN',type=float,default=0.01, help='')
+opt.parser.add_argument('--lambda_D',type=float,default=1, help='')
+opt.parser.add_argument('--load_thread',type=int,default=16, help='number of thread for loading data')
+
+opt.parser.add_argument('--dataset',type=str,default='./datasets/face/', help='')
+opt.parser.add_argument('--dataset_test',type=str,default='./datasets/face_test/', help='')
+opt.parser.add_argument('--n_epoch',type=int,default=200, help='')
+opt.parser.add_argument('--save_freq',type=int,default=10000, help='')
+opt.parser.add_argument('--continue_train', action='store_true', help='')
+opt.parser.add_argument('--savename',type=str,default='face', help='')
+opt.parser.add_argument('--showresult_freq',type=int,default=1000, help='')
+opt.parser.add_argument('--showresult_num',type=int,default=4, help='')
+
+def ImageQualityEvaluation(tensor1,tensor2,showiter,writer,tag):
+    batch_len = len(tensor1)
+    psnr,ssmi = 0,0
+    for i in range(len(tensor1)):
+        img1,img2 = data.tensor2im(tensor1,rgb2bgr=False,batch_index=i), data.tensor2im(tensor2,rgb2bgr=False,batch_index=i)
+        psnr += impro.psnr(img1,img2)
+        ssmi += structural_similarity(img1,img2,multichannel=True)
+    writer.add_scalars('quality/psnr', {tag:psnr/batch_len}, showiter)
+    writer.add_scalars('quality/ssmi', {tag:ssmi/batch_len}, showiter)
+    return psnr/batch_len,ssmi/batch_len
+
+def ShowImage(tensor1,tensor2,tensor3,showiter,max_num,writer,tag):
+    show_imgs = []
+    for i in range(max_num):
+        show_imgs += [  data.tensor2im(tensor1,rgb2bgr = False,batch_index=i),
+                        data.tensor2im(tensor2,rgb2bgr = False,batch_index=i),
+                        data.tensor2im(tensor3,rgb2bgr = False,batch_index=i)]
+    show_img = impro.splice(show_imgs,  (opt.showresult_num,3))
+    writer.add_image(tag, show_img,showiter,dataformats='HWC')
+
+'''
+--------------------------Init--------------------------
+'''
+opt = opt.getparse()
+opt.T = 2*opt.N+1
+if opt.showresult_num >opt.batchsize:
+    opt.showresult_num = opt.batchsize
+dir_checkpoint = os.path.join('checkpoints',opt.savename)
+util.makedirs(dir_checkpoint)
+# start tensorboard
+localtime = time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime())
+tensorboard_savedir = os.path.join('checkpoints/tensorboard',localtime+'_'+opt.savename)
+TBGlobalWriter = SummaryWriter(tensorboard_savedir)
+print('Please run "tensorboard --logdir checkpoints/tensorboardX --host=your_server_ip" and input "'+localtime+'" to filter outputs')
+
+'''
+--------------------------Init Network--------------------------
+'''
+if opt.gpu_id != '-1' and len(opt.gpu_id) == 1:
+    torch.backends.cudnn.benchmark = True
+
+netG = BVDNet.define_G(opt.N,opt.n_blocks,gpu_id=opt.gpu_id)
+optimizer_G = torch.optim.Adam(netG.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
+lossfun_L2 = nn.MSELoss()
+lossfun_VGG = model_util.VGGLoss(opt.gpu_id)
+if not opt.no_gan:
+    netD = BVDNet.define_D(n_layers_D=opt.n_layers_D,num_D=opt.num_D,gpu_id=opt.gpu_id)
+    optimizer_D = torch.optim.Adam(netD.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
+    lossfun_GAND = BVDNet.GANLoss('D')
+    lossfun_GANG = BVDNet.GANLoss('G')
+
+'''
+--------------------------Init DataLoader--------------------------
+'''
+videolist_tmp = os.listdir(opt.dataset)
+videolist = []
+for video in videolist_tmp:
+    if os.path.isdir(os.path.join(opt.dataset,video)):
+        if len(os.listdir(os.path.join(opt.dataset,video,'mask')))>=opt.M:
+            videolist.append(video)
+sorted(videolist)
+videolist_train = videolist[:int(len(videolist)*0.8)].copy()
+videolist_eval = videolist[int(len(videolist)*0.8):].copy()
+
+Videodataloader_train = dataloader.VideoDataLoader(opt, videolist_train)
+Videodataloader_eval = dataloader.VideoDataLoader(opt, videolist_eval)
+
+'''
+--------------------------Train--------------------------
+'''
+previous_predframe_tmp = 0
+for train_iter in range(Videodataloader_train.n_iter):
+    t_start = time.time()
+    # train
+    ori_stream,mosaic_stream,previous_frame = Videodataloader_train.get_data()
+    ori_stream = data.to_tensor(ori_stream, opt.gpu_id)
+    mosaic_stream = data.to_tensor(mosaic_stream, opt.gpu_id)
+    if previous_frame is None:
+        previous_frame = data.to_tensor(previous_predframe_tmp, opt.gpu_id)
+    else:
+        previous_frame = data.to_tensor(previous_frame, opt.gpu_id)
+
+    ############### Forward ####################
+    # Fake Generator
+    out = netG(mosaic_stream,previous_frame)
+    # Discriminator
+    if not opt.no_gan:
+        dis_real = netD(torch.cat((mosaic_stream[:,:,opt.N],ori_stream[:,:,opt.N].detach()),dim=1))
+        dis_fake_D = netD(torch.cat((mosaic_stream[:,:,opt.N],out.detach()),dim=1))
+        loss_D = lossfun_GAND(dis_fake_D,dis_real) * opt.lambda_GAN * opt.lambda_D
+    # Generator
+    loss_L2 = lossfun_L2(out,ori_stream[:,:,opt.N]) * opt.lambda_L2
+    loss_VGG = lossfun_VGG(out,ori_stream[:,:,opt.N]) * opt.lambda_VGG
+    loss_G = loss_L2+loss_VGG
+    if not opt.no_gan:
+        dis_fake_G = netD(torch.cat((mosaic_stream[:,:,opt.N],out),dim=1))
+        loss_GANG = lossfun_GANG(dis_fake_G) * opt.lambda_GAN
+        loss_G = loss_G + loss_GANG
+
+    ############### Backward Pass ####################
+    optimizer_G.zero_grad()
+    loss_G.backward()
+    optimizer_G.step()
+
+    if not opt.no_gan:
+        optimizer_D.zero_grad()
+        loss_D.backward()        
+        optimizer_D.step()
+
+    previous_predframe_tmp = out.detach().cpu().numpy()
+    
+    if not opt.no_gan:
+        TBGlobalWriter.add_scalars('loss/train', {'L2':loss_L2.item(),'VGG':loss_VGG.item(),
+            'loss_D':loss_D.item(),'loss_G':loss_G.item()}, train_iter)
+    else:
+        TBGlobalWriter.add_scalars('loss/train', {'L2':loss_L2.item(),'VGG':loss_VGG.item()}, train_iter)
+
+    # save network
+    if train_iter%opt.save_freq == 0 and train_iter != 0:
+        model_util.save(netG, os.path.join('checkpoints',opt.savename,str(train_iter)+'_G.pth'), opt.gpu_id)
+        if not opt.no_gan:
+            model_util.save(netD, os.path.join('checkpoints',opt.savename,str(train_iter)+'_D.pth'), opt.gpu_id)
+
+    # Image quality evaluation 
+    if train_iter%(opt.showresult_freq//10) == 0:
+        ImageQualityEvaluation(out,ori_stream[:,:,opt.N],train_iter,TBGlobalWriter,'train')
+    
+    # Show result
+    if train_iter % opt.showresult_freq == 0:
+        ShowImage(mosaic_stream[:,:,opt.N],out,ori_stream[:,:,opt.N],train_iter,opt.showresult_num,TBGlobalWriter,'train')
+    
+    '''
+    --------------------------Eval--------------------------
+    '''
+    if (train_iter)%5 ==0:
+        ori_stream,mosaic_stream,previous_frame = Videodataloader_eval.get_data()
+        ori_stream = data.to_tensor(ori_stream, opt.gpu_id)
+        mosaic_stream = data.to_tensor(mosaic_stream, opt.gpu_id)
+        if previous_frame is None:
+            previous_frame = data.to_tensor(previous_predframe_tmp, opt.gpu_id)
+        else:
+            previous_frame = data.to_tensor(previous_frame, opt.gpu_id)
+        with torch.no_grad():
+            out = netG(mosaic_stream,previous_frame)
+            loss_L2 = lossfun_L2(out,ori_stream[:,:,opt.N]) * opt.lambda_L2
+            loss_VGG = lossfun_VGG(out,ori_stream[:,:,opt.N]) * opt.lambda_VGG
+        #TBGlobalWriter.add_scalars('loss/eval', {'L2':loss_L2.item(),'VGG':loss_VGG.item()}, train_iter)
+        previous_predframe_tmp = out.detach().cpu().numpy()
+
+        # Image quality evaluation 
+        if train_iter%(opt.showresult_freq//10) == 0:
+            psnr,ssmi = ImageQualityEvaluation(out,ori_stream[:,:,opt.N],train_iter,TBGlobalWriter,'eval')
+        
+        # Show result
+        if train_iter % opt.showresult_freq == 0:
+            ShowImage(mosaic_stream[:,:,opt.N],out,ori_stream[:,:,opt.N],train_iter,opt.showresult_num,TBGlobalWriter,'eval')
+            t_end = time.time()
+            print('iter:{0:d}  t:{1:.2f}  L2:{2:.4f}  vgg:{3:.4f}  psnr:{4:.2f}  ssmi:{5:.3f}'.format(train_iter,t_end-t_start,
+                loss_L2.item(),loss_VGG.item(),psnr,ssmi) )
+            t_strat = time.time()
+
+    '''
+    --------------------------Test--------------------------
+    '''
+    if train_iter % opt.showresult_freq == 0 and os.path.isdir(opt.dataset_test):
+        show_imgs = []
+        videos = os.listdir(opt.dataset_test)
+        sorted(videos)
+        for video in videos:
+            frames = os.listdir(os.path.join(opt.dataset_test,video,'image'))
+            sorted(frames)
+            for step in range(5):
+                mosaic_stream = []
+                for i in range(opt.T):
+                    _mosaic = impro.imread(os.path.join(opt.dataset_test,video,'image',frames[i*opt.S+step]),loadsize=opt.finesize,rgb=True)
+                    mosaic_stream.append(_mosaic)
+                if step == 0:
+                    previous = impro.imread(os.path.join(opt.dataset_test,video,'image',frames[opt.N*opt.S-1]),loadsize=opt.finesize,rgb=True)
+                    previous = data.im2tensor(previous,bgr2rgb = False, gpu_id = opt.gpu_id, is0_1 = False)
+                mosaic_stream = (np.array(mosaic_stream).astype(np.float32)/255.0-0.5)/0.5
+                mosaic_stream = mosaic_stream.reshape(1,opt.T,opt.finesize,opt.finesize,3).transpose((0,4,1,2,3))
+                mosaic_stream = data.to_tensor(mosaic_stream, opt.gpu_id)
+                with torch.no_grad():
+                    out = netG(mosaic_stream,previous)
+                previous = out
+            show_imgs+= [data.tensor2im(mosaic_stream[:,:,opt.N],rgb2bgr = False),data.tensor2im(out,rgb2bgr = False)]
+
+        show_img = impro.splice(show_imgs, (len(videos),2))
+        TBGlobalWriter.add_image('test', show_img,train_iter,dataformats='HWC')
\ No newline at end of file
--- a/util/data.py
+++ b/util/data.py
 import random
 import os
+from util.mosaic import get_random_parameter
 import numpy as np
 import torch
 import torchvision.transforms as transforms
 import cv2
 from . import image_processing as impro
-from . import mosaic
-transform = transforms.Compose([  
-    transforms.ToTensor(),  
-    transforms.Normalize(mean = (0.5, 0.5, 0.5), std = (0.5, 0.5, 0.5))  
-    ]  
-)  
-
-def tensor2im(image_tensor, imtype=np.uint8, gray=False, rgb2bgr = True ,is0_1 = False):
+from . import degradater
+
+def to_tensor(data,gpu_id):
+    data = torch.from_numpy(data)
+    if gpu_id != '-1':
+        data = data.cuda()
+    return data
+
+def normalize(data):
+    '''
+    normalize to -1 ~ 1
+    '''
+    return (data.astype(np.float32)/255.0-0.5)/0.5
+
+def anti_normalize(data):
+    return np.clip((data*0.5+0.5)*255,0,255).astype(np.uint8)
+
+def tensor2im(image_tensor, gray=False, rgb2bgr = True ,is0_1 = False, batch_index=0):
    image_tensor =image_tensor.data
-    image_numpy = image_tensor[0].cpu().float().numpy()
+    image_numpy = image_tensor[batch_index].cpu().float().numpy()
    
    if not is0_1:
        image_numpy = (image_numpy + 1)/2.0
@@ -24,7 +35,7 @@ def tensor2im(image_tensor, imtype=np.uint8, gray=False, rgb2bgr = True ,is0_1 =
    if gray:
        h, w = image_numpy.shape[1:]
        image_numpy = image_numpy.reshape(h,w)
-        return image_numpy.astype(imtype)
+        return image_numpy.astype(np.uint8)

    # output 3ch
    if image_numpy.shape[0] == 1:
@@ -32,11 +43,10 @@ def tensor2im(image_tensor, imtype=np.uint8, gray=False, rgb2bgr = True ,is0_1 =
    image_numpy = image_numpy.transpose((1, 2, 0))  
    if rgb2bgr and not gray:
        image_numpy = image_numpy[...,::-1]-np.zeros_like(image_numpy)
-    return image_numpy.astype(imtype)
+    return image_numpy.astype(np.uint8)


-def im2tensor(image_numpy, imtype=np.uint8, gray=False,bgr2rgb = True, reshape = True, use_gpu = 0,  use_transform = True,is0_1 = True):
-    
+def im2tensor(image_numpy, gray=False,bgr2rgb = True, reshape = True, gpu_id = '-1',is0_1 = False):
    if gray:
        h, w = image_numpy.shape
        image_numpy = (image_numpy/255.0-0.5)/0.5
@@ -47,18 +57,15 @@ def im2tensor(image_numpy, imtype=np.uint8, gray=False,bgr2rgb = True, reshape =
        h, w ,ch = image_numpy.shape
        if bgr2rgb:
            image_numpy = image_numpy[...,::-1]-np.zeros_like(image_numpy)
-        if use_transform:
-            image_tensor = transform(image_numpy)
+        if is0_1:
+            image_numpy = image_numpy/255.0
        else:
-            if is0_1:
-                image_numpy = image_numpy/255.0
-            else:
-                image_numpy = (image_numpy/255.0-0.5)/0.5
-            image_numpy = image_numpy.transpose((2, 0, 1))
-            image_tensor = torch.from_numpy(image_numpy).float()
+            image_numpy = (image_numpy/255.0-0.5)/0.5
+        image_numpy = image_numpy.transpose((2, 0, 1))
+        image_tensor = torch.from_numpy(image_numpy).float()
        if reshape:
            image_tensor = image_tensor.reshape(1,ch,h,w)
-    if use_gpu != -1:
+    if gpu_id != '-1':
        image_tensor = image_tensor.cuda()
    return image_tensor

@@ -68,53 +75,7 @@ def shuffledata(data,target):
    np.random.set_state(state)
    np.random.shuffle(target)

-def random_transform_video(src,target,finesize,N):
-    #random blur
-    if random.random()<0.2:
-        h,w = src.shape[:2]
-        src = src[:8*(h//8),:8*(w//8)]
-        Q_ran = random.randint(1,15)
-        src[:,:,:3*N] = impro.dctblur(src[:,:,:3*N],Q_ran)
-        target = impro.dctblur(target,Q_ran)
-
-    #random crop
-    h,w = target.shape[:2]
-    h_move = int((h-finesize)*random.random())
-    w_move = int((w-finesize)*random.random())
-    target = target[h_move:h_move+finesize,w_move:w_move+finesize,:]
-    src = src[h_move:h_move+finesize,w_move:w_move+finesize,:]
-
-    #random flip
-    if random.random()<0.5:
-        src = src[:,::-1,:]
-        target = target[:,::-1,:]
-
-    #random color
-    alpha = random.uniform(-0.1,0.1)
-    beta  = random.uniform(-0.1,0.1)
-    b     = random.uniform(-0.05,0.05)
-    g     = random.uniform(-0.05,0.05)
-    r     = random.uniform(-0.05,0.05)
-    for i in range(N):
-        src[:,:,i*3:(i+1)*3] = impro.color_adjust(src[:,:,i*3:(i+1)*3],alpha,beta,b,g,r)
-    target = impro.color_adjust(target,alpha,beta,b,g,r)
-
-    #random resize blur
-    if random.random()<0.5:
-        interpolations = [cv2.INTER_LINEAR,cv2.INTER_CUBIC,cv2.INTER_LANCZOS4]
-        size_ran = random.uniform(0.7,1.5)
-        interpolation_up = interpolations[random.randint(0,2)]
-        interpolation_down =interpolations[random.randint(0,2)]
-
-        tmp = cv2.resize(src[:,:,:3*N], (int(finesize*size_ran),int(finesize*size_ran)),interpolation=interpolation_up)
-        src[:,:,:3*N] = cv2.resize(tmp, (finesize,finesize),interpolation=interpolation_down)
-
-        tmp = cv2.resize(target, (int(finesize*size_ran),int(finesize*size_ran)),interpolation=interpolation_up)
-        target = cv2.resize(tmp, (finesize,finesize),interpolation=interpolation_down)
-
-    return src,target
-
-def random_transform_single(img,out_shape):
+def random_transform_single_mask(img,out_shape):
    out_h,out_w = out_shape
    img = cv2.resize(img,(int(out_w*random.uniform(1.1, 1.5)),int(out_h*random.uniform(1.1, 1.5))))
    h,w = img.shape[:2]
@@ -130,90 +91,65 @@ def random_transform_single(img,out_shape):
        img = cv2.resize(img,(out_w,out_h))
    return img

-def random_transform_image(img,mask,finesize,test_flag = False):
-    #random scale
-    if random.random()<0.5:
-        h,w = img.shape[:2]
-        loadsize = min((h,w))
-        a = (float(h)/float(w))*random.uniform(0.9, 1.1)
-        if h<w:
-            mask = cv2.resize(mask, (int(loadsize/a),loadsize))
-            img = cv2.resize(img, (int(loadsize/a),loadsize))
-        else:
-            mask = cv2.resize(mask, (loadsize,int(loadsize*a)))
-            img = cv2.resize(img, (loadsize,int(loadsize*a)))
-
-    #random crop
-    h,w = img.shape[:2]
-    h_move = int((h-finesize)*random.random())
-    w_move = int((w-finesize)*random.random())
-    img_crop = img[h_move:h_move+finesize,w_move:w_move+finesize]
-    mask_crop = mask[h_move:h_move+finesize,w_move:w_move+finesize]
+def get_transform_params():
+    crop_flag  = True
+    rotat_flag = np.random.random()<0.2
+    color_flag = True
+    flip_flag  = np.random.random()<0.2
+    degradate_flag  = np.random.random()<0.5
+    flag_dict = {'crop':crop_flag,'rotat':rotat_flag,'color':color_flag,'flip':flip_flag,'degradate':degradate_flag}
+    
+    crop_rate = [np.random.random(),np.random.random()]
+    rotat_rate = np.random.random()
+    color_rate = [np.random.uniform(-0.05,0.05),np.random.uniform(-0.05,0.05),np.random.uniform(-0.05,0.05),
+        np.random.uniform(-0.05,0.05),np.random.uniform(-0.05,0.05)]
+    flip_rate = np.random.random()
+    degradate_params = degradater.get_random_degenerate_params(mod='weaker_2')
+    rate_dict = {'crop':crop_rate,'rotat':rotat_rate,'color':color_rate,'flip':flip_rate,'degradate':degradate_params}
+
+    return {'flag':flag_dict,'rate':rate_dict}
+
+def random_transform_single_image(img,finesize,params=None,test_flag = False):
+    if params is None:
+        params = get_transform_params()
+    
+    if params['flag']['degradate']:
+        img = degradater.degradate(img,params['rate']['degradate'])

-    if test_flag:
-        return img_crop,mask_crop
+    if params['flag']['crop']:
+        h,w = img.shape[:2]
+        h_move = int((h-finesize)*params['rate']['crop'][0])
+        w_move = int((w-finesize)*params['rate']['crop'][1])
+        img = img[h_move:h_move+finesize,w_move:w_move+finesize]
    
-    #random rotation
-    if random.random()<0.2:
-        h,w = img_crop.shape[:2]
-        M = cv2.getRotationMatrix2D((w/2,h/2),90*int(4*random.random()),1)
-        img = cv2.warpAffine(img_crop,M,(w,h))
-        mask = cv2.warpAffine(mask_crop,M,(w,h))
-    else:
-        img,mask = img_crop,mask_crop
+    if test_flag:
+        return img

-    #random color
-    img = impro.color_adjust(img,ran=True)
+    if params['flag']['rotat']:
+        h,w = img.shape[:2]
+        M = cv2.getRotationMatrix2D((w/2,h/2),90*int(4*params['rate']['rotat']),1)
+        img = cv2.warpAffine(img,M,(w,h))

-    #random flip
-    if random.random()<0.5:
-        if random.random()<0.5:
-            img = img[:,::-1,:]
-            mask = mask[:,::-1]
-        else:
-            img = img[::-1,:,:]
-            mask = mask[::-1,:]
+    if params['flag']['color']:
+        img = impro.color_adjust(img,params['rate']['color'][0],params['rate']['color'][1],
+            params['rate']['color'][2],params['rate']['color'][3],params['rate']['color'][4])
+
+    if params['flag']['flip']:
+        img = img[:,::-1]

-    #random blur
-    if random.random()<0.5:
-        img = impro.dctblur(img,random.randint(1,15))
-        
-        # interpolations = [cv2.INTER_LINEAR,cv2.INTER_CUBIC,cv2.INTER_LANCZOS4]
-        # size_ran = random.uniform(0.7,1.5)
-        # img = cv2.resize(img, (int(finesize*size_ran),int(finesize*size_ran)),interpolation=interpolations[random.randint(0,2)])
-        # img = cv2.resize(img, (finesize,finesize),interpolation=interpolations[random.randint(0,2)])
-    
    #check shape
-    if img.shape[0]!= finesize or img.shape[1]!= finesize or mask.shape[0]!= finesize or mask.shape[1]!= finesize:
+    if img.shape[0]!= finesize or img.shape[1]!= finesize:
        img = cv2.resize(img,(finesize,finesize))
-        mask = cv2.resize(mask,(finesize,finesize))
        print('warning! shape error.')
-    return img,mask
-
-
-def load_train_video(videoname,img_index,opt):
-    N = opt.N    
-    input_img = np.zeros((opt.loadsize,opt.loadsize,3*N+1), dtype='uint8')
-    # this frame
-    this_mask = impro.imread(os.path.join(opt.dataset,videoname,'mask','%05d'%(img_index)+'.png'),'gray',loadsize=opt.loadsize)
-    input_img[:,:,-1] = this_mask
-    #print(os.path.join(opt.dataset,videoname,'origin_image','%05d'%(img_index)+'.jpg'))
-    ground_true =  impro.imread(os.path.join(opt.dataset,videoname,'origin_image','%05d'%(img_index)+'.jpg'),loadsize=opt.loadsize)
-    mosaic_size,mod,rect_rat,feather = mosaic.get_random_parameter(ground_true,this_mask)
-    start_pos = mosaic.get_random_startpos(num=N,bisa_p=0.3,bisa_max=mosaic_size,bisa_max_part=3)
-    # merge other frame
-    for i in range(0,N):
-        img =  impro.imread(os.path.join(opt.dataset,videoname,'origin_image','%05d'%(img_index+i-int(N/2))+'.jpg'),loadsize=opt.loadsize)
-        mask = impro.imread(os.path.join(opt.dataset,videoname,'mask','%05d'%(img_index+i-int(N/2))+'.png'),'gray',loadsize=opt.loadsize)
-        img_mosaic = mosaic.addmosaic_base(img, mask, mosaic_size,model = mod,rect_rat=rect_rat,feather=feather,start_point=start_pos[i])
-        input_img[:,:,i*3:(i+1)*3] = img_mosaic
-    # to tensor
-    input_img,ground_true = random_transform_video(input_img,ground_true,opt.finesize,N)
-    input_img = im2tensor(input_img,bgr2rgb=False,use_gpu=-1,use_transform = False,is0_1=False)
-    ground_true = im2tensor(ground_true,bgr2rgb=False,use_gpu=-1,use_transform = False,is0_1=False)
-    
-    return input_img,ground_true
+    return img

+def random_transform_pair_image(img,mask,finesize,test_flag = False):
+    params = get_transform_params()
+    img = random_transform_single_image(img,finesize,params)
+    params['flag']['degradate'] = False
+    params['flag']['color'] = False
+    mask = random_transform_single_image(mask,finesize,params)
+    return img,mask

 def showresult(img1,img2,img3,name,is0_1 = False):
    size = img1.shape[3]

--- a/util/dataloader.py
+++ b/util/dataloader.py
+import os
+import random
+import numpy as np
+from multiprocessing import Process, Queue
+from . import image_processing as impro
+from . import mosaic,data
+
+class VideoLoader(object):
+    """docstring for VideoLoader
+    Load a single video(Converted to images)
+    How to use:
+    1.Init VideoLoader as loader
+    2.Get data by loader.ori_stream
+    3.loader.next()  to get next stream
+    """
+    def __init__(self, opt, video_dir, test_flag=False):
+        super(VideoLoader, self).__init__()
+        self.opt = opt
+        self.test_flag = test_flag
+        self.video_dir = video_dir
+        self.t = 0
+        self.n_iter = self.opt.M -self.opt.S*(self.opt.T+1)
+        self.transform_params = data.get_transform_params()
+        self.ori_load_pool = []
+        self.mosaic_load_pool = []
+        self.previous_pred = None
+        feg_ori =  impro.imread(os.path.join(video_dir,'origin_image','00001.jpg'),loadsize=self.opt.loadsize,rgb=True)
+        feg_mask = impro.imread(os.path.join(video_dir,'mask','00001.png'),mod='gray',loadsize=self.opt.loadsize)
+        self.mosaic_size,self.mod,self.rect_rat,self.feather = mosaic.get_random_parameter(feg_ori,feg_mask)
+        self.startpos = [random.randint(0,self.mosaic_size),random.randint(0,self.mosaic_size)]
+        self.loadsize = self.opt.loadsize
+        #Init load pool
+        for i in range(self.opt.S*self.opt.T):
+            _ori_img = impro.imread(os.path.join(video_dir,'origin_image','%05d' % (i+1)+'.jpg'),loadsize=self.loadsize,rgb=True)
+            _mask = impro.imread(os.path.join(video_dir,'mask','%05d' % (i+1)+'.png' ),mod='gray',loadsize=self.loadsize)
+            _mosaic_img = mosaic.addmosaic_base(_ori_img, _mask, self.mosaic_size,0, self.mod,self.rect_rat,self.feather,self.startpos)
+            _ori_img = data.random_transform_single_image(_ori_img,opt.finesize,self.transform_params)
+            _mosaic_img = data.random_transform_single_image(_mosaic_img,opt.finesize,self.transform_params)
+
+            self.ori_load_pool.append(self.normalize(_ori_img))
+            self.mosaic_load_pool.append(self.normalize(_mosaic_img))
+        self.ori_load_pool = np.array(self.ori_load_pool)
+        self.mosaic_load_pool = np.array(self.mosaic_load_pool)
+
+        #Init frist stream
+        self.ori_stream    = self.ori_load_pool   [np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
+        self.mosaic_stream = self.mosaic_load_pool[np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
+        # stream B,T,H,W,C -> B,C,T,H,W
+        self.ori_stream    = self.ori_stream.reshape   (1,self.opt.T,opt.finesize,opt.finesize,3).transpose((0,4,1,2,3))
+        self.mosaic_stream = self.mosaic_stream.reshape(1,self.opt.T,opt.finesize,opt.finesize,3).transpose((0,4,1,2,3))
+        
+        #Init frist previous frame
+        self.previous_pred = self.ori_load_pool[self.opt.S*self.opt.N-1].copy()
+        # previous B,C,H,W
+        self.previous_pred = self.previous_pred.reshape(1,opt.finesize,opt.finesize,3).transpose((0,3,1,2))
+    
+    def normalize(self,data):
+        '''
+        normalize to -1 ~ 1
+        '''
+        return (data.astype(np.float32)/255.0-0.5)/0.5
+
+    def anti_normalize(self,data):
+        return np.clip((data*0.5+0.5)*255,0,255).astype(np.uint8)
+    
+    def next(self):
+        # random
+        if np.random.random()<0.05:
+            self.startpos = [random.randint(0,self.mosaic_size),random.randint(0,self.mosaic_size)]
+        if np.random.random()<0.02:
+            self.transform_params['rate']['crop'] = [np.random.random(),np.random.random()]
+        if np.random.random()<0.02:
+            self.loadsize = np.random.randint(self.opt.finesize,self.opt.loadsize)
+        
+        if self.t != 0:
+            self.previous_pred = None
+            self.ori_load_pool   [:self.opt.S*self.opt.T-1] = self.ori_load_pool   [1:self.opt.S*self.opt.T]
+            self.mosaic_load_pool[:self.opt.S*self.opt.T-1] = self.mosaic_load_pool[1:self.opt.S*self.opt.T]
+            #print(os.path.join(self.video_dir,'origin_image','%05d' % (self.opt.S*self.opt.T+self.t)+'.jpg'))
+            _ori_img = impro.imread(os.path.join(self.video_dir,'origin_image','%05d' % (self.opt.S*self.opt.T+self.t)+'.jpg'),loadsize=self.loadsize,rgb=True)
+            _mask = impro.imread(os.path.join(self.video_dir,'mask','%05d' % (self.opt.S*self.opt.T+self.t)+'.png' ),mod='gray',loadsize=self.loadsize)
+            _mosaic_img = mosaic.addmosaic_base(_ori_img, _mask, self.mosaic_size,0, self.mod,self.rect_rat,self.feather,self.startpos)
+            _ori_img = data.random_transform_single_image(_ori_img,self.opt.finesize,self.transform_params)
+            _mosaic_img = data.random_transform_single_image(_mosaic_img,self.opt.finesize,self.transform_params)
+            
+            _ori_img,_mosaic_img = self.normalize(_ori_img),self.normalize(_mosaic_img)
+            self.ori_load_pool   [self.opt.S*self.opt.T-1] = _ori_img
+            self.mosaic_load_pool[self.opt.S*self.opt.T-1] = _mosaic_img
+
+            self.ori_stream    = self.ori_load_pool   [np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
+            self.mosaic_stream = self.mosaic_load_pool[np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
+
+            # stream B,T,H,W,C -> B,C,T,H,W
+            self.ori_stream    = self.ori_stream.reshape   (1,self.opt.T,self.opt.finesize,self.opt.finesize,3).transpose((0,4,1,2,3))
+            self.mosaic_stream = self.mosaic_stream.reshape(1,self.opt.T,self.opt.finesize,self.opt.finesize,3).transpose((0,4,1,2,3))
+
+        self.t += 1
+
+class VideoDataLoader(object):
+    """VideoDataLoader"""
+    def __init__(self, opt, videolist, test_flag=False):
+        super(VideoDataLoader, self).__init__()
+        self.videolist = []
+        self.opt = opt
+        self.test_flag = test_flag
+        for i in range(self.opt.n_epoch):
+            self.videolist += videolist.copy()
+        random.shuffle(self.videolist)
+        self.each_video_n_iter = self.opt.M -self.opt.S*(self.opt.T+1)
+        self.n_iter = len(self.videolist)//self.opt.load_thread//self.opt.batchsize*self.each_video_n_iter*self.opt.load_thread
+        self.queue = Queue(self.opt.load_thread)
+        self.ori_stream = np.zeros((self.opt.batchsize,3,self.opt.T,self.opt.finesize,self.opt.finesize),dtype=np.float32)# B,C,T,H,W
+        self.mosaic_stream = np.zeros((self.opt.batchsize,3,self.opt.T,self.opt.finesize,self.opt.finesize),dtype=np.float32)# B,C,T,H,W
+        self.previous_pred = np.zeros((self.opt.batchsize,3,self.opt.finesize,self.opt.finesize),dtype=np.float32)
+        self.load_init()
+
+    def load(self,videolist):
+        for load_video_iter in range(len(videolist)//self.opt.batchsize):
+            iter_videolist = videolist[load_video_iter*self.opt.batchsize:(load_video_iter+1)*self.opt.batchsize]
+            videoloaders = [VideoLoader(self.opt,os.path.join(self.opt.dataset,iter_videolist[i]),self.test_flag) for i in range(self.opt.batchsize)]
+            for each_video_iter in range(self.each_video_n_iter):
+                for i in range(self.opt.batchsize):
+                    self.ori_stream[i] = videoloaders[i].ori_stream
+                    self.mosaic_stream[i] = videoloaders[i].mosaic_stream
+                    if each_video_iter == 0:
+                        self.previous_pred[i] = videoloaders[i].previous_pred
+                    videoloaders[i].next()
+                if each_video_iter == 0:
+                    self.queue.put([self.ori_stream.copy(),self.mosaic_stream.copy(),self.previous_pred])
+                else:
+                    self.queue.put([self.ori_stream.copy(),self.mosaic_stream.copy(),None])
+    
+    def load_init(self):
+        ptvn = len(self.videolist)//self.opt.load_thread #pre_thread_video_num
+        for i in range(self.opt.load_thread):
+            p = Process(target=self.load,args=(self.videolist[i*ptvn:(i+1)*ptvn],))
+            p.daemon = True
+            p.start()
+
+    def get_data(self):
+        return self.queue.get()
\ No newline at end of file
--- a/util/degradater.py
+++ b/util/degradater.py
+'''
+https://github.com/sonack/GFRNet_pytorch_new
+'''
+import random
+import cv2
+import numpy as np
+
+def gaussian_blur(img, sigma=3, size=13):
+    if sigma > 0:
+        if isinstance(size, int):
+            size = (size, size)
+        img = cv2.GaussianBlur(img, size, sigma)
+    return img
+
+def down(img, scale, shape):
+    if scale > 1:
+        h, w, _ = shape
+        scaled_h, scaled_w = int(h / scale), int(w / scale)
+        img = cv2.resize(img, (scaled_w, scaled_h), interpolation = cv2.INTER_CUBIC)
+    return img
+
+def up(img, scale, shape):
+    if scale > 1:
+        h, w, _ = shape
+        img = cv2.resize(img, (w, h), interpolation = cv2.INTER_CUBIC)
+    return img
+    
+def awgn(img, level):
+    if level > 0:
+        noise = np.random.randn(*img.shape) * level
+        img = (img + noise).clip(0,255).astype(np.uint8)
+    return img
+
+def jpeg_compressor(img,quality):
+    if quality > 0:    # 0 indicating no lossy compression (i.e losslessly compression)
+        encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
+        img = cv2.imdecode(cv2.imencode('.jpg', img, encode_param)[1], 1)
+    return img
+
+def get_random_degenerate_params(mod='strong'):
+    '''
+    mod : strong | only_downsample | only_4x | weaker_1 | weaker_2
+    '''
+    params = {}
+    gaussianBlur_size_list = list(range(3,14,2))
+
+    if mod == 'strong':
+        gaussianBlur_sigma_list = [1 + x for x in range(3)]
+        gaussianBlur_sigma_list += [0]
+        downsample_scale_list = [1 + x * 0.1 for x in range(0,71)]
+        awgn_level_list = list(range(1, 8, 1))        
+        jpeg_quality_list = list(range(10, 41, 1))
+        jpeg_quality_list += int(len(jpeg_quality_list) * 0.33) * [0]
+    
+    elif mod == 'only_downsample':
+        gaussianBlur_sigma_list = [0]
+        downsample_scale_list = [1 + x * 0.1 for x in range(0,71)]
+        awgn_level_list = [0]
+        jpeg_quality_list = [0]
+    
+    elif mod == 'only_4x':
+        gaussianBlur_sigma_list = [0]
+        downsample_scale_list = [4]
+        awgn_level_list = [0]
+        jpeg_quality_list = [0]
+
+    elif mod == 'weaker_1':   # 0.5 trigger prob
+        gaussianBlur_sigma_list = [1 + x for x in range(3)]
+        gaussianBlur_sigma_list += int(len(gaussianBlur_sigma_list)) * [0] # 1/2 trigger this degradation
+        
+        downsample_scale_list = [1 + x * 0.1 for x in range(0,71)]
+        downsample_scale_list += int(len(downsample_scale_list)) * [1]
+        
+        awgn_level_list = list(range(1, 8, 1))
+        awgn_level_list += int(len(awgn_level_list)) * [0]
+        
+        jpeg_quality_list = list(range(10, 41, 1))
+        jpeg_quality_list += int(len(jpeg_quality_list)) * [0]
+
+    elif mod == 'weaker_2':    # weaker than weaker_1, jpeg [20,40]
+        gaussianBlur_sigma_list = [1 + x for x in range(3)]
+        gaussianBlur_sigma_list += int(len(gaussianBlur_sigma_list)) * [0] # 1/2 trigger this degradation
+        
+        downsample_scale_list = [1 + x * 0.1 for x in range(0,71)]
+        downsample_scale_list += int(len(downsample_scale_list)) * [1]
+        
+        awgn_level_list = list(range(1, 8, 1))
+        awgn_level_list += int(len(awgn_level_list)) * [0]
+        
+        jpeg_quality_list = list(range(20, 41, 1))
+        jpeg_quality_list += int(len(jpeg_quality_list)) * [0]
+    
+    params['blur_sigma'] = random.choice(gaussianBlur_sigma_list)
+    params['blur_size'] = random.choice(gaussianBlur_size_list)
+    params['updown_scale'] = random.choice(downsample_scale_list)
+    params['awgn_level'] = random.choice(awgn_level_list)
+    params['jpeg_quality'] = random.choice(jpeg_quality_list)
+
+    return params
+
+def degradate(img,params,jpeg_last = True):
+    shape = img.shape
+    if not params:
+        params = get_random_degenerate_params('original')
+        
+    if jpeg_last:
+        img = gaussian_blur(img,params['blur_sigma'],params['blur_size'])
+        img = down(img,params['updown_scale'],shape)
+        img = awgn(img,params['awgn_level'])
+        img = up(img,params['updown_scale'],shape)
+        img = jpeg_compressor(img,params['jpeg_quality'])
+    else:
+        img = gaussian_blur(img,params['blur_sigma'],params['blur_size'])
+        img = down(img,params['updown_scale'],shape)
+        img = awgn(img,params['awgn_level'])
+        img = jpeg_compressor(img,params['jpeg_quality'])
+        img = up(img,params['updown_scale'],shape)
+
+    return img
\ No newline at end of file
--- a/util/ffmpeg.py
+++ b/util/ffmpeg.py
 import os,json
-
+import subprocess
 # ffmpeg 3.4.6

 def args2cmd(args):
@@ -32,17 +32,18 @@ def run(args,mode = 0):
        return sout

 def video2image(videopath, imagepath, fps=0, start_time='00:00:00', last_time='00:00:00'):
-    args = ['ffmpeg', '-i', '"'+videopath+'"']
+    args = ['ffmpeg']
    if last_time != '00:00:00':
        args += ['-ss', start_time]
        args += ['-t', last_time]
+    args += ['-i', '"'+videopath+'"']
    if fps != 0:
        args += ['-r', str(fps)]
    args += ['-f', 'image2','-q:v','-0',imagepath]
    run(args)

 def video2voice(videopath, voicepath, start_time='00:00:00', last_time='00:00:00'):
-    args = ['ffmpeg', '-i', '"'+videopath+'"','-f mp3','-b:a 320k']
+    args = ['ffmpeg', '-i', '"'+videopath+'"','-async 1 -f mp3','-b:a 320k']
    if last_time != '00:00:00':
        args += ['-ss', start_time]
        args += ['-t', last_time]

--- a/util/image_processing.py
+++ b/util/image_processing.py
 import cv2
 import numpy as np
 import random
+from threading import Thread

 import platform

@@ -8,23 +9,14 @@ system_type = 'Linux'
 if 'Windows' in platform.platform():
    system_type = 'Windows'

-DCT_Q = np.array([[8,16,19,22,26,27,29,34],
-                [16,16,22,24,27,29,34,37],
-                [19,22,26,27,29,34,34,38],
-                [22,22,26,27,29,34,37,40],
-                [22,26,27,29,32,35,40,48],
-                [26,27,29,32,35,40,48,58],
-                [26,27,29,34,38,46,56,59],
-                [27,29,35,38,46,56,69,83]])
-
-def imread(file_path,mod = 'normal',loadsize = 0):
+def imread(file_path,mod = 'normal',loadsize = 0, rgb=False):
    '''
    mod:  'normal' | 'gray' | 'all'
    loadsize: 0->original
    '''
    if system_type == 'Linux':
        if mod == 'normal':
-            img = cv2.imread(file_path)
+            img = cv2.imread(file_path,1)
        elif mod == 'gray':
            img = cv2.imread(file_path,0)
        elif mod == 'all':
@@ -33,25 +25,37 @@ def imread(file_path,mod = 'normal',loadsize = 0):
    #In windows, for chinese path, use cv2.imdecode insteaded.
    #It will loss EXIF, I can't fix it
    else: 
-        if mod == 'gray':
+        if mod == 'normal':
+            img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),1)
+        elif mod == 'gray':
            img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),0)
-        else:
+        elif mod == 'all':
            img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),-1)
-    
+            
    if loadsize != 0:
        img = resize(img, loadsize, interpolation=cv2.INTER_CUBIC)

+    if rgb and img.ndim==3:
+        img = img[:,:,::-1]
+
    return img

-def imwrite(file_path,img):
+def imwrite(file_path,img,use_thread=False):
    '''
    in other to save chinese path images in windows,
    this fun just for save final output images
    '''
-    if system_type == 'Linux':
-        cv2.imwrite(file_path, img)
+    def subfun(file_path,img):
+        if system_type == 'Linux':
+            cv2.imwrite(file_path, img)
+        else:
+            cv2.imencode('.jpg', img)[1].tofile(file_path)
+    if use_thread:
+        t = Thread(target=subfun,args=(file_path, img,))
+        t.daemon()
+        t.start
    else:
-        cv2.imencode('.jpg', img)[1].tofile(file_path)
+        subfun(file_path,img)

 def resize(img,size,interpolation=cv2.INTER_LINEAR):
    '''
@@ -108,6 +112,12 @@ def color_adjust(img,alpha=0,beta=0,b=0,g=0,r=0,ran = False):
    
    return (np.clip(img,0,255)).astype('uint8')

+def CAdaIN(src,dst):
+    '''
+    make src has dst's style
+    '''
+    return np.std(dst)*((src-np.mean(src))/np.std(src))+np.mean(dst)
+
 def makedataset(target_image,orgin_image):
    target_image = resize(target_image,256)
    orgin_image = resize(orgin_image,256)
@@ -116,34 +126,6 @@ def makedataset(target_image,orgin_image):
    img[0:256,0:256] = target_image[0:256,int(w/2-256/2):int(w/2+256/2)]
    img[0:256,256:512] = orgin_image[0:256,int(w/2-256/2):int(w/2+256/2)]
    return img
-
-def block_dct_and_idct(g,QQF,QQF_16):
-    return cv2.idct(np.round(16.0*cv2.dct(g)/QQF)*QQF_16)
-
-def image_dct_and_idct(I,QF):
-    h,w = I.shape
-    QQF = DCT_Q*QF
-    QQF_16 = QQF/16.0
-    for i in range(h//8):
-        for j in range(w//8):
-            I[i*8:(i+1)*8,j*8:(j+1)*8] = cv2.idct(np.round(16.0*cv2.dct(I[i*8:(i+1)*8,j*8:(j+1)*8])/QQF)*QQF_16)
-            #I[i*8:(i+1)*8,j*8:(j+1)*8] = block_dct_and_idct(I[i*8:(i+1)*8,j*8:(j+1)*8],QQF,QQF_16)
-    return I
-
-def dctblur(img,Q):
-    '''
-    Q: 1~20, 1->best
-    '''
-    h,w = img.shape[:2]
-    img = img[:8*(h//8),:8*(w//8)]
-    img = img.astype(np.float32)
-    if img.ndim == 2:
-        img = image_dct_and_idct(img, Q)
-    if img.ndim == 3:
-        h,w,ch = img.shape
-        for i in range(ch):
-            img[:,:,i] = image_dct_and_idct(img[:,:,i], Q)
-    return (np.clip(img,0,255)).astype(np.uint8)
    
 def find_mostlikely_ROI(mask):
    contours,hierarchy=cv2.findContours(mask, cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
@@ -210,6 +192,30 @@ def mask_area(mask):
        area = 0
    return area

+def replace_mosaic(img_origin,img_fake,mask,x,y,size,no_feather):
+    img_fake = cv2.resize(img_fake,(size*2,size*2),interpolation=cv2.INTER_CUBIC)
+    if no_feather:
+        img_origin[y-size:y+size,x-size:x+size]=img_fake
+        return img_origin
+    else:
+        # #color correction
+        # RGB_origin = img_origin[y-size:y+size,x-size:x+size].mean(0).mean(0)
+        # RGB_fake = img_fake.mean(0).mean(0)
+        # for i in range(3):img_fake[:,:,i] = np.clip(img_fake[:,:,i]+RGB_origin[i]-RGB_fake[i],0,255)
+        #eclosion
+        eclosion_num = int(size/10)+2
+
+        mask_crop = cv2.resize(mask,(img_origin.shape[1],img_origin.shape[0]))[y-size:y+size,x-size:x+size]
+        mask_crop = ch_one2three(mask_crop)
+
+        mask_crop = (cv2.blur(mask_crop, (eclosion_num, eclosion_num)))
+        mask_crop = mask_crop/255.0
+
+        img_crop = img_origin[y-size:y+size,x-size:x+size]
+        img_origin[y-size:y+size,x-size:x+size] = np.clip((img_crop*(1-mask_crop)+img_fake*mask_crop),0,255).astype('uint8')
+        
+        return img_origin
+

 def Q_lapulase(resImg):
    '''
@@ -223,31 +229,25 @@ def Q_lapulase(resImg):
    score = res.var()
    return score

-def replace_mosaic(img_origin,img_fake,mask,x,y,size,no_feather):
-    img_fake = cv2.resize(img_fake,(size*2,size*2),interpolation=cv2.INTER_LANCZOS4)
-    if no_feather:
-        img_origin[y-size:y+size,x-size:x+size]=img_fake
-        img_result = img_origin
-    else:
-        #color correction
-        RGB_origin = img_origin[y-size:y+size,x-size:x+size].mean(0).mean(0)
-        RGB_fake = img_fake.mean(0).mean(0)
-        for i in range(3):img_fake[:,:,i] = np.clip(img_fake[:,:,i]+RGB_origin[i]-RGB_fake[i],0,255)      
-        #eclosion
-        eclosion_num = int(size/5)
-        entad = int(eclosion_num/2+2)
-
-        mask = cv2.resize(mask,(img_origin.shape[1],img_origin.shape[0]))
-        mask = ch_one2three(mask)
-        
-        mask = (cv2.blur(mask, (eclosion_num, eclosion_num)))
-        mask_tmp = np.zeros_like(mask)
-        mask_tmp[y-size:y+size,x-size:x+size] = mask[y-size:y+size,x-size:x+size]# Fix edge overflow
-        mask = mask_tmp/255.0
-
-        img_tmp = np.zeros(img_origin.shape)
-        img_tmp[y-size:y+size,x-size:x+size]=img_fake
-        img_result = img_origin.copy()
-        img_result = (img_origin*(1-mask)+img_tmp*mask).astype('uint8')
+def psnr(img1,img2):
+    mse = np.mean((img1/255.0-img2/255.0)**2)
+    if mse < 1e-10:
+        return 100
+    psnr_v = 20*np.log10(1/np.sqrt(mse))
+    return psnr_v
+
+def splice(imgs,splice_shape):
+    '''Stitching multiple images, all imgs must have the same size
+    imgs : [img1,img2,img3,img4]
+    splice_shape: (2,2)
+    '''
+    h,w,ch = imgs[0].shape
+    output = np.zeros((h*splice_shape[0],w*splice_shape[1],ch),np.uint8)
+    cnt = 0
+    for i in range(splice_shape[0]):
+        for j in range(splice_shape[1]):
+            if cnt < len(imgs):
+                output[h*i:h*(i+1),w*j:w*(j+1)] = imgs[cnt]
+                cnt += 1
+    return output

-    return img_result
\ No newline at end of file
--- a/util/util.py
+++ b/util/util.py
+import json
 import os
+import random
+import string
 import shutil

 def Traversal(filedir):
@@ -10,6 +13,9 @@ def Traversal(filedir):
            Traversal(dir)
    return file_list

+def randomstr(num):
+    return ''.join(random.sample(string.ascii_letters + string.digits, num))
+
 def is_img(path):
    ext = os.path.splitext(path)[1]
    ext = ext.lower()
@@ -47,13 +53,25 @@ def is_dirs(paths):
            tmp.append(path)
    return tmp  

-def  writelog(path,log,isprint=False):
+def writelog(path,log,isprint=False):
    f = open(path,'a+')
    f.write(log+'\n')
    f.close()
    if isprint:
        print(log)

+def savejson(path,data_dict):
+    json_str = json.dumps(data_dict)
+    f = open(path,'w+')
+    f.write(json_str)
+    f.close()
+
+def loadjson(path):
+    f = open(path, 'r')
+    txt_data = f.read()
+    f.close()
+    return json.loads(txt_data)
+
 def makedirs(path):
    if os.path.isdir(path):
        print(path,'existed')
@@ -70,12 +88,13 @@ def clean_tempfiles(opt,tmp_init=True):
        os.makedirs(tmpdir)
        os.makedirs(os.path.join(tmpdir, 'video2image'))
        os.makedirs(os.path.join(tmpdir, 'addmosaic_image'))
-        os.makedirs(os.path.join(tmpdir, 'mosaic_crop'))
        os.makedirs(os.path.join(tmpdir, 'replace_mosaic'))
        os.makedirs(os.path.join(tmpdir, 'mosaic_mask'))
        os.makedirs(os.path.join(tmpdir, 'ROI_mask'))
-        os.makedirs(os.path.join(tmpdir, 'ROI_mask_check'))
        os.makedirs(os.path.join(tmpdir, 'style_transfer'))
+        # make dataset
+        os.makedirs(os.path.join(tmpdir, 'mosaic_crop'))
+        os.makedirs(os.path.join(tmpdir, 'ROI_mask_check'))
 
 def file_init(opt):
    if not os.path.isdir(opt.result_dir):
@@ -90,11 +109,16 @@ def second2stamp(s):
    s = int(s%60)
    return "%02d:%02d:%02d" % (h, m, s)

-def counttime(t1,t2,now_num,all_num):
+def stamp2second(stamp):
+    substamps = stamp.split(':')
+    return int(substamps[0])*3600 + int(substamps[1])*60 + int(substamps[2])
+
+
+def counttime(start_time,current_time,now_num,all_num):
    '''
-    t1,t2: time.time()
+    start_time,current_time: time.time()
    '''
-    used_time = int(t2-t1)
+    used_time = int(current_time-start_time)
    all_time = int(used_time/now_num*all_num)
    return second2stamp(used_time)+'/'+second2stamp(all_time)