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.gitattributes

@@ -58,3 +58,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+outputs/snapshot/p2s-75000.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+outputs/snapshot/pretrain_clean_line_drawings/p2s-75000.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+outputs/snapshot/pretrain_faces/p2s-90000.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+outputs/snapshot/pretrain_rough_sketches/p2s-90000.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,21 @@
+.idea
+.idea/
+data/
+datas/
+dataset/
+datasets/
+model/
+models/
+testData/
+output/
+outputs/
+
+*.csv
+
+# temporary files
+*.txt~
+*.pyc
+.DS_Store
+.gitignore~
+
+*.h5

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -0,0 +1,178 @@
+# General Virtual Sketching Framework for Vector Line Art - SIGGRAPH 2021
+
+[[Paper]](https://esslab.jp/publications/HaoranSIGRAPH2021.pdf) | [[Project Page]](https://markmohr.github.io/virtual_sketching/) | [[中文Readme]](/README_CN.md) | [[中文论文介绍]](https://blog.csdn.net/qq_33000225/article/details/118883153)
+
+This code is used for **line drawing vectorization**, **rough sketch simplification** and **photograph to vector line drawing**.
+
+<img src='docs/figures/muten.png' height=300><img src='docs/figures/muten-black-full-simplest.gif' height=300>
+
+<img src='docs/figures/rocket.png' height=150><img src='docs/figures/rocket-blue-simplest.gif' height=150>&nbsp;&nbsp;&nbsp;&nbsp;<img src='docs/figures/1390.png' height=150><img src='docs/figures/face-blue-1390-simplest.gif' height=150>
+
+## Outline
+- [Dependencies](#dependencies)
+- [Testing with Trained Weights](#testing-with-trained-weights)
+- [Training](#training)
+- [Citation](#citation)
+- [Projects Using this Model/Method](#projects-using-this-modelmethod)
+- [Blogs Mentioning this Paper](#blogs-mentioning-this-paper)
+- [For Windows users](#-windows-users)
+
+## Dependencies
+ - [Tensorflow](https://www.tensorflow.org/) (1.12.0 <= version <=1.15.0)
+ - [opencv](https://opencv.org/) == 3.4.2
+ - [pillow](https://pillow.readthedocs.io/en/latest/index.html) == 6.2.0
+ - [scipy](https://www.scipy.org/) == 1.5.2
+ - [gizeh](https://github.com/Zulko/gizeh) == 0.1.11
+
+## Testing with Trained Weights
+### Model Preparation
+
+Download the models [here](https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing): 
+  - `pretrain_clean_line_drawings` (105 MB): for vectorization
+  - `pretrain_rough_sketches` (105 MB): for rough sketch simplification
+  - `pretrain_faces` (105 MB): for photograph to line drawing
+
+Then, place them in this file structure:
+```
+outputs/
+    snapshot/
+        pretrain_clean_line_drawings/
+        pretrain_rough_sketches/
+        pretrain_faces/
+```
+
+### Usage
+Choose the image in the `sample_inputs/` directory, and run one of the following commands for each task. The results will be under `outputs/sampling/`.
+
+``` python
+python3 test_vectorization.py --input muten.png
+
+python3 test_rough_sketch_simplification.py --input rocket.png
+
+python3 test_photograph_to_line.py --input 1390.png
+```
+
+**Note!!!** Our approach starts drawing from a randomly selected initial position, so it outputs different results in every testing trial (some might be fine and some might not be good enough). It is recommended to do several trials to select the visually best result. The number of outputs can be defined by the `--sample` argument:
+
+``` python
+python3 test_vectorization.py --input muten.png --sample 10
+
+python3 test_rough_sketch_simplification.py --input rocket.png --sample 10
+
+python3 test_photograph_to_line.py --input 1390.png --sample 10
+```
+
+**Reproducing Paper Figures:** our results (download from [here](https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing)) are selected by doing a certain number of trials. Apparently, it is required to use the same initial drawing positions to reproduce our results.
+
+### Additional Tools
+
+#### a) Visualization
+
+Our vector output is stored in a `npz` package. Run the following command to obtain the rendered output and the drawing order. Results will be under the same directory of the `npz` file.
+``` python
+python3 tools/visualize_drawing.py --file path/to/the/result.npz 
+```
+
+#### b) GIF Making
+
+To see the dynamic drawing procedure, run the following command to obtain the `gif`. Result will be under the same directory of the `npz` file.
+``` python
+python3 tools/gif_making.py --file path/to/the/result.npz 
+```
+
+
+#### c) Conversion to SVG
+
+Our vector output in a `npz` package is stored as Eq.(1) in the main paper. Run the following command to convert it to the `svg` format. Result will be under the same directory of the `npz` file.
+
+``` python
+python3 tools/svg_conversion.py --file path/to/the/result.npz 
+```
+  - The conversion is implemented in two modes (by setting the `--svg_type` argument):
+    - `single` (default): each stroke (a single segment) forms a path in the SVG file
+    - `cluster`: each continuous curve (with multiple strokes) forms a path in the SVG file
+
+**Important Notes**
+
+In SVG format, all the segments on a path share the same *stroke-width*. While in our stroke design,  strokes on a common curve have different widths. Inside a stroke (a single segment), the thickness also changes linearly from an endpoint to another. 
+Therefore, neither of the two conversion methods above generate visually the same results as the ones in our paper.
+*(Please mention this issue in your paper if you do qualitative comparisons with our results in SVG format.)*
+
+
+<br>
+
+## Training
+
+### Preparations
+
+Download the models [here](https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing): 
+  - `pretrain_neural_renderer` (40 MB): the pre-trained neural renderer
+  - `pretrain_perceptual_model` (691 MB): the pre-trained perceptual model for raster loss
+
+Download the datasets [here](https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing): 
+  - `QuickDraw-clean` (14 MB): for clean line drawing vectorization. Taken from [QuickDraw](https://github.com/googlecreativelab/quickdraw-dataset) dataset.
+  - `QuickDraw-rough` (361 MB): for rough sketch simplification. Synthesized by the pencil drawing generation method from [Sketch Simplification](https://github.com/bobbens/sketch_simplification#pencil-drawing-generation).
+  - `CelebAMask-faces` (370 MB): for photograph to line drawing. Processed from the [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ) dataset.
+
+Then, place them in this file structure:
+```
+datasets/
+    QuickDraw-clean/
+    QuickDraw-rough/
+    CelebAMask-faces/
+outputs/
+    snapshot/
+        pretrain_neural_renderer/
+        pretrain_perceptual_model/
+```
+
+### Running
+
+It is recommended to train with multi-GPU. We train each task with 2 GPUs (each with 11 GB).
+
+``` python
+python3 train_vectorization.py
+
+python3 train_rough_photograph.py --data rough
+
+python3 train_rough_photograph.py --data face
+```
+
+<br>
+
+## Citation
+
+If you use the code and models please cite:
+
+```
+@article{mo2021virtualsketching,
+  title   = {General Virtual Sketching Framework for Vector Line Art},
+  author  = {Mo, Haoran and Simo-Serra, Edgar and Gao, Chengying and Zou, Changqing and Wang, Ruomei},
+  journal = {ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH 2021)},
+  year    = {2021},
+  volume  = {40},
+  number  = {4},
+  pages   = {51:1--51:14}
+}
+```
+
+<br>
+
+## Projects Using this Model/Method
+
+| **[Painterly style transfer](https://github.com/xch-liu/Painterly-Style-Transfer) (TVCG 2023)**  | **[Robot calligraphy](https://github.com/LoYuXr/CalliRewrite) (ICRA 2024)** | 
+|:-------------:|:-------------------:|
+| <img src="docs/figures/applications/Painterly-Style-Transfer.png" style="height: 170px"> | <img src="docs/figures/applications/robot-calligraphy.png" style="height: 170px"> |
+| **[Geometrized cartoon line inbetweening](https://github.com/lisiyao21/AnimeInbet) (ICCV 2023)**  | **[Stroke correspondence and inbetweening](https://github.com/MarkMoHR/JoSTC) (TOG 2024)** | 
+| <img src="docs/figures/applications/Geometrized-Cartoon-Line-Inbetweening.png" style="height: 170px"> | <img src="docs/figures/applications/Vector-Line-Inbetweening2.png" style="height: 170px"><img src="docs/figures/applications/Vector-Line-Inbetweening-dynamic1.gif" style="height: 170px"> |
+| **[Modelling complex vector drawings](https://github.com/Co-do/Stroke-Cloud) (ICLR 2024)**  | **[Sketch-to-Image Generation](https://github.com/BlockDetail/Block-and-Detail) (UIST 2024)** | 
+| <img src="docs/figures/applications/complex-vector-drawings.png" style="height: 170px"> | <img src="docs/figures/applications/sketch-to-image.png" style="height: 170px"> |
+
+## Blogs Mentioning this Paper
+
+- [The state of AI for hand-drawn animation inbetweening](https://yosefk.com/blog/the-state-of-ai-for-hand-drawn-animation-inbetweening.html)
+
+## 🪟 Windows users
+
+See [WINDOWS_INSTALL_GUIDE.md](WINDOWS_INSTALL_GUIDE.md) for a complete Windows installation guide and GUI usage.
+

README_CN.md

@@ -0,0 +1,155 @@
+# General Virtual Sketching Framework for Vector Line Art - SIGGRAPH 2021
+
+[[论文]](https://esslab.jp/publications/HaoranSIGRAPH2021.pdf) | [[项目主页]](https://markmohr.github.io/virtual_sketching/)
+
+这份代码能用于实现：**线稿矢量化**、**粗糙草图简化**和**自然图像到矢量草图转换**。
+
+<img src='docs/figures/muten.png' height=300><img src='docs/figures/muten-black-full-simplest.gif' height=300>
+
+<img src='docs/figures/rocket.png' height=150><img src='docs/figures/rocket-blue-simplest.gif' height=150>&nbsp;&nbsp;&nbsp;&nbsp;<img src='docs/figures/1390.png' height=150><img src='docs/figures/face-blue-1390-simplest.gif' height=150>
+
+## 目录
+- [环境依赖](#环境依赖)
+- [使用预训练模型测试](#使用预训练模型测试)
+- [重新训练](#重新训练)
+- [引用](#引用)
+
+## 环境依赖
+ - [Tensorflow](https://www.tensorflow.org/) (1.12.0 <= 版本 <=1.15.0)
+ - [opencv](https://opencv.org/) == 3.4.2
+ - [pillow](https://pillow.readthedocs.io/en/latest/index.html) == 6.2.0
+ - [scipy](https://www.scipy.org/) == 1.5.2
+ - [gizeh](https://github.com/Zulko/gizeh) == 0.1.11
+
+## 使用预训练模型测试
+### 模型下载与准备
+
+在[这里](https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing)下载模型：
+  - `pretrain_clean_line_drawings` (105 MB): 用于线稿矢量化
+  - `pretrain_rough_sketches` (105 MB): 用于粗糙草图简化
+  - `pretrain_faces` (105 MB): 用于自然图像到矢量草图转换
+
+然后，按照如下结构放置模型：
+```
+outputs/
+    snapshot/
+        pretrain_clean_line_drawings/
+        pretrain_rough_sketches/
+        pretrain_faces/
+```
+
+### 测试方法
+在`sample_inputs/`文件夹下选择图像，然后根据任务类型运行下面其中一个命令。生成结果会在`outputs/sampling/`目录下看到。
+
+``` python
+python3 test_vectorization.py --input muten.png
+
+python3 test_rough_sketch_simplification.py --input rocket.png
+
+python3 test_photograph_to_line.py --input 1390.png
+```
+
+**注意!!!** 我们的方法从一个随机挑选的初始位置启动绘制，所以每跑一次测试理论上都会得到一个不同的结果（有可能效果不错，但也可能效果不是很好）。因此，建议做多几次测试来挑选看上去最好的结果。也可以通过设置 `--sample`参数来定义跑一次测试代码同时输出（不同结果）的数量：
+
+``` python
+python3 test_vectorization.py --input muten.png --sample 10
+
+python3 test_rough_sketch_simplification.py --input rocket.png --sample 10
+
+python3 test_photograph_to_line.py --input 1390.png --sample 10
+```
+
+**如何复现论文展示的结果？** 可以从[这里](https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing)下载论文展示的结果。这些是我们通过若干次测试得到不同输出后挑选的最好的结果。显然，若要复现这些结果，需要使用相同的初始位置启动绘制。
+
+### 其他工具
+
+#### a) 可视化
+
+我们的矢量输出均使用`npz` 文件包存储。运行以下的命令可以得到渲染后的结果以及绘制顺序。可以在`npz` 文件包相同的目录下找到这些可视化结果。
+``` python
+python3 tools/visualize_drawing.py --file path/to/the/result.npz 
+```
+
+#### b) GIF制作
+
+若要看到动态的绘制过程，可以运行以下命令来得到 `gif`。结果在`npz` 文件包相同的目录下。
+``` python
+python3 tools/gif_making.py --file path/to/the/result.npz 
+```
+
+
+#### c) 转化为SVG
+
+`npz` 文件包中的矢量结果均按照论文里面的公式(1)格式存储。可以运行以下命令行，来将其转化为 `svg` 文件格式。结果在`npz` 文件包相同的目录下。
+
+``` python
+python3 tools/svg_conversion.py --file path/to/the/result.npz 
+```
+  - 转化过程以两种模式实现（设置`--svg_type`参数）：
+    - `single` (默认模式): 每个笔划（一根单独的曲线）构成SVG文件中的一个path路径
+    - `cluster`: 每个连续曲线（多个笔划）构成SVG文件中的一个path路径
+
+**重要注意事项**
+
+在SVG文件格式中，一个path上的所有线段均只有同一个线宽（*stroke-width*）。然而在我们论文里面，定义一个连续曲线上所有的笔划可以有不同的线宽。同时，对于一个单独的笔划（贝塞尔曲线），定义其线宽从一个端点到另一个端点线性递增或者递减。
+
+因此，上述两个转化方法得到的SVG结果理论上都无法保证跟论文里面的结果在视觉上完全一致。（*假如你在论文里面使用这里转化后的SVG结果进行视觉上的对比，请提及此问题。*）
+
+
+<br>
+
+## 重新训练
+
+### 训练准备
+
+在[这里](https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing)下载模型：
+  - `pretrain_neural_renderer` (40 MB): 预训练好的神经网络渲染器
+  - `pretrain_perceptual_model` (691 MB): 预训练好的perceptual model，用于算 raster loss
+
+在[这里](https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing)下载训练数据集：
+  - `QuickDraw-clean` (14 MB): 用于线稿矢量化。来自 [QuickDraw](https://github.com/googlecreativelab/quickdraw-dataset)数据集。
+  - `QuickDraw-rough` (361 MB): 用于粗糙草图简化。利用[Sketch Simplification](https://github.com/bobbens/sketch_simplification#pencil-drawing-generation)里面的铅笔画图像生成方法合成。
+  - `CelebAMask-faces` (370 MB): 用于自然图像到矢量草图转换。使用[CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ)数据集进行处理后得到。
+
+然后，按照如下结构放置数据集：
+```
+datasets/
+    QuickDraw-clean/
+    QuickDraw-rough/
+    CelebAMask-faces/
+outputs/
+    snapshot/
+        pretrain_neural_renderer/
+        pretrain_perceptual_model/
+```
+
+### 训练方法
+
+建议使用多GPU进行训练。每个任务，我们均使用2个GPU（每个11 GB）来训练。
+
+``` python
+python3 train_vectorization.py
+
+python3 train_rough_photograph.py --data rough
+
+python3 train_rough_photograph.py --data face
+```
+
+<br>
+
+## 引用
+
+若使用此代码和模型，请引用本工作，谢谢！
+
+```
+@article{mo2021virtualsketching,
+  title   = {General Virtual Sketching Framework for Vector Line Art},
+  author  = {Mo, Haoran and Simo-Serra, Edgar and Gao, Chengying and Zou, Changqing and Wang, Ruomei},
+  journal = {ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH 2021)},
+  year    = {2021},
+  volume  = {40},
+  number  = {4},
+  pages   = {51:1--51:14}
+}
+```
+

WINDOWS_INSTALL_GUIDE.md

@@ -0,0 +1,146 @@
+# 🪟 Windows Installation Guide for Virtual Sketching
+
+This guide provides step-by-step instructions to set up and run the [Virtual Sketching](https://github.com/MarkMoHR/virtual_sketching) project on Windows using Anaconda and Python 3.6.
+
+## ✅ Requirements
+
+- Windows 10 or newer
+- Anaconda installed
+- Git installed (optional, but recommended)
+
+---
+
+## 📦 Step 1: Create and Activate Conda Environment
+
+```bash
+conda create -n virtual_sketching python=3.6 -y
+conda activate virtual_sketching
+```
+
+## 📂 Step 2: Clone the Repository
+
+```bash
+cd D:\
+git clone https://github.com/MarkMoHR/virtual_sketching.git
+cd virtual_sketching
+```
+
+(If you plan to contribute, consider forking the repo and using your own URL.)
+
+---
+
+## 🔧 Step 3: Install Required Packages
+
+### From `conda`:
+```bash
+conda install opencv=3.4.2 pillow=6.2.0 scipy=1.5.2 -y
+conda install -c conda-forge pycairo gtk3 cffi -y
+```
+
+### Then remove default TensorFlow (if installed via conda):
+```bash
+conda remove tensorflow
+```
+
+### Install required packages via `pip`:
+```bash
+pip install tensorflow==1.15.0
+pip install numpy gizeh cairocffi matplotlib svgwrite
+```
+
+> ⚠️ Do not upgrade `pillow`, `scipy`, or `tensorflow` — newer versions are incompatible.
+
+---
+
+## 🛠️ Step 4: Fix Backend Compatibility
+
+In `utils.py`, near the top, ensure the following:
+
+```python
+from PIL import Image
+import matplotlib
+matplotlib.use('TkAgg')  # force compatible backend
+import matplotlib.pyplot as plt
+```
+
+This ensures proper rendering with tkinter on Windows.
+
+---
+
+## 🚀 Step 5: Run a Demo
+
+From the project directory:
+
+```bash
+python test_vectorization.py --input sample_inputs\muten.png --sample 5
+```
+
+> ⚠️ This script only generates `.npz` and `.png` files. To convert to `.svg`, see next section.
+
+---
+
+## 🖼️ Optional: Use GUI Tool (Windows Only)
+
+### Step 1: Launch GUI with provided batch file
+
+Use the `runme.bat` file to activate the conda environment and launch the Python GUI:
+
+```bat
+rem runme.bat
+set CONDAPATH=C:\ProgramData\anaconda3
+set ENVNAME=virtual_sketching
+call %CONDAPATH%\Scripts\activate.bat %ENVNAME%
+python virtual_sketch_gui.py
+pause
+```
+
+### Step 2: Select an input image and model
+
+The GUI allows you to:
+- Choose input image (PNG, JPEG, BMP, etc.)
+- Select one of the three models
+- Automatically runs processing and converts results to SVG
+- Saves all outputs into a `sketches/` subfolder of the input image directory
+
+---
+
+## 🧠 Known Compatibility Notes
+
+- Python 3.6 and TensorFlow 1.15 are required (due to use of `tensorflow.contrib`)
+- Windows support requires manual setup of Gizeh and Cairo backends
+- GPU usage optional — TensorFlow 1.15 requires CUDA 10.0 and cuDNN 7
+
+---
+
+## 🧩 Troubleshooting Tips
+
+- ❌ `No module named '_cffi_backend'` → Run: `conda install -c conda-forge cffi`
+- ❌ `ImportError: cannot import name 'draw_svg_from_npz'` → Use `svg_conversion.py` instead
+- ❌ `.svg` looks wrong → Make sure you're using the official `svg_conversion.py` from `tools/`
+- ❌ Missing `.svg`? → Use `virtual_sketch_gui.py` or run `tools/svg_conversion.py` manually on `.npz`
+
+---
+
+## 🤝 Contributing
+
+Feel free to open issues or pull requests if you encounter bugs or want to improve the Windows support!
+
+---
+
+## 📁 Folder Structure Suggestion
+
+```
+virtual_sketching/
+├── sample_inputs/
+├── tools/
+├── outputs/
+├── virtual_sketch_gui.py
+├── runme.bat
+├── README.md
+└── WINDOWS_INSTALL_GUIDE.md   ← You are here
+```
+
+---
+
+Made with ❤️ by the community to help Windows users get started!
+

batch_npz_to_svg.py

@@ -0,0 +1,78 @@
+"""
+Batch convert npz files to SVG, placing each <name>_0.svg next to the
+corresponding <name>_0_pred.png (instead of inside seq_data/<name>/).
+
+Walks `outputs/sampling/simplified/` for every <name>_0.npz under .../seq_data/
+and writes <parent_dir>/<name>_0.svg.
+"""
+import os
+import argparse
+import glob
+import numpy as np
+
+# Reuse the conversion core directly
+from tools.svg_conversion import convert_strokes_to_svg
+
+
+def convert_one(npz_path, svg_type, out_path,
+                min_window_size=32, raster_size=128):
+    data = np.load(npz_path, encoding='latin1', allow_pickle=True)
+    strokes_data = data['strokes_data']
+    init_cursors = data['init_cursors']
+    image_size = data['image_size']
+    round_length = data['round_length']
+    init_width = data['init_width']
+
+    if round_length.ndim == 0:
+        round_lengths = [round_length]
+    else:
+        round_lengths = round_length
+
+    convert_strokes_to_svg(
+        strokes_data, init_cursors, image_size, round_lengths, init_width,
+        min_window_size=min_window_size, raster_size=raster_size,
+        save_path=out_path, svg_type=svg_type)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--root', type=str,
+                        default='outputs/sampling/simplified',
+                        help="Walk this directory for *_0.npz files.")
+    parser.add_argument('--svg_type', type=str,
+                        choices=['single', 'cluster'], default='single')
+    parser.add_argument('--overwrite', action='store_true', default=False)
+    args = parser.parse_args()
+
+    pattern = os.path.join(args.root, '**', 'seq_data', '*.npz')
+    npz_files = sorted(glob.glob(pattern, recursive=True))
+    print('Found {} npz files under {}'.format(len(npz_files), args.root))
+
+    done, skipped, failed = 0, 0, []
+    for npz_path in npz_files:
+        # .../<name>/seq_data/<name>_0.npz  ->  .../<name>/<name>_0.svg
+        seq_dir = os.path.dirname(npz_path)             # .../<name>/seq_data
+        parent_dir = os.path.dirname(seq_dir)           # .../<name>
+        base = os.path.splitext(os.path.basename(npz_path))[0]  # <name>_0
+        out_path = os.path.join(parent_dir, base + '.svg')
+
+        if not args.overwrite and os.path.exists(out_path):
+            skipped += 1
+            continue
+
+        try:
+            convert_one(npz_path, args.svg_type, out_path)
+            done += 1
+            print('[OK] {}'.format(out_path))
+        except Exception as e:
+            failed.append((npz_path, repr(e)))
+            print('[FAIL] {}  err={}'.format(npz_path, repr(e)))
+
+    print('\n=== Done. converted={}, skipped(existing)={}, failed={} ==='
+          .format(done, skipped, len(failed)))
+    for p, e in failed:
+        print('  FAIL', p, e)
+
+
+if __name__ == '__main__':
+    main()

batch_vectorize.py

@@ -0,0 +1,252 @@
+"""
+Batch vectorization script.
+Reads images recursively from sample_inputs/simplified/<subdir>/<img>.png
+and outputs to outputs/sampling/simplified/<subdir>/<img_basename>/
+
+Reuses the core inference logic from test_vectorization.py but loads the model
+ONCE and runs many images in the same TF session to amortize startup time.
+"""
+import os
+import sys
+import time
+import argparse
+import numpy as np
+
+# Limit TF/numpy thread counts BEFORE importing TF, so they take effect.
+# Each shard worker should only use a fraction of the CPU cores to avoid
+# contention when running many shards in parallel.
+_NUM_THREADS = os.environ.get('VS_NUM_THREADS', '')
+if _NUM_THREADS:
+    for _v in ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS',
+               'NUMEXPR_NUM_THREADS', 'TF_NUM_INTRAOP_THREADS',
+               'TF_NUM_INTEROP_THREADS']:
+        os.environ.setdefault(_v, _NUM_THREADS)
+
+import tensorflow as tf
+from PIL import Image
+
+import hyper_parameters as hparams
+from model_common_test import DiffPastingV3, VirtualSketchingModel
+from utils import (reset_graph, load_checkpoint, update_hyperparams,
+                   save_seq_data, draw_strokes)
+from dataset_utils import GeneralRawDataLoader, copy_hparams
+
+# import sample() function from test_vectorization
+from test_vectorization import sample as sample_vectorization
+
+os.environ['CUDA_VISIBLE_DEVICES'] = os.environ.get('CUDA_VISIBLE_DEVICES', '0')
+
+
+def collect_inputs(input_root):
+    """Walk input_root and collect all PNG/JPG files, returning
+    list of (relative_subdir, filename, full_path)."""
+    items = []
+    for dirpath, _, files in os.walk(input_root):
+        rel_subdir = os.path.relpath(dirpath, input_root)
+        for f in sorted(files):
+            if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp')):
+                items.append((rel_subdir, f, os.path.join(dirpath, f)))
+    return items
+
+
+def build_eval_hparams(model_base_dir, model_name):
+    """Create the eval/sample hparams matching what the loaded checkpoint expects."""
+    model_params_default = hparams.get_default_hparams_clean()
+    # update_hyperparams reads the saved model_config.json under model_dir
+    model_params = update_hyperparams(
+        model_params_default, model_base_dir, model_name,
+        infer_dataset='clean_line_drawings')
+
+    eval_model_params = copy_hparams(model_params)
+    eval_model_params.use_input_dropout = 0
+    eval_model_params.use_recurrent_dropout = 0
+    eval_model_params.use_output_dropout = 0
+    eval_model_params.batch_size = 1
+    eval_model_params.model_mode = 'sample'
+
+    sample_model_params = copy_hparams(eval_model_params)
+    sample_model_params.batch_size = 1
+    sample_model_params.max_seq_len = 1
+
+    return eval_model_params, sample_model_params
+
+
+def process_one_image(sess, sampling_model, paste_v3_func,
+                      image_path, eval_hps, sample_hps,
+                      out_dir, image_basename,
+                      longer_infer_lens, state_dependent,
+                      round_stop_state_num, stroke_acc_threshold,
+                      draw_seq=False, draw_color_order=True):
+    """Run inference for one image and dump outputs into out_dir."""
+    os.makedirs(out_dir, exist_ok=True)
+
+    test_set = GeneralRawDataLoader(image_path, eval_hps.raster_size,
+                                    test_dataset='clean_line_drawings')
+
+    input_photos, init_cursors, test_image_size = test_set.get_test_image()
+    # input_photos: (1, image_size, image_size), [0-stroke, 1-BG]
+
+    if init_cursors.ndim == 3:
+        init_cursors = np.expand_dims(init_cursors, axis=0)
+
+    input_photos = input_photos[0:1, :, :]
+    ori_img = (input_photos.copy()[0] * 255.0).astype(np.uint8)
+    ori_img = np.stack([ori_img for _ in range(3)], axis=2)
+    Image.fromarray(ori_img, 'RGB').save(
+        os.path.join(out_dir, image_basename + '_input.png'), 'PNG')
+
+    (strokes_raw_out_list, states_raw_out_list, states_soft_out_list,
+     pred_imgs_out, window_size_out_list, round_new_cursors,
+     round_new_lengths) = sample_vectorization(
+        sess, sampling_model, input_photos, init_cursors, test_image_size,
+        eval_hps.max_seq_len, longer_infer_lens, state_dependent,
+        paste_v3_func, round_stop_state_num, stroke_acc_threshold)
+
+    best_result_idx = 0
+    strokes_raw_out = np.stack(strokes_raw_out_list[best_result_idx], axis=0)
+
+    multi_cursors = [init_cursors[0, best_result_idx, 0]]
+    for c_i in range(len(round_new_cursors)):
+        multi_cursors.append(round_new_cursors[c_i][best_result_idx, 0])
+
+    save_seq_data(out_dir, image_basename + '_0',
+                  strokes_raw_out, multi_cursors,
+                  test_image_size, round_new_lengths, eval_hps.min_width)
+
+    draw_strokes(strokes_raw_out, out_dir, image_basename + '_0_pred.png',
+                 ori_img, test_image_size,
+                 multi_cursors, round_new_lengths,
+                 eval_hps.min_width, eval_hps.cursor_type,
+                 sample_hps.raster_size, sample_hps.min_window_size,
+                 sess, pasting_func=paste_v3_func,
+                 save_seq=draw_seq, draw_order=draw_color_order)
+
+    return strokes_raw_out.shape[0]  # number of strokes
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_root', type=str,
+                        default='sample_inputs/simplified',
+                        help="Root directory of input images.")
+    parser.add_argument('--output_root', type=str,
+                        default='outputs/sampling/simplified',
+                        help="Root directory for outputs.")
+    parser.add_argument('--model', type=str,
+                        default='pretrain_clean_line_drawings')
+    parser.add_argument('--model_base_dir', type=str,
+                        default='outputs/snapshot')
+    parser.add_argument('--progress_log', type=str,
+                        default='outputs/sampling/simplified/_progress.log')
+    parser.add_argument('--skip_existing', action='store_true', default=True)
+    parser.add_argument('--shard', type=str, default='0/1',
+                        help="Shard spec 'i/N': this worker processes items "
+                             "where index%%N == i (0-indexed).")
+    args = parser.parse_args()
+
+    # Parse shard
+    try:
+        shard_i, shard_n = (int(x) for x in args.shard.split('/'))
+    except Exception:
+        raise SystemExit("--shard must look like '0/8'")
+    assert 0 <= shard_i < shard_n, "Bad shard"
+
+    # Hyper-params equivalent to test_vectorization.main()
+    state_dependent = False
+    longer_infer_lens = [500 for _ in range(10)]
+    round_stop_state_num = 12
+    stroke_acc_threshold = 0.95
+
+    np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)
+
+    items = collect_inputs(args.input_root)
+    print('Found {} images under {}'.format(len(items), args.input_root))
+
+    # Apply shard filter
+    items = [it for idx, it in enumerate(items) if idx % shard_n == shard_i]
+    print('Shard {}/{} -> {} images'.format(shard_i, shard_n, len(items)))
+
+    # Build hparams + model ONCE
+    eval_hps, sample_hps = build_eval_hparams(args.model_base_dir, args.model)
+
+    reset_graph()
+    sampling_model = VirtualSketchingModel(sample_hps)
+    paste_v3_func = DiffPastingV3(sample_hps.raster_size)
+
+    tfconfig = tf.ConfigProto()
+    tfconfig.gpu_options.allow_growth = True
+    if _NUM_THREADS:
+        n = int(_NUM_THREADS)
+        tfconfig.intra_op_parallelism_threads = n
+        tfconfig.inter_op_parallelism_threads = n
+    sess = tf.InteractiveSession(config=tfconfig)
+    sess.run(tf.global_variables_initializer())
+
+    model_dir = os.path.join(args.model_base_dir, args.model)
+    snapshot_step = load_checkpoint(sess, model_dir, gen_model_pretrain=True)
+    print('Loaded snapshot_step:', snapshot_step)
+
+    # Per-shard progress log
+    progress_log = args.progress_log
+    if shard_n > 1:
+        base, ext = os.path.splitext(progress_log)
+        progress_log = '{}.shard{}of{}{}'.format(base, shard_i, shard_n, ext)
+    os.makedirs(os.path.dirname(progress_log), exist_ok=True)
+    log_f = open(progress_log, 'a', buffering=1)
+    log_f.write('=== run started at {} (shard {}/{}) ===\n'.format(
+        time.strftime('%F %T'), shard_i, shard_n))
+    log_f.write('total_images={}\n'.format(len(items)))
+
+    total_time = 0.0
+    done = 0
+    failed = []
+
+    for idx, (rel_subdir, fname, full_path) in enumerate(items, 1):
+        basename = os.path.splitext(fname)[0]
+        out_dir = os.path.join(args.output_root, rel_subdir, basename)
+
+        # Skip if already done
+        if args.skip_existing and os.path.exists(
+                os.path.join(out_dir, basename + '_0_pred.png')):
+            print('[{}/{}] SKIP (exists): {}'.format(idx, len(items), full_path))
+            continue
+
+        t0 = time.time()
+        try:
+            num_strokes = process_one_image(
+                sess, sampling_model, paste_v3_func,
+                full_path, eval_hps, sample_hps,
+                out_dir, basename,
+                longer_infer_lens, state_dependent,
+                round_stop_state_num, stroke_acc_threshold)
+            elapsed = time.time() - t0
+            total_time += elapsed
+            done += 1
+            avg = total_time / done
+            line = ('[{}/{}] OK  {:.1f}s  strokes={}  avg={:.1f}s  {}\n'
+                    .format(idx, len(items), elapsed, num_strokes, avg, full_path))
+            print(line, end='')
+            log_f.write(line)
+        except Exception as e:
+            elapsed = time.time() - t0
+            line = ('[{}/{}] FAIL {:.1f}s  {}  err={}\n'
+                    .format(idx, len(items), elapsed, full_path, repr(e)))
+            print(line, end='')
+            log_f.write(line)
+            failed.append(full_path)
+
+    summary = ('\n=== Done. processed={}, failed={}, total_time={:.1f}s, '
+               'avg={:.2f}s/img ===\n').format(
+                done, len(failed), total_time,
+                total_time / max(done, 1))
+    print(summary)
+    log_f.write(summary)
+    if failed:
+        log_f.write('Failed files:\n')
+        for f in failed:
+            log_f.write('  ' + f + '\n')
+    log_f.close()
+
+
+if __name__ == '__main__':
+    main()

dataset_utils.py

@@ -0,0 +1,1224 @@
+import os
+import math
+import random
+import scipy.io
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+
+from rasterization_utils.RealRenderer import GizehRasterizor as RealRenderer
+
+
+def copy_hparams(hparams):
+    """Return a copy of an HParams instance."""
+    return tf.contrib.training.HParams(**hparams.values())
+
+
+class GeneralRawDataLoader(object):
+    def __init__(self,
+                 image_path,
+                 raster_size,
+                 test_dataset):
+        self.image_path = image_path
+        self.raster_size = raster_size
+        self.test_dataset = test_dataset
+
+    def get_test_image(self, random_cursor=True, init_cursor_on_undrawn_pixel=False, init_cursor_num=1):
+        input_image_data, image_size_test = self.gen_input_images(self.image_path)
+        input_image_data = np.array(input_image_data,
+                                    dtype=np.float32)  # (1, image_size, image_size, (3)), [0.0-strokes, 1.0-BG]
+
+        return input_image_data, \
+               self.gen_init_cursors(input_image_data, random_cursor, init_cursor_on_undrawn_pixel, init_cursor_num), \
+               image_size_test
+
+    def gen_input_images(self, image_path):
+        img = Image.open(image_path).convert('RGB')
+        height, width = img.height, img.width
+        max_dim = max(height, width)
+
+        img = np.array(img, dtype=np.uint8)
+
+        if height != width:
+            # Padding to a square image
+            if self.test_dataset == 'clean_line_drawings':
+                pad_value = [255, 255, 255]
+            elif self.test_dataset == 'faces':
+                pad_value = [0, 0, 0]
+            else:
+                # TODO: find better padding pixel value
+                pad_value = img[height - 10, width - 10]
+
+            img_r, img_g, img_b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
+            pad_width = max_dim - width
+            pad_height = max_dim - height
+
+            pad_img_r = np.pad(img_r, ((0, pad_height), (0, pad_width)), 'constant', constant_values=pad_value[0])
+            pad_img_g = np.pad(img_g, ((0, pad_height), (0, pad_width)), 'constant', constant_values=pad_value[1])
+            pad_img_b = np.pad(img_b, ((0, pad_height), (0, pad_width)), 'constant', constant_values=pad_value[2])
+            image_array = np.stack([pad_img_r, pad_img_g, pad_img_b], axis=-1)
+        else:
+            image_array = img
+
+        if self.test_dataset == 'faces' and max_dim != 256:
+            image_array_resize = Image.fromarray(image_array, 'RGB')
+            image_array_resize = image_array_resize.resize(size=(256, 256), resample=Image.BILINEAR)
+            image_array = np.array(image_array_resize, dtype=np.uint8)
+
+        assert image_array.shape[0] == image_array.shape[1]
+        img_size = image_array.shape[0]
+        image_array = image_array.astype(np.float32)
+        if self.test_dataset == 'clean_line_drawings':
+            image_array = image_array[:, :, 0] / 255.0  # [0.0-stroke, 1.0-BG]
+        else:
+            image_array = image_array / 255.0  # [0.0-stroke, 1.0-BG]
+        image_array = np.expand_dims(image_array, axis=0)
+        return image_array, img_size
+
+    def crop_patch(self, image, center, image_size, crop_size):
+        x0 = center[0] - crop_size // 2
+        x1 = x0 + crop_size
+        y0 = center[1] - crop_size // 2
+        y1 = y0 + crop_size
+        x0 = max(0, min(x0, image_size))
+        y0 = max(0, min(y0, image_size))
+        x1 = max(0, min(x1, image_size))
+        y1 = max(0, min(y1, image_size))
+        patch = image[y0:y1, x0:x1]
+        return patch
+
+    def gen_init_cursor_single(self, sketch_image, init_cursor_on_undrawn_pixel, misalign_size=3):
+        # sketch_image: [0.0-stroke, 1.0-BG]
+        image_size = sketch_image.shape[0]
+        if np.sum(1.0 - sketch_image) == 0:
+            center = np.zeros((2), dtype=np.int32)
+            return center
+        else:
+            while True:
+                center = np.random.randint(0, image_size, size=(2))  # (2), in large size
+                patch = 1.0 - self.crop_patch(sketch_image, center, image_size, self.raster_size)
+                if np.sum(patch) != 0:
+                    if not init_cursor_on_undrawn_pixel:
+                        return center.astype(np.float32) / float(image_size)  # (2), in size [0.0, 1.0)
+                    else:
+                        center_patch = 1.0 - self.crop_patch(sketch_image, center, image_size, misalign_size)
+                        if np.sum(center_patch) != 0:
+                            return center.astype(np.float32) / float(image_size)  # (2), in size [0.0, 1.0)
+
+    def gen_init_cursors(self, sketch_data, random_pos=True, init_cursor_on_undrawn_pixel=False, init_cursor_num=1):
+        init_cursor_batch_list = []
+        for cursor_i in range(init_cursor_num):
+            if random_pos:
+                init_cursor_batch = []
+                for i in range(len(sketch_data)):
+                    sketch_image = sketch_data[i].copy().astype(np.float32)  # [0.0-stroke, 1.0-BG]
+                    center = self.gen_init_cursor_single(sketch_image, init_cursor_on_undrawn_pixel)
+                    init_cursor_batch.append(center)
+
+                init_cursor_batch = np.stack(init_cursor_batch, axis=0)  # (N, 2)
+            else:
+                raise Exception('Not finished')
+            init_cursor_batch_list.append(init_cursor_batch)
+
+        if init_cursor_num == 1:
+            init_cursor_batch = init_cursor_batch_list[0]
+            init_cursor_batch = np.expand_dims(init_cursor_batch, axis=1).astype(np.float32)  # (N, 1, 2)
+        else:
+            init_cursor_batch = np.stack(init_cursor_batch_list, axis=1)  # (N, init_cursor_num, 2)
+            init_cursor_batch = np.expand_dims(init_cursor_batch, axis=2).astype(
+                np.float32)  # (N, init_cursor_num, 1, 2)
+
+        return init_cursor_batch
+
+
+def load_dataset_testing(test_data_base_dir, test_dataset, test_img_name, model_params):
+    assert test_dataset in ['clean_line_drawings', 'rough_sketches', 'faces']
+    img_path = os.path.join(test_data_base_dir, test_dataset, test_img_name)
+    print('Loaded {} from {}'.format(img_path, test_dataset))
+
+    eval_model_params = copy_hparams(model_params)
+    eval_model_params.use_input_dropout = 0
+    eval_model_params.use_recurrent_dropout = 0
+    eval_model_params.use_output_dropout = 0
+    eval_model_params.batch_size = 1
+    eval_model_params.model_mode = 'sample'
+
+    sample_model_params = copy_hparams(eval_model_params)
+    sample_model_params.batch_size = 1  # only sample one at a time
+    sample_model_params.max_seq_len = 1  # sample one point at a time
+
+    test_set = GeneralRawDataLoader(img_path, eval_model_params.raster_size, test_dataset=test_dataset)
+
+    result = [test_set, eval_model_params, sample_model_params]
+    return result
+
+
+class GeneralMultiObjectDataLoader(object):
+    def __init__(self,
+                 stroke3_data,
+                 batch_size,
+                 raster_size,
+                 image_size_small,
+                 image_size_large,
+                 is_bin,
+                 is_train):
+        self.batch_size = batch_size  # minibatch size
+        self.raster_size = raster_size
+        self.image_size_small = image_size_small
+        self.image_size_large = image_size_large
+        self.is_bin = is_bin
+        self.is_train = is_train
+
+        self.num_batches = len(stroke3_data) // self.batch_size
+        self.batch_idx = -1
+        print('batch_size', batch_size, ', num_batches', self.num_batches)
+
+        self.rasterizor = RealRenderer()
+        self.memory_sketch_data_batch = []
+
+        assert type(stroke3_data) is list
+        self.preprocess_rand_data(stroke3_data)
+
+    def preprocess_rand_data(self, stroke3):
+        if self.is_train:
+            random.shuffle(stroke3)
+        self.stroke3_data = stroke3
+
+    def cal_dist(self, posA, posB):
+        return np.sqrt(np.sum(np.power(posA - posB, 2)))
+
+    def invalid_position(self, pos, obj_size, pos_list, size_list):
+        if len(pos_list) == 0:
+            return False
+
+        pos_a = pos
+        size_a = obj_size
+        for i in range(len(pos_list)):
+            pos_b = pos_list[i]
+            size_b = size_list[i]
+
+            if self.cal_dist(pos_a, pos_b) < ((size_a + size_b) // 4):
+                return True
+
+        return False
+
+    def get_object_info(self, image_size, vary_thickness=True, try_total_times=3):
+        if image_size <= 172:
+            obj_num = 1
+            obj_thickness_list = [3]
+        elif image_size <= 225:
+            obj_num = random.randint(1, 2)
+            obj_thickness_list = np.random.randint(3, 4 + 1, size=(obj_num))
+        elif image_size <= 278:
+            obj_num = 2
+            obj_thickness_list = np.random.randint(3, 4 + 1, size=(obj_num))
+        elif image_size <= 331:
+            obj_num = random.randint(2, 3)
+            while True:
+                obj_thickness_list = np.random.randint(3, 5 + 1, size=(obj_num))
+                if np.sum(obj_thickness_list) / obj_num != 5 and np.sum(obj_thickness_list) < 13:
+                    break
+        elif image_size <= 384:
+            obj_num = 3
+            while True:
+                obj_thickness_list = np.random.randint(3, 5 + 1, size=(obj_num))
+                if np.sum(obj_thickness_list) / obj_num != 5 and np.sum(obj_thickness_list) < 13:
+                    break
+        else:
+            raise Exception('Invalid image_size', image_size)
+
+        if not vary_thickness:
+            num_item = len(obj_thickness_list)
+            obj_thickness_list = [3 for _ in range(num_item)]
+
+        obj_pos_list = []
+        obj_size_list = []
+        if obj_num == 1:
+            obj_size_list.append(image_size)
+            center = (image_size // 2, image_size // 2)
+            obj_pos_list.append(center)
+        else:
+            for obj_i in range(obj_num):
+                for try_i in range(try_total_times):
+                    obj_size = random.randint(128, image_size * 3 // 4)
+                    obj_center = np.random.randint(obj_size // 3, image_size - (obj_size // 3) + 1, size=(2))
+
+                    if not self.invalid_position(obj_center, obj_size, obj_pos_list,
+                                                 obj_size_list) or try_i == try_total_times - 1:
+                        obj_pos_list.append(obj_center)
+                        obj_size_list.append(obj_size)
+                        break
+
+        assert len(obj_size_list) == len(obj_pos_list) == len(obj_thickness_list) == obj_num
+        return obj_num, obj_size_list, obj_pos_list, obj_thickness_list
+
+    def object_pasting(self, obj_img, canvas_img, center):
+        c_y, c_x = center[0], center[1]
+        obj_size = obj_img.shape[0]
+        canvas_size = canvas_img.shape[0]
+        box_left = max(0, c_x - obj_size // 2)
+        box_right = min(canvas_size, c_x + obj_size // 2)
+        box_up = max(0, c_y - obj_size // 2)
+        box_bottom = min(canvas_size, c_y + obj_size // 2)
+
+        box_canvas = canvas_img[box_up: box_bottom, box_left: box_right]
+
+        obj_box_up = box_up - (c_y - obj_size // 2)
+        obj_box_left = box_left - (c_x - obj_size // 2)
+        box_obj = obj_img[obj_box_up: obj_box_up + (box_bottom - box_up),
+                  obj_box_left: obj_box_left + (box_right - box_left)]
+
+        box_canvas += box_obj
+
+        rst_canvas = np.copy(canvas_img)
+        rst_canvas[box_up: box_bottom, box_left: box_right] = box_canvas
+        rst_canvas = np.clip(rst_canvas, 0.0, 1.0)
+
+        return rst_canvas
+
+    def get_multi_object_image(self, img_size, vary_thickness):
+        object_num, object_size_list, object_pos_list, object_thickness_list = self.get_object_info(
+            img_size, vary_thickness=vary_thickness)
+
+        canvas = np.zeros(shape=(img_size, img_size), dtype=np.float32)
+
+        for obj_i in range(object_num):
+            rand_idx = np.random.randint(0, len(self.stroke3_data))
+            rand_stroke3 = self.stroke3_data[rand_idx]  # (N_points, 3)
+
+            object_size = object_size_list[obj_i]
+            object_enter = object_pos_list[obj_i]
+            object_thickness = object_thickness_list[obj_i]
+
+            stroke_image = self.gen_stroke_images([rand_stroke3], object_size, object_thickness)
+            stroke_image = 1.0 - stroke_image[0]  # (image_size, image_size), [0.0-BG, 1.0-strokes]
+
+            canvas = self.object_pasting(stroke_image, canvas, object_enter)  # [0.0-BG, 1.0-strokes]
+
+        canvas = 1.0 - canvas  # [0.0-strokes, 1.0-BG]
+        return canvas
+
+    def get_batch_from_memory(self, memory_idx, vary_thickness, fixed_image_size=-1, random_cursor=True,
+                              init_cursor_on_undrawn_pixel=False, init_cursor_num=1):
+        if len(self.memory_sketch_data_batch) >= memory_idx + 1:
+            sketch_data_batch = self.memory_sketch_data_batch[memory_idx]
+            sketch_data_batch = np.expand_dims(sketch_data_batch,
+                                               axis=0)  # (1, image_size, image_size), [0.0-strokes, 1.0-BG]
+            image_size_rand = sketch_data_batch.shape[1]
+        else:
+            if fixed_image_size == -1:
+                image_size_rand = random.randint(self.image_size_small, self.image_size_large)
+            else:
+                image_size_rand = fixed_image_size
+
+            multi_obj_image = self.get_multi_object_image(image_size_rand, vary_thickness)  # [0.0-strokes, 1.0-BG]
+            self.memory_sketch_data_batch.append(multi_obj_image)
+            sketch_data_batch = np.expand_dims(multi_obj_image,
+                                               axis=0)  # (1, image_size, image_size), [0.0-strokes, 1.0-BG]
+
+        return None, sketch_data_batch, \
+               self.gen_init_cursors(sketch_data_batch, random_cursor, init_cursor_on_undrawn_pixel, init_cursor_num), \
+               image_size_rand
+
+    def get_batch_multi_res(self, loop_num, vary_thickness, random_cursor=True,
+                            init_cursor_on_undrawn_pixel=False, init_cursor_num=1):
+        sketch_data_batch = []
+        init_cursors_batch = []
+        image_size_batch = []
+        batch_size_per_loop = self.batch_size // loop_num
+        for loop_i in range(loop_num):
+            image_size_rand = random.randint(self.image_size_small, self.image_size_large)
+            sketch_data_sub_batch = []
+            for batch_i in range(batch_size_per_loop):
+                multi_obj_image = self.get_multi_object_image(image_size_rand, vary_thickness)  # [0.0-strokes, 1.0-BG]
+                sketch_data_sub_batch.append(multi_obj_image)
+            sketch_data_sub_batch = np.stack(sketch_data_sub_batch,
+                                             axis=0)  # (N, image_size, image_size), [0.0-strokes, 1.0-BG]
+
+            init_cursors_sub_batch = self.gen_init_cursors(sketch_data_sub_batch, random_cursor,
+                                                           init_cursor_on_undrawn_pixel, init_cursor_num)
+            sketch_data_batch.append(sketch_data_sub_batch)
+            init_cursors_batch.append(init_cursors_sub_batch)
+            image_size_batch.append(image_size_rand)
+
+        return None, \
+               sketch_data_batch, \
+               init_cursors_batch, \
+               image_size_batch
+
+    def gen_stroke_images(self, stroke3_list, image_size, stroke_width):
+        """
+        :param stroke3_list: list of (batch_size,), each with (N_points, 3)
+        :param image_size:
+        :return:
+        """
+        gt_image_array = self.rasterizor.raster_func(stroke3_list, image_size, stroke_width=stroke_width,
+                                                     is_bin=self.is_bin, version='v2')
+        gt_image_array = np.stack(gt_image_array, axis=0)
+        gt_image_array = 1.0 - gt_image_array  # (batch_size, image_size, image_size), [0.0-strokes, 1.0-BG]
+        return gt_image_array
+
+    def crop_patch(self, image, center, image_size, crop_size):
+        x0 = center[0] - crop_size // 2
+        x1 = x0 + crop_size
+        y0 = center[1] - crop_size // 2
+        y1 = y0 + crop_size
+        x0 = max(0, min(x0, image_size))
+        y0 = max(0, min(y0, image_size))
+        x1 = max(0, min(x1, image_size))
+        y1 = max(0, min(y1, image_size))
+        patch = image[y0:y1, x0:x1]
+        return patch
+
+    def gen_init_cursor_single(self, sketch_image, init_cursor_on_undrawn_pixel, misalign_size=3):
+        # sketch_image: [0.0-stroke, 1.0-BG]
+        image_size = sketch_image.shape[0]
+        if np.sum(1.0 - sketch_image) == 0:
+            center = np.zeros((2), dtype=np.int32)
+            return center
+        else:
+            while True:
+                center = np.random.randint(0, image_size, size=(2))  # (2), in large size
+                patch = 1.0 - self.crop_patch(sketch_image, center, image_size, self.raster_size)
+                if np.sum(patch) != 0:
+                    if not init_cursor_on_undrawn_pixel:
+                        return center.astype(np.float32) / float(image_size)  # (2), in size [0.0, 1.0)
+                    else:
+                        center_patch = 1.0 - self.crop_patch(sketch_image, center, image_size, misalign_size)
+                        if np.sum(center_patch) != 0:
+                            return center.astype(np.float32) / float(image_size)  # (2), in size [0.0, 1.0)
+
+    def gen_init_cursors(self, sketch_data, random_pos=True, init_cursor_on_undrawn_pixel=False, init_cursor_num=1):
+        init_cursor_batch_list = []
+        for cursor_i in range(init_cursor_num):
+            if random_pos:
+                init_cursor_batch = []
+                for i in range(len(sketch_data)):
+                    sketch_image = sketch_data[i].copy().astype(np.float32)  # [0.0-stroke, 1.0-BG]
+                    center = self.gen_init_cursor_single(sketch_image, init_cursor_on_undrawn_pixel)
+                    init_cursor_batch.append(center)
+
+                init_cursor_batch = np.stack(init_cursor_batch, axis=0)  # (N, 2)
+            else:
+                raise Exception('Not finished')
+            init_cursor_batch_list.append(init_cursor_batch)
+
+        if init_cursor_num == 1:
+            init_cursor_batch = init_cursor_batch_list[0]
+            init_cursor_batch = np.expand_dims(init_cursor_batch, axis=1).astype(np.float32)  # (N, 1, 2)
+        else:
+            init_cursor_batch = np.stack(init_cursor_batch_list, axis=1)  # (N, init_cursor_num, 2)
+            init_cursor_batch = np.expand_dims(init_cursor_batch, axis=2).astype(
+                np.float32)  # (N, init_cursor_num, 1, 2)
+
+        return init_cursor_batch
+
+
+def load_dataset_multi_object(dataset_base_dir, model_params):
+    train_stroke3_data = []
+    val_stroke3_data = []
+
+    if model_params.data_set == 'clean_line_drawings':
+        def load_qd_npz_data(npz_path):
+            data = np.load(npz_path, encoding='latin1', allow_pickle=True)
+            selected_strokes3 = data['stroke3']  # (N_sketches,), each with (N_points, 3)
+            selected_strokes3 = selected_strokes3.tolist()
+            return selected_strokes3
+
+        base_dir_clean = 'QuickDraw-clean'
+        cates = ['airplane', 'bus', 'car', 'sailboat', 'bird', 'cat', 'dog',
+                 # 'rabbit',
+                 'tree', 'flower',
+                 # 'circle', 'line',
+                 'zigzag'
+                 ]
+
+        for cate in cates:
+            train_cate_sketch_data_npz_path = os.path.join(dataset_base_dir, base_dir_clean, 'train', cate + '.npz')
+            val_cate_sketch_data_npz_path = os.path.join(dataset_base_dir, base_dir_clean, 'test', cate + '.npz')
+            print(train_cate_sketch_data_npz_path)
+
+            train_cate_stroke3_data = load_qd_npz_data(
+                train_cate_sketch_data_npz_path)  # list of (N_sketches,), each with (N_points, 3)
+            val_cate_stroke3_data = load_qd_npz_data(val_cate_sketch_data_npz_path)
+            train_stroke3_data += train_cate_stroke3_data
+            val_stroke3_data += val_cate_stroke3_data
+    else:
+        raise Exception('Unknown data type:', model_params.data_set)
+
+    print('Loaded {}/{} from {}'.format(len(train_stroke3_data), len(val_stroke3_data), model_params.data_set))
+    print('model_params.max_seq_len %i.' % model_params.max_seq_len)
+
+    eval_sample_model_params = copy_hparams(model_params)
+    eval_sample_model_params.use_input_dropout = 0
+    eval_sample_model_params.use_recurrent_dropout = 0
+    eval_sample_model_params.use_output_dropout = 0
+    eval_sample_model_params.batch_size = 1  # only sample one at a time
+    eval_sample_model_params.model_mode = 'eval_sample'
+
+    train_set = GeneralMultiObjectDataLoader(train_stroke3_data,
+                                             model_params.batch_size, model_params.raster_size,
+                                             model_params.image_size_small, model_params.image_size_large,
+                                             model_params.bin_gt, is_train=True)
+    val_set = GeneralMultiObjectDataLoader(val_stroke3_data,
+                                           eval_sample_model_params.batch_size, eval_sample_model_params.raster_size,
+                                           eval_sample_model_params.image_size_small,
+                                           eval_sample_model_params.image_size_large,
+                                           eval_sample_model_params.bin_gt, is_train=False)
+
+    result = [train_set, val_set, model_params, eval_sample_model_params]
+    return result
+
+
+class GeneralDataLoaderMultiObjectRough(object):
+    def __init__(self,
+                 photo_data,
+                 sketch_data,
+                 texture_data,
+                 shadow_data,
+                 batch_size,
+                 raster_size,
+                 image_size_small,
+                 image_size_large,
+                 is_train):
+        self.batch_size = batch_size  # minibatch size
+        self.raster_size = raster_size
+        self.image_size_small = image_size_small
+        self.image_size_large = image_size_large
+        self.is_train = is_train
+
+        assert photo_data is not None
+        assert len(photo_data) == len(sketch_data)
+        # self.num_batches = len(sketch_data) // self.batch_size
+        self.batch_idx = -1
+        print('batch_size', batch_size)
+
+        assert type(photo_data) is list
+        assert type(sketch_data) is list
+        assert type(texture_data) is list and len(texture_data) > 0
+        assert type(shadow_data) is list and len(shadow_data) > 0
+        self.photo_data = photo_data
+        self.sketch_data = sketch_data
+        self.texture_data = texture_data  # list of (H, W, 3), [0, 255], uint8
+        self.shadow_data = shadow_data  # list of (H, W), [0, 255], uint8
+
+        self.memory_photo_data_batch = []
+        self.memory_sketch_data_batch = []
+
+    def rough_augmentation(self, raw_photo, texture_prob=0.20, noise_prob=0.15, shadow_prob=0.20):
+        # raw_photo: (H, W), [0.0-stroke, 1.0-BG]
+        aug_photo_rgb = np.stack([raw_photo for _ in range(3)], axis=-1)
+
+        def texture_generation(texture_list, image_shape):
+            while True:
+                random_texture_id = random.randint(0, len(texture_list) - 1)
+                texture_large = texture_list[random_texture_id]
+                t_w, t_h = texture_large.shape[1], texture_large.shape[0]
+                i_w, i_h = image_shape[1], image_shape[0]
+
+                if t_h >= i_h and t_w >= i_w:
+                    texture_large = np.copy(texture_large).astype(np.float32)
+                    crop_y = random.randint(0, t_h - i_h)
+                    crop_x = random.randint(0, t_w - i_w)
+                    crop_texture = texture_large[crop_y: crop_y + i_h, crop_x: crop_x + i_w, :]
+                    return crop_texture
+
+        def texture_change(rough_img_, all_textures):
+            # rough_img_: (H, W, 3), [0.0-stroke, 1.0-BG]
+
+            texture_image = texture_generation(all_textures, rough_img_.shape)  # (h, w, 3)
+            texture_image /= 255.0
+
+            rand_b = np.random.uniform(1.0, 2.0, size=rough_img_.shape)
+            textured_img = rough_img_ * (texture_image / rand_b + (rand_b - 1.0) / rand_b)  # [0.0, 1.0]
+            return textured_img
+
+        def noise_change(rough_img_, noise_scale=25):
+            # rough_img_: (H, W, 3), [0.0, 1.0]
+            rough_img_255 = rough_img_ * 255.0
+
+            rand_noise = np.random.uniform(-1.0, 1.0, size=rough_img_255.shape) * noise_scale
+            # rand_noise = np.random.normal(size=rough_img.shape) * noise_scale
+            noise_img = rough_img_255 + rand_noise
+            noise_img = np.clip(noise_img, 0.0, 255.0)
+            noise_img /= 255.0
+            return noise_img
+
+        def shadow_change(rough_img_, all_shadows):
+            # rough_img_: (H, W, 3), [0.0, 1.0]
+            rough_img_255 = rough_img_ * 255.0
+
+            shadow_i = random.randint(0, len(all_shadows) - 1)
+            shadow_full = all_shadows[shadow_i]  # (H, W), [0, 255]
+            shadow_img_size = shadow_full.shape[0]
+
+            while True:
+                position = np.random.randint(-shadow_img_size // 2, shadow_img_size // 2, (2))
+                if abs(position[0]) > (shadow_img_size // 8) and abs(position[1]) > (shadow_img_size // 8):
+                    break
+            position += (shadow_img_size // 2)
+
+            crop_up = shadow_img_size - position[0]
+            crop_left = shadow_img_size - position[1]
+
+            shadow_image_large = shadow_full[crop_up: crop_up + shadow_img_size, crop_left: crop_left + shadow_img_size]
+            shadow_bg = Image.fromarray(shadow_image_large, 'L')
+            shadow_bg = shadow_bg.resize(size=(rough_img_255.shape[1], rough_img_255.shape[0]), resample=Image.BILINEAR)
+            shadow_bg = np.array(shadow_bg, dtype=np.float32) / 255.0  # [0.0-shadow, 1.0-BG]
+            shadow_bg = np.stack([shadow_bg for _ in range(3)], axis=-1)
+
+            shadow_img = rough_img_255 * shadow_bg
+            shadow_img /= 255.0
+            return shadow_img
+
+        if random.random() <= texture_prob:
+            aug_photo_rgb = texture_change(aug_photo_rgb, self.texture_data)  # (H, W, 3), [0.0, 1.0]
+        if random.random() <= noise_prob:
+            aug_photo_rgb = noise_change(aug_photo_rgb)  # (H, W, 3), [0.0, 1.0]
+        if random.random() <= shadow_prob:
+            aug_photo_rgb = shadow_change(aug_photo_rgb, self.shadow_data)  # (H, W, 3), [0.0, 1.0]
+
+        return aug_photo_rgb
+
+    def image_interpolation(self, photo, sketch, photo_prob):
+        interp_photo = photo * photo_prob + sketch * (1.0 - photo_prob)
+        interp_photo = np.clip(interp_photo, 0.0, 1.0)
+        return interp_photo
+
+    def get_batch_from_memory(self, memory_idx, interpolate_type, fixed_image_size=-1, random_cursor=True,
+                              photo_prob=1.0, init_cursor_num=1):
+        if len(self.memory_sketch_data_batch) >= memory_idx + 1:
+            photo_data_batch = self.memory_photo_data_batch[memory_idx]
+            sketch_data_batch = self.memory_sketch_data_batch[memory_idx]
+            image_size_rand = sketch_data_batch.shape[1]
+        else:
+            if fixed_image_size == -1:
+                image_size_rand = random.randint(self.image_size_small, self.image_size_large)
+            else:
+                image_size_rand = fixed_image_size
+
+            # photo_prob = 0.0 if photo_prob_type == 'zero' else 1.0
+            photo_data_batch, sketch_data_batch = self.select_sketch(
+                image_size_rand)  # both: (H, W), [0.0-stroke, 1.0-BG]
+            photo_data_batch = self.rough_augmentation(photo_data_batch)  # (H, W, 3), [0.0-stroke, 1.0-BG]
+
+            self.memory_photo_data_batch.append(photo_data_batch)
+            self.memory_sketch_data_batch.append(sketch_data_batch)
+
+        if interpolate_type == 'prob':
+            if random.random() >= photo_prob:
+                photo_data_batch = np.stack([sketch_data_batch for _ in range(3)],
+                                            axis=-1)  # (H, W, 3), [0.0-stroke, 1.0-BG]
+        elif interpolate_type == 'image':
+            photo_data_batch = self.image_interpolation(
+                photo_data_batch, np.stack([sketch_data_batch for _ in range(3)], axis=-1), photo_prob)
+        else:
+            raise Exception('Unknown interpolate_type', interpolate_type)
+
+        photo_data_batch = np.expand_dims(photo_data_batch, axis=0)  # (1, image_size, image_size, 3)
+        sketch_data_batch = np.expand_dims(sketch_data_batch,
+                                           axis=0)  # (1, image_size, image_size), [0.0-strokes, 1.0-BG]
+
+        return photo_data_batch, sketch_data_batch, \
+               self.gen_init_cursors(sketch_data_batch, random_cursor, init_cursor_num), image_size_rand
+
+    def select_sketch(self, image_size_rand):
+        resolution_idx = image_size_rand - self.image_size_small
+        img_idx = random.randint(0, len(self.sketch_data[resolution_idx]) - 1)
+        assert img_idx != -1
+
+        selected_sketch = self.sketch_data[resolution_idx][img_idx]  # [0-stroke, 255-BG], uint8
+        selected_photo = self.photo_data[resolution_idx][img_idx]  # [0-stroke, 255-BG], uint8
+
+        rst_sketch_image = selected_sketch.astype(np.float32) / 255.0  # [0.0-stroke, 1.0-BG]
+        rst_photo_image = selected_photo.astype(np.float32) / 255.0  # [0.0-stroke, 1.0-BG]
+
+        return rst_photo_image, rst_sketch_image
+
+    def get_batch_multi_res(self, loop_num, interpolate_type, random_cursor=True, init_cursor_num=1, photo_prob=1.0):
+        photo_data_batch = []
+        sketch_data_batch = []
+        init_cursors_batch = []
+        image_size_batch = []
+        batch_size_per_loop = self.batch_size // loop_num
+        for loop_i in range(loop_num):
+            image_size_rand = random.randint(self.image_size_small, self.image_size_large)
+
+            photo_data_sub_batch = []
+            sketch_data_sub_batch = []
+            for img_i in range(batch_size_per_loop):
+                photo_patch, sketch_patch = self.select_sketch(image_size_rand)  # both: (H, W), [0.0-stroke, 1.0-BG]
+                photo_patch = self.rough_augmentation(photo_patch)  # (H, W, 3), [0.0-stroke, 1.0-BG]
+
+                if interpolate_type == 'prob':
+                    if random.random() >= photo_prob:
+                        photo_patch = np.stack([sketch_patch for _ in range(3)],
+                                               axis=-1)  # (H, W, 3), [0.0-stroke, 1.0-BG]
+                elif interpolate_type == 'image':
+                    photo_patch = self.image_interpolation(
+                        photo_patch, np.stack([sketch_patch for _ in range(3)], axis=-1), photo_prob)
+                else:
+                    raise Exception('Unknown interpolate_type', interpolate_type)
+
+                photo_data_sub_batch.append(photo_patch)
+                sketch_data_sub_batch.append(sketch_patch)
+
+            photo_data_sub_batch = np.stack(photo_data_sub_batch,
+                                            axis=0)  # (N, image_size, image_size, 3), [0.0-strokes, 1.0-BG]
+            sketch_data_sub_batch = np.stack(sketch_data_sub_batch,
+                                             axis=0)  # (N, image_size, image_size), [0.0-strokes, 1.0-BG]
+            init_cursors_sub_batch = self.gen_init_cursors(sketch_data_sub_batch, random_cursor, init_cursor_num)
+            photo_data_batch.append(photo_data_sub_batch)
+            sketch_data_batch.append(sketch_data_sub_batch)
+            init_cursors_batch.append(init_cursors_sub_batch)
+            image_size_batch.append(image_size_rand)
+
+        return photo_data_batch, sketch_data_batch, init_cursors_batch, image_size_batch
+
+    def crop_patch(self, image, center, image_size, crop_size):
+        x0 = center[0] - crop_size // 2
+        x1 = x0 + crop_size
+        y0 = center[1] - crop_size // 2
+        y1 = y0 + crop_size
+        x0 = max(0, min(x0, image_size))
+        y0 = max(0, min(y0, image_size))
+        x1 = max(0, min(x1, image_size))
+        y1 = max(0, min(y1, image_size))
+        patch = image[y0:y1, x0:x1]
+        return patch
+
+    def gen_init_cursor_single(self, sketch_image):
+        # sketch_image: [0.0-stroke, 1.0-BG]
+        image_size = sketch_image.shape[0]
+        if np.sum(1.0 - sketch_image) == 0:
+            center = np.zeros((2), dtype=np.int32)
+            return center
+        else:
+            while True:
+                center = np.random.randint(0, image_size, size=(2))  # (2), in large size
+                patch = 1.0 - self.crop_patch(sketch_image, center, image_size, self.raster_size)
+                if np.sum(patch) != 0:
+                    return center.astype(np.float32) / float(image_size)  # (2), in size [0.0, 1.0)
+
+    def gen_init_cursors(self, sketch_data, random_pos=True, init_cursor_num=1):
+        init_cursor_batch_list = []
+        for cursor_i in range(init_cursor_num):
+            if random_pos:
+                init_cursor_batch = []
+                for i in range(len(sketch_data)):
+                    sketch_image = sketch_data[i].copy().astype(np.float32)  # [0.0-stroke, 1.0-BG]
+                    center = self.gen_init_cursor_single(sketch_image)
+                    init_cursor_batch.append(center)
+
+                init_cursor_batch = np.stack(init_cursor_batch, axis=0)  # (N, 2)
+            else:
+                raise Exception('Not finished')
+            init_cursor_batch_list.append(init_cursor_batch)
+
+        if init_cursor_num == 1:
+            init_cursor_batch = init_cursor_batch_list[0]
+            init_cursor_batch = np.expand_dims(init_cursor_batch, axis=1).astype(np.float32)  # (N, 1, 2)
+        else:
+            init_cursor_batch = np.stack(init_cursor_batch_list, axis=1)  # (N, init_cursor_num, 2)
+            init_cursor_batch = np.expand_dims(init_cursor_batch, axis=2).astype(
+                np.float32)  # (N, init_cursor_num, 1, 2)
+
+        return init_cursor_batch
+
+
+def load_dataset_multi_object_rough(dataset_base_dir, model_params):
+    train_photo_data = []
+    train_sketch_data = []
+    val_photo_data = []
+    val_sketch_data = []
+    texture_data = []
+    shadow_data = []
+
+    if model_params.data_set == 'rough_sketches':
+        base_dir_rough = 'QuickDraw-rough'
+
+        def load_sketch_data(mat_path):
+            sketch_data_mat = scipy.io.loadmat(mat_path)
+            sketch_data = sketch_data_mat['sketch_array']
+            sketch_data = np.array(sketch_data, dtype=np.uint8)  # (N, resolution, resolution), [0-strokes, 255-BG]
+            return sketch_data
+
+        def load_photo_data(mat_path):
+            photo_data_mat = scipy.io.loadmat(mat_path)
+            photo_data = photo_data_mat['image_array']
+            photo_data = np.array(photo_data, dtype=np.uint8)  # (N, resolution, resolution), [0-strokes, 255-BG]
+            return photo_data
+
+        def load_normal_data(img_path):
+            assert '.png' in img_path or '.jpg'
+            img = Image.open(img_path).convert('RGB')
+            img = np.array(img, dtype=np.uint8)  # (H, W, 3), [0-stroke, 255-BG], uint8
+            return img
+
+        ## Texture
+        texture_base = os.path.join(dataset_base_dir, base_dir_rough, 'texture')
+        all_texture = os.listdir(texture_base)
+        all_texture.sort()
+
+        for file_name in all_texture:
+            texture_path = os.path.join(texture_base, file_name)
+            texture_uint8 = load_normal_data(texture_path)
+            texture_data.append(texture_uint8)
+
+        ## shadow
+        def process_angle(img, temp_size):
+            padded_img = img.copy()
+            padded_img[0, 0:temp_size] -= 1
+            padded_img[0, -(temp_size + 1):-1] -= 1
+            padded_img[-1, 0:temp_size] -= 1
+            padded_img[-1, -(temp_size + 1):-1] -= 1
+
+            padded_img[0:temp_size, 0] -= 1
+            padded_img[0:temp_size, -1] -= 1
+            padded_img[-(temp_size + 1):-1, 0] -= 1
+            padded_img[-(temp_size + 1):-1, -1] -= 1
+            return padded_img
+
+        def pad_img(ori_img, pad_value):
+            padded_img = np.pad(ori_img, 1, constant_values=pad_value)
+            img_h, img_w = padded_img.shape[0], padded_img.shape[1]
+
+            temp_size = img_h // 3
+            padded_img = process_angle(padded_img, temp_size)
+
+            temp_size = img_h // 9
+            padded_img = process_angle(padded_img, temp_size)
+
+            temp_size = img_h // 15
+            padded_img = process_angle(padded_img, temp_size)
+
+            temp_size = img_h // 21
+            padded_img = process_angle(padded_img, temp_size)
+
+            padded_img = np.clip(padded_img, 0, 255)
+
+            return padded_img
+
+        def shadow_generation(transparency, shadow_img_size=1024):
+            deepest_value = int(255 * transparency)
+
+            center_patch = np.zeros((shadow_img_size // 2, shadow_img_size // 2), dtype=np.uint8)
+            center_patch.fill(255)
+
+            pad_gap = shadow_img_size // 2
+            shadow_patch = center_patch.copy()
+            for i in range(pad_gap):
+                curr_pad_value = 255.0 - float(255.0 - deepest_value) / float(pad_gap) * (i + 1)
+                shadow_patch = pad_img(shadow_patch, pad_value=curr_pad_value)
+
+            for i in range(shadow_img_size // 4):
+                shadow_patch = pad_img(shadow_patch, pad_value=deepest_value)
+
+            assert shadow_patch.shape[0] == shadow_img_size * 2, shadow_patch.shape[0]
+            return shadow_patch
+
+        for transparency_ in range(90, 95 + 1):
+            transparency = transparency_ / 100.0
+            shadow_full = shadow_generation(transparency)
+            shadow_data.append(shadow_full)
+
+        splits = ['train', 'test']
+
+        resolutions = [model_params.image_size_small, model_params.image_size_large]
+
+        for resolution in range(resolutions[0], resolutions[1] + 1):
+            for split in splits:
+                sketch_mat1_path = os.path.join(dataset_base_dir, base_dir_rough, 'model_pencil1',
+                                                'sketch', split, 'res_' + str(resolution) + '.mat')
+                photo_mat1_path = os.path.join(dataset_base_dir, base_dir_rough, 'model_pencil1',
+                                               'photo', split, 'res_' + str(resolution) + '.mat')
+                sketch_data1_uint8 = load_sketch_data(
+                    sketch_mat1_path)  # (N, resolution, resolution), [0-strokes, 255-BG]
+                photo_data1_uint8 = load_photo_data(photo_mat1_path)  # (N, resolution, resolution), [0-strokes, 255-BG]
+
+                sketch_mat2_path = os.path.join(dataset_base_dir, base_dir_rough, 'model_pencil2',
+                                                'sketch', split, 'res_' + str(resolution) + '.mat')
+                photo_mat2_path = os.path.join(dataset_base_dir, base_dir_rough, 'model_pencil2',
+                                               'photo', split, 'res_' + str(resolution) + '.mat')
+                sketch_data2_uint8 = load_sketch_data(
+                    sketch_mat2_path)  # (N, resolution, resolution), [0-strokes, 255-BG]
+                photo_data2_uint8 = load_photo_data(photo_mat2_path)  # (N, resolution, resolution), [0-strokes, 255-BG]
+
+                sketch_data_uint8 = np.concatenate([sketch_data1_uint8, sketch_data2_uint8],
+                                                   axis=0)  # (N, resolution, resolution), [0-strokes, 255-BG]
+                photo_data_uint8 = np.concatenate([photo_data1_uint8, photo_data2_uint8],
+                                                  axis=0)  # (N, resolution, resolution), [0-strokes, 255-BG]
+
+                if split == 'train':
+                    train_photo_data.append(photo_data_uint8)
+                    train_sketch_data.append(sketch_data_uint8)
+                else:
+                    val_photo_data.append(photo_data_uint8)
+                    val_sketch_data.append(sketch_data_uint8)
+
+        assert len(train_sketch_data) == len(train_photo_data)
+        assert len(val_sketch_data) == len(val_photo_data)
+    else:
+        raise Exception('Unknown data type:', model_params.data_set)
+
+    print('Loaded {}/{} from {}'.format(len(train_sketch_data), len(val_sketch_data), model_params.data_set))
+    print('model_params.max_seq_len %i.' % model_params.max_seq_len)
+
+    eval_sample_model_params = copy_hparams(model_params)
+    eval_sample_model_params.use_input_dropout = 0
+    eval_sample_model_params.use_recurrent_dropout = 0
+    eval_sample_model_params.use_output_dropout = 0
+    eval_sample_model_params.batch_size = 1  # only sample one at a time
+    eval_sample_model_params.model_mode = 'eval_sample'
+
+    train_set = GeneralDataLoaderMultiObjectRough(train_photo_data, train_sketch_data,
+                                                  texture_data, shadow_data,
+                                                  model_params.batch_size, model_params.raster_size,
+                                                  model_params.image_size_small, model_params.image_size_large,
+                                                  is_train=True)
+    val_set = GeneralDataLoaderMultiObjectRough(val_photo_data, val_sketch_data,
+                                                texture_data, shadow_data,
+                                                eval_sample_model_params.batch_size,
+                                                eval_sample_model_params.raster_size,
+                                                eval_sample_model_params.image_size_small,
+                                                eval_sample_model_params.image_size_large,
+                                                is_train=False)
+
+    result = [
+        train_set, val_set, model_params, eval_sample_model_params
+    ]
+    return result
+
+
+class GeneralDataLoaderNormalImageLinear(object):
+    def __init__(self,
+                 photo_data,
+                 sketch_data,
+                 sketch_shape,
+                 batch_size,
+                 raster_size,
+                 image_size_small,
+                 image_size_large,
+                 random_image_size,
+                 flip_prob,
+                 rotate_prob,
+                 is_train):
+        self.batch_size = batch_size  # minibatch size
+        self.raster_size = raster_size
+        self.image_size_small = image_size_small
+        self.image_size_large = image_size_large
+        self.random_image_size = random_image_size
+        self.is_train = is_train
+
+        assert photo_data is not None
+        assert len(photo_data) == len(sketch_data)
+        self.num_batches = len(sketch_data) // self.batch_size
+        self.batch_idx = -1
+        print('batch_size', batch_size, ', num_batches', self.num_batches)
+
+        self.flip_prob = flip_prob
+        self.rotate_prob = rotate_prob
+
+        assert type(photo_data) is list
+        assert type(sketch_data) is list
+        self.photo_data = photo_data
+        self.sketch_data = sketch_data
+        self.sketch_shape = sketch_shape
+
+    def get_batch_from_memory(self, memory_idx, interpolate_type, fixed_image_size=-1, random_cursor=True,
+                              photo_prob=1.0,
+                              init_cursor_num=1):
+        if self.random_image_size:
+            image_size_rand = fixed_image_size
+        else:
+            image_size_rand = self.image_size_large
+
+        photo_data_batch, sketch_data_batch = self.select_sketch_and_crop(
+            image_size_rand, data_idx=memory_idx, photo_prob=photo_prob,
+            interpolate_type=interpolate_type)  # sketch_patch: [0.0-stroke, 1.0-BG]
+
+        photo_data_batch = np.expand_dims(photo_data_batch, axis=0)  # (1, image_size, image_size, 3)
+        sketch_data_batch = np.expand_dims(sketch_data_batch,
+                                           axis=0)  # (1, image_size, image_size), [0.0-strokes, 1.0-BG]
+        image_size_rand = sketch_data_batch.shape[1]
+
+        return photo_data_batch, sketch_data_batch, \
+               self.gen_init_cursors(sketch_data_batch, random_cursor, init_cursor_num), image_size_rand
+
+    def crop_and_augment(self, photo, sketch, shape, crop_size, rotate_angle, stroke_cover=0.01):
+        # img: [0-stroke, 255-BG], uint8
+
+        def angle_convert(angle):
+            return angle / 180.0 * math.pi
+
+        img_h, img_w = shape[0], shape[1]
+
+        if self.is_train:
+            crop_up = random.randint(0, img_h - crop_size)
+            crop_left = random.randint(0, img_w - crop_size)
+        else:
+            crop_up = (img_h - crop_size) // 2
+            crop_left = (img_w - crop_size) // 2
+
+        assert crop_up >= 0
+        assert crop_left >= 0
+
+        crop_box = (crop_left, crop_up, crop_left + crop_size, crop_up + crop_size)
+        rst_sketch_image = sketch.crop(crop_box)
+        rst_photo_image = photo.crop(crop_box)
+
+        if random.random() <= self.flip_prob and self.is_train:
+            rst_sketch_image = rst_sketch_image.transpose(Image.FLIP_LEFT_RIGHT)
+            rst_photo_image = rst_photo_image.transpose(Image.FLIP_LEFT_RIGHT)
+
+        if rotate_angle != 0 and self.is_train:
+            rst_sketch_image = rst_sketch_image.rotate(rotate_angle, resample=Image.BILINEAR)
+            rst_photo_image = rst_photo_image.rotate(rotate_angle, resample=Image.BILINEAR)
+            rst_sketch_image = np.array(rst_sketch_image, dtype=np.uint8)
+            rst_photo_image = np.array(rst_photo_image, dtype=np.uint8)
+
+            center = rst_photo_image.shape[0] // 2
+
+            new_dim = float(crop_size) / (
+                        math.sin(angle_convert(abs(rotate_angle))) + math.cos(angle_convert(abs(rotate_angle))))
+            new_dim = int(round(new_dim))
+
+            start_pos = center - new_dim // 2
+            end_pos = start_pos + new_dim
+            rst_sketch_image = rst_sketch_image[start_pos:end_pos, start_pos:end_pos, :]
+            rst_photo_image = rst_photo_image[start_pos:end_pos, start_pos:end_pos, :]
+
+        rst_sketch_image = np.array(rst_sketch_image, dtype=np.float32) / 255.0  # [0.0-stroke, 1.0-BG]
+        rst_sketch_image = rst_sketch_image[:, :, 0]
+        rst_photo_image = np.array(rst_photo_image, dtype=np.float32) / 255.0  # [0.0-stroke, 1.0-BG]
+
+        percentage = np.mean(1.0 - rst_sketch_image)
+        valid = True
+        if percentage < stroke_cover:
+            valid = False
+
+        return rst_photo_image, rst_sketch_image, valid
+
+    def image_interpolation(self, photo, sketch, photo_prob):
+        interp_photo = photo * photo_prob + sketch * (1.0 - photo_prob)
+        interp_photo = np.clip(interp_photo, 0.0, 1.0)
+        return interp_photo
+
+    def select_sketch_and_crop(self, image_size_rand, interpolate_type, rotate_angle=0, photo_prob=1.0,
+                               data_idx=-1, trial_times=10):
+        if self.is_train:
+            while True:
+                rand_img_idx = random.randint(0, len(self.sketch_data) - 1)
+                selected_sketch_shape = self.sketch_shape[rand_img_idx]
+                if selected_sketch_shape[0] >= image_size_rand and selected_sketch_shape[1] >= image_size_rand:
+                    img_idx = rand_img_idx
+                    break
+        else:
+            assert data_idx != -1
+            img_idx = data_idx
+
+        assert img_idx != -1
+        selected_sketch = self.sketch_data[img_idx]
+        selected_photo = self.photo_data[img_idx]
+        selected_shape = self.sketch_shape[img_idx]
+
+        assert interpolate_type in ['prob', 'image']
+
+        if interpolate_type == 'prob' and random.random() >= photo_prob:
+            selected_photo = self.sketch_data[img_idx]
+
+        for trial_i in range(trial_times):
+            cropped_photo, cropped_sketch, valid = \
+                self.crop_and_augment(selected_photo, selected_sketch, selected_shape, image_size_rand, rotate_angle)
+            # cropped_photo, cropped_sketch: [0.0-stroke, 1.0-BG]
+
+            if valid or trial_i == trial_times - 1:
+                if interpolate_type == 'image':
+                    cropped_photo = self.image_interpolation(cropped_photo,
+                                                             np.stack([cropped_sketch for _ in range(3)], axis=-1),
+                                                             photo_prob)
+
+                return cropped_photo, cropped_sketch
+
+    def get_batch_multi_res(self, loop_num, interpolate_type, random_cursor=True, init_cursor_num=1, photo_prob=1.0):
+        photo_data_batch = []
+        sketch_data_batch = []
+        init_cursors_batch = []
+        image_size_batch = []
+        batch_size_per_loop = self.batch_size // loop_num
+        for loop_i in range(loop_num):
+            if self.random_image_size:
+                image_size_rand = random.randint(self.image_size_small, self.image_size_large)
+            else:
+                image_size_rand = self.image_size_large
+
+            rotate_angle = 0
+            if random.random() <= self.rotate_prob:
+                rotate_angle = random.randint(-45, 45)
+
+            photo_data_sub_batch = []
+            sketch_data_sub_batch = []
+            for img_i in range(batch_size_per_loop):
+                photo_patch, sketch_patch = \
+                    self.select_sketch_and_crop(image_size_rand, rotate_angle=rotate_angle, photo_prob=photo_prob,
+                                                interpolate_type=interpolate_type)  # sketch_patch: [0.0-stroke, 1.0-BG]
+                photo_data_sub_batch.append(photo_patch)
+                sketch_data_sub_batch.append(sketch_patch)
+
+            photo_data_sub_batch = np.stack(photo_data_sub_batch,
+                                            axis=0)  # (N, image_size, image_size, 3), [0.0-strokes, 1.0-BG]
+            sketch_data_sub_batch = np.stack(sketch_data_sub_batch,
+                                             axis=0)  # (N, image_size, image_size), [0.0-strokes, 1.0-BG]
+            init_cursors_sub_batch = self.gen_init_cursors(sketch_data_sub_batch, random_cursor, init_cursor_num)
+
+            photo_data_batch.append(photo_data_sub_batch)
+            sketch_data_batch.append(sketch_data_sub_batch)
+            init_cursors_batch.append(init_cursors_sub_batch)
+
+            image_size_rand = photo_data_sub_batch.shape[1]
+            image_size_batch.append(image_size_rand)
+
+        return photo_data_batch, sketch_data_batch, init_cursors_batch, image_size_batch
+
+    def crop_patch(self, image, center, image_size, crop_size):
+        x0 = center[0] - crop_size // 2
+        x1 = x0 + crop_size
+        y0 = center[1] - crop_size // 2
+        y1 = y0 + crop_size
+        x0 = max(0, min(x0, image_size))
+        y0 = max(0, min(y0, image_size))
+        x1 = max(0, min(x1, image_size))
+        y1 = max(0, min(y1, image_size))
+        patch = image[y0:y1, x0:x1]
+        return patch
+
+    def gen_init_cursor_single(self, sketch_image):
+        # sketch_image: [0.0-stroke, 1.0-BG]
+        image_size = sketch_image.shape[0]
+        if np.sum(1.0 - sketch_image) == 0:
+            center = np.zeros((2), dtype=np.int32)
+            return center
+        else:
+            while True:
+                center = np.random.randint(0, image_size, size=(2))  # (2), in large size
+                patch = 1.0 - self.crop_patch(sketch_image, center, image_size, self.raster_size)
+                if np.sum(patch) != 0:
+                    return center.astype(np.float32) / float(image_size)  # (2), in size [0.0, 1.0)
+
+    def gen_init_cursors(self, sketch_data, random_pos=True, init_cursor_num=1):
+        init_cursor_batch_list = []
+        for cursor_i in range(init_cursor_num):
+            if random_pos:
+                init_cursor_batch = []
+                for i in range(len(sketch_data)):
+                    sketch_image = sketch_data[i].copy().astype(np.float32)  # [0.0-stroke, 1.0-BG]
+                    center = self.gen_init_cursor_single(sketch_image)
+                    init_cursor_batch.append(center)
+
+                init_cursor_batch = np.stack(init_cursor_batch, axis=0)  # (N, 2)
+            else:
+                raise Exception('Not finished')
+            init_cursor_batch_list.append(init_cursor_batch)
+
+        if init_cursor_num == 1:
+            init_cursor_batch = init_cursor_batch_list[0]
+            init_cursor_batch = np.expand_dims(init_cursor_batch, axis=1).astype(np.float32)  # (N, 1, 2)
+        else:
+            init_cursor_batch = np.stack(init_cursor_batch_list, axis=1)  # (N, init_cursor_num, 2)
+            init_cursor_batch = np.expand_dims(init_cursor_batch, axis=2).astype(
+                np.float32)  # (N, init_cursor_num, 1, 2)
+
+        return init_cursor_batch
+
+
+def load_dataset_normal_images(dataset_base_dir, model_params):
+    train_photo_data = []
+    train_sketch_data = []
+    train_data_shape = []
+    val_photo_data = []
+    val_sketch_data = []
+    val_data_shape = []
+
+    if model_params.data_set == 'faces':
+        random_training_image_size = False
+        flip_prob = -0.1
+        rotate_prob = -0.1
+
+        splits = ['train', 'val']
+
+        database = os.path.join(dataset_base_dir, 'CelebAMask-faces')
+        photo_base = os.path.join(database, 'CelebA-HQ-img256')
+        edge_base = os.path.join(database, 'CelebAMask-HQ-edge256')
+
+        train_split_txt_save_path = os.path.join(database, 'train.txt')
+        val_split_txt_save_path = os.path.join(database, 'val.txt')
+        celeba_train_txt = np.loadtxt(train_split_txt_save_path, dtype=str)
+        celeba_val_txt = np.loadtxt(val_split_txt_save_path, dtype=str)
+        splits_indices_map = {'train': celeba_train_txt, 'val': celeba_val_txt}
+
+        for split in splits:
+            split_indices = splits_indices_map[split]
+
+            for i in range(len(split_indices)):
+                file_idx = split_indices[i]
+                img_file_path = os.path.join(photo_base, str(file_idx) + '.jpg')
+                edge_img_path = os.path.join(edge_base, str(file_idx) + '.png')
+
+                img_data = Image.open(img_file_path).convert('RGB')
+                edge_data = Image.open(edge_img_path).convert('RGB')
+
+                if split == 'train':
+                    train_photo_data.append(img_data)
+                    train_sketch_data.append(edge_data)
+                    train_data_shape.append((img_data.height, img_data.width))
+                else:  # split == 'val'
+                    val_photo_data.append(img_data)
+                    val_sketch_data.append(edge_data)
+                    val_data_shape.append((img_data.height, img_data.width))
+
+        assert len(train_sketch_data) == len(train_data_shape) == len(train_photo_data)
+        assert len(val_sketch_data) == len(val_data_shape) == len(val_photo_data)
+    else:
+        raise Exception('Unknown data type:', model_params.data_set)
+
+    print('Loaded {}/{} from {}'.format(len(train_sketch_data), len(val_sketch_data), model_params.data_set))
+    print('model_params.max_seq_len %i.' % model_params.max_seq_len)
+
+    eval_sample_model_params = copy_hparams(model_params)
+    eval_sample_model_params.use_input_dropout = 0
+    eval_sample_model_params.use_recurrent_dropout = 0
+    eval_sample_model_params.use_output_dropout = 0
+    eval_sample_model_params.batch_size = 1  # only sample one at a time
+    eval_sample_model_params.model_mode = 'eval_sample'
+
+    train_set = GeneralDataLoaderNormalImageLinear(train_photo_data, train_sketch_data, train_data_shape,
+                                                   model_params.batch_size, model_params.raster_size,
+                                                   image_size_small=model_params.image_size_small,
+                                                   image_size_large=model_params.image_size_large,
+                                                   random_image_size=random_training_image_size,
+                                                   flip_prob=flip_prob, rotate_prob=rotate_prob,
+                                                   is_train=True)
+    val_set = GeneralDataLoaderNormalImageLinear(val_photo_data, val_sketch_data, val_data_shape,
+                                                 eval_sample_model_params.batch_size,
+                                                 eval_sample_model_params.raster_size,
+                                                 image_size_small=eval_sample_model_params.image_size_small,
+                                                 image_size_large=eval_sample_model_params.image_size_large,
+                                                 random_image_size=random_training_image_size,
+                                                 flip_prob=flip_prob, rotate_prob=rotate_prob,
+                                                 is_train=False)
+
+    result = [
+        train_set, val_set, model_params, eval_sample_model_params
+    ]
+    return result
+
+
+def load_dataset_training(dataset_base_dir, model_params):
+    if model_params.data_set == 'clean_line_drawings':
+        return load_dataset_multi_object(dataset_base_dir, model_params)
+    elif model_params.data_set == 'rough_sketches':
+        return load_dataset_multi_object_rough(dataset_base_dir, model_params)
+    elif model_params.data_set == 'faces':
+        return load_dataset_normal_images(dataset_base_dir, model_params)
+    else:
+        raise Exception('Unknown data_set', model_params.data_set)

docs/assets/font.css

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+}
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+/* Section */
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+  padding: 20pt 30pt;
+  border: 1pt hidden #000;
+  text-align: justify;
+  color: #000000;
+  background: #ffffff;
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+
+/* Header (Title and Logo) */
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+  float: left;
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+  text-align: center;
+  font-size: 22pt;
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+/* Teaser */
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+  margin: 20pt 0;
+  text-align: left;
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+
+/* Related Work */
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docs/index.html

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+<!doctype html>
+<html lang="en">
+
+
+<!-- === Header Starts === -->
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+
+  <title>General Virtual Sketching Framework for Vector Line Art</title>
+
+  <link href="./assets/bootstrap.min.css" rel="stylesheet">
+  <link href="./assets/font.css" rel="stylesheet" type="text/css">
+  <link href="./assets/style.css" rel="stylesheet" type="text/css">
+</head>
+<!-- === Header Ends === -->
+
+
+<body>
+
+
+<!-- === Home Section Starts === -->
+<div class="section">
+  <!-- === Title Starts === -->
+    <div class="title">
+      <b>General Virtual Sketching Framework for Vector Line Art</b>
+    </div>
+  <!-- === Title Ends === -->
+  <div class="author">
+    <a href="http://mo-haoran.com/" target="_blank">Haoran Mo</a><sup>1</sup>,&nbsp;
+    <a href="https://esslab.jp/~ess/en/" target="_blank">Edgar Simo-Serra</a><sup>2</sup>,&nbsp;
+    <a href="http://cse.sysu.edu.cn/content/2537" target="_blank">Chengying Gao</a><sup>*1</sup>,&nbsp;
+    <a href="https://changqingzou.weebly.com/" target="_blank">Changqing Zou</a><sup>3</sup>,&nbsp;
+    <a href="http://cse.sysu.edu.cn/content/2523" target="_blank">Ruomei Wang</a><sup>1</sup>
+  </div>
+  <div class="institution">
+    <sup>1</sup>Sun Yat-sen University,&nbsp;
+    <sup>2</sup>Waseda University,&nbsp;
+    <br>
+    <sup>3</sup>Huawei Technologies Canada
+  </div>
+  <br>
+  <div class="institution">
+    Accepted by <a href="https://s2021.siggraph.org/" target="_blank">ACM SIGGRAPH 2021</a>
+  </div>
+  <div class="link">
+    <a href="https://esslab.jp/publications/HaoranSIGRAPH2021.pdf" target="_blank">[Paper]</a>&nbsp;
+    <a href="https://github.com/MarkMoHR/virtual_sketching" target="_blank">[Code]</a>
+  </div>
+  <div class="teaser">
+    <img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/teaser6.png" style="width: 100%;">
+    <br>
+    <br>
+    <font size="3">
+      Given clean line drawings, rough sketches or photographs of arbitrary resolution as input, our framework generates the corresponding vector line drawings directly. As shown in (b), the framework models a virtual pen surrounded by a dynamic window (red boxes), which moves while drawing the strokes. It learns to move around by scaling the window and sliding to an undrawn area for restarting the drawing (bottom example; sliding trajectory in blue arrow). With our proposed stroke regularization mechanism, the framework is able to enlarge the window and draw long strokes for simplicity (top example).
+    </font>
+  </div>
+</div>
+<!-- === Home Section Ends === -->
+
+
+<!-- === Overview Section Starts === -->
+<div class="section">
+  <div class="title">Abstract</div>
+  <div class="body">
+    Vector line art plays an important role in graphic design, however, it is tedious to manually create.
+    We introduce a general framework to produce line drawings from a wide variety of images,
+    by learning a mapping from raster image space to vector image space.
+    Our approach is based on a recurrent neural network that draws the lines one by one.
+    A differentiable rasterization module allows for training with only supervised raster data.
+    We use a dynamic window around a virtual pen while drawing lines,
+    implemented with a proposed aligned cropping and differentiable pasting modules.
+    Furthermore, we develop a stroke regularization loss
+    that encourages the model to use fewer and longer strokes to simplify the resulting vector image.
+    Ablation studies and comparisons with existing methods corroborate the efficiency of our approach
+    which is able to generate visually better results in less computation time,
+    while generalizing better to a diversity of images and applications.
+  </div>
+  <div class="link">
+    <a href="https://esslab.jp/publications/HaoranSIGRAPH2021.pdf" target="_blank">[Paper]</a>&nbsp; &nbsp;
+    <a href="https://dl.acm.org/doi/abs/10.1145/3450626.3459833" target="_blank">[Paper (ACM)]</a>&nbsp; &nbsp;
+    <a href="https://markmohr.github.io/files/SIG2021/SketchVectorization_SIG2021_supplemental.pdf" target="_blank">[Supplementary]</a>&nbsp; &nbsp;
+	  <a href="https://github.com/MarkMoHR/virtual_sketching" target="_blank">[Code]</a>&nbsp; &nbsp;
+    <a href="https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing" target="_blank">[All Precomputed Results]</a>
+	  <!-- <a href="" target="_blank">[Presentation (TBD)]</a>&nbsp; &nbsp; -->
+  </div>
+</div>
+<!-- === Overview Section Ends === -->
+
+
+<!-- === Result Section Starts === -->
+<div class="section">
+  <div class="title">Method</div>
+  <br>
+  <div class="body">
+    <p style="text-align:center; font-size:23px; font-weight:bold">Framework Overview<p>
+    <img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/framework6.png" width="100%">
+    <br>
+    <br>
+    <font size="4">
+      Our framework generates the parametrized strokes step by step in a recurrent manner.
+      It uses a dynamic window (dashed red boxes) around a virtual pen to draw the strokes,
+      and can both move and change the size of the window.
+      (a) Four main modules at each time step: aligned cropping, stroke generation, differentiable rendering and differentiable pasting.
+      (b) Architecture of the stroke generation module.
+      (c) Structural strokes predicted at each step;
+      movement only is illustrated by blue arrows during which no stroke is drawn on the canvas.
+    </font>
+    <br>
+    <br>
+
+    <p style="text-align:center; font-size:23px; font-weight:bold">
+      Overall Introduction
+    <p>
+    <p style="text-align:center; font-size:20px">
+      (Or watch on <a href="https://www.bilibili.com/video/BV1gM4y1V7i7/" target="_blank">Bilibili</a>)
+      <br>
+      👇
+    <p>
+    <!-- Adjust the frame size based on the demo (EVERY project differs). -->
+    <div style="position: relative; padding-top: 50%; text-align: center;">
+      <iframe src="https://www.youtube.com/embed/gXk3TMceByY" frameborder=0
+              style="position: absolute; top: 1%; left: 5%; width: 90%; height: 100%;"
+              allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
+              allowfullscreen></iframe>
+    </div>
+
+  </div>
+</div>
+<!-- === Result Section Ends === -->
+
+<!-- === Result Section Starts === -->
+<div class="section">
+  <div class="title">Results</div>
+  <div class="body">
+    Our framework is applicable to a diversity of image types, such as clean line drawing images, rough sketches and photographs.
+
+    <p style="margin-top: 10pt; text-align:center; font-size:23px; font-weight:bold">Vectorization<p>
+    <table width="100%" style="margin: 0pt auto; text-align: center; border-collapse: separate; border-spacing: 5pt;">
+      <tr>
+        <td width="45%"><img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/gifs/clean/muten.png" width="100%"></td>
+        <td width="10%"></td>
+        <td width="45%"><img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/gifs/clean/muten-black-full-simplest.gif" width="100%"></td>
+      </tr>
+    </table>
+    <br>
+
+    <p style="margin-top: 10pt; text-align:center; font-size:23px; font-weight:bold">Rough sketch simplification<p>
+    <table width="100%" style="margin: 0pt auto; text-align: center; border-collapse: separate; border-spacing: 5pt;">
+      <tr>
+        <td width="26%"><img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/gifs/rough/rocket.png" width="100%"></td>
+        <td width="26%"><img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/gifs/rough/rocket-blue-simplest.gif" width="100%"></td>
+        <td width="4%"></td>
+        <td width="14%"><img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/gifs/rough/penguin.png" width="100%"></td>
+        <td width="14%"><img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/gifs/rough/penguin-blue-simplest.gif" width="100%"></td>
+      </tr>
+    </table>
+    <br>
+
+    <p style="margin-top: 10pt; text-align:center; font-size:23px; font-weight:bold">Photograph to line drawing<p>
+    <table width="100%" style="margin: 0pt auto; text-align: center; border-collapse: separate; border-spacing: 5pt;">
+      <tr>
+        <td width="23%"><img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/gifs/face/1390_input.png" width="100%"></td>
+        <td width="23%"><img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/gifs/face/face-blue-1390-simplest.gif" width="100%"></td>
+        <td width="8%"></td>
+        <td width="23%"><img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/gifs/face/1190_input.png" width="100%"></td>
+        <td width="23%"><img src="https://cdn.jsdelivr.net/gh/mark-cdn/CDN-for-works@1.4/files/SIG21/gifs/face/face-blue-1190-simplest.gif" width="100%"></td>
+      </tr>
+    </table>
+    <br>
+
+    <p style="margin-top: 10pt; text-align:center; font-size:23px; font-weight:bold">
+      More Results
+    <p>
+    <p style="text-align:center; font-size:20px">
+      (Or watch on <a href="https://www.bilibili.com/video/BV1pv411N7Yx/" target="_blank">Bilibili</a>)
+      <br>
+      👇
+    <p>
+    <!-- Adjust the frame size based on the demo (EVERY project differs). -->
+    <div style="position: relative; padding-top: 50%; text-align: center;">
+      <iframe src="https://www.youtube.com/embed/Pr6mK9ddXkQ" frameborder=0
+              style="position: absolute; top: 1%; left: 5%; width: 90%; height: 100%;"
+              allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
+              allowfullscreen></iframe>
+    </div>
+    <br>
+
+    <div class="link">
+      <a href="https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing" target="_blank">
+      [Download Our Precomputed Output Results (7MB)]</a>
+    </div>
+
+  </div>
+</div>
+<!-- === Result Section Ends === -->
+
+
+<!-- === Result Section Starts === -->
+<div class="section">
+  <div class="title">Presentations</div>
+  <div class="body">
+
+    <p style="margin-top: 10pt; text-align:center; font-size:23px; font-weight:bold">
+      3-5 minute presentation
+    <p>
+    <p style="text-align:center; font-size:20px">
+      (Or watch on <a href="https://www.bilibili.com/video/BV1S3411q7VX/" target="_blank">Bilibili</a>)
+      <br>
+      👇
+    <p>
+    <!-- Adjust the frame size based on the demo (EVERY project differs). -->
+    <div style="position: relative; padding-top: 50%; text-align: center;">
+      <iframe src="https://www.youtube.com/embed/BSJN1ixacts" frameborder=0
+              style="position: absolute; top: 1%; left: 5%; width: 90%; height: 100%;"
+              allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
+              allowfullscreen></iframe>
+    </div>
+    <br>
+
+    <div class="link">
+      👉 15-20 minute presentation:
+      <a href="https://youtu.be/D_U4e1qh5qc" target="_blank">[YouTube]</a>
+      <a href="https://www.bilibili.com/video/BV1uU4y1E7Wg/" target="_blank">[Bilibili]</a>
+    </div>
+
+    <div class="link">
+      👉 30-second fast forward:
+      <a href="https://youtu.be/d0EbSU_EeFg" target="_blank">[YouTube]</a>
+      <a href="https://www.bilibili.com/video/BV1vq4y1M7j1/" target="_blank">[Bilibili]</a>
+    </div>
+
+  </div>
+</div>
+<!-- === Result Section Ends === -->
+
+
+<!-- === Reference Section Starts === -->
+<div class="section">
+  <div class="bibtex">BibTeX</div>
+<pre>
+@article{mo2021virtualsketching,
+    title   = {General Virtual Sketching Framework for Vector Line Art},
+    author  = {Mo, Haoran and Simo-Serra, Edgar and Gao, Chengying and Zou, Changqing and Wang, Ruomei},
+    journal = {ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH 2021)},
+    year    = {2021},
+    volume  = {40},
+    number  = {4},
+    pages   = {51:1--51:14}
+}
+</pre>
+
+  <br>
+  <div class="bibtex">Related Work</div>
+  <div class="citation">
+    <div class="comment">
+      Jean-Dominique Favreau, Florent Lafarge and Adrien Bousseau.
+      <strong>Fidelity vs. Simplicity: a Global Approach to Line Drawing Vectorization</strong>. SIGGRAPH 2016.
+      [<a href="https://www-sop.inria.fr/reves/Basilic/2016/FLB16/fidelity_simplicity.pdf">Paper</a>]
+      [<a href="https://www-sop.inria.fr/reves/Basilic/2016/FLB16/">Webpage</a>]
+      <br><br>
+    </div>
+
+    <div class="comment">
+      Mikhail Bessmeltsev and Justin Solomon. 
+      <strong>Vectorization of Line Drawings via PolyVector Fields</strong>. SIGGRAPH 2019. 
+      [<a href="https://arxiv.org/abs/1801.01922">Paper</a>]
+      [<a href="https://github.com/bmpix/PolyVectorization">Code</a>]
+      <br><br>
+    </div>
+
+    <div class="comment">
+      Edgar Simo-Serra, Satoshi Iizuka and Hiroshi Ishikawa. 
+      <strong>Mastering Sketching: Adversarial Augmentation for Structured Prediction</strong>. SIGGRAPH 2018. 
+      [<a href="https://esslab.jp/~ess/publications/SimoSerraTOG2018.pdf">Paper</a>]
+      [<a href="https://esslab.jp/~ess/en/research/sketch_master/">Webpage</a>]
+      [<a href="https://github.com/bobbens/sketch_simplification">Code</a>]
+      <br><br>
+    </div>
+
+    <div class="comment">
+      Zhewei Huang, Wen Heng and Shuchang Zhou. 
+      <strong>Learning to Paint With Model-based Deep Reinforcement Learning</strong>. ICCV 2019. 
+      [<a href="https://openaccess.thecvf.com/content_ICCV_2019/papers/Huang_Learning_to_Paint_With_Model-Based_Deep_Reinforcement_Learning_ICCV_2019_paper.pdf">Paper</a>]
+      [<a href="https://github.com/megvii-research/ICCV2019-LearningToPaint">Code</a>]
+      <br><br>
+    </div>
+  </div>
+</div>
+<!-- === Reference Section Ends === -->
+
+
+</body>
+</html>

hyper_parameters.py

@@ -0,0 +1,341 @@
+import tensorflow as tf
+
+
+#############################################
+# Common parameters
+#############################################
+
+FLAGS = tf.app.flags.FLAGS
+
+tf.app.flags.DEFINE_string(
+    'dataset_dir',
+    'datasets',
+    'The directory of sketch data of the dataset.')
+tf.app.flags.DEFINE_string(
+    'log_root',
+    'outputs/log',
+    'Directory to store tensorboard.')
+tf.app.flags.DEFINE_string(
+    'log_img_root',
+    'outputs/log_img',
+    'Directory to store intermediate output images.')
+tf.app.flags.DEFINE_string(
+    'snapshot_root',
+    'outputs/snapshot',
+    'Directory to store model checkpoints.')
+tf.app.flags.DEFINE_string(
+    'neural_renderer_path',
+    'outputs/snapshot/pretrain_neural_renderer/renderer_300000.tfmodel',
+    'Path to the neural renderer model.')
+tf.app.flags.DEFINE_string(
+    'perceptual_model_root',
+    'outputs/snapshot/pretrain_perceptual_model',
+    'Directory to store perceptual model.')
+tf.app.flags.DEFINE_string(
+    'data',
+    '',
+    'The dataset type.')
+
+
+def get_default_hparams_clean():
+    """Return default HParams for sketch-rnn."""
+    hparams = tf.contrib.training.HParams(
+        program_name='new_train_clean_line_drawings',
+        data_set='clean_line_drawings',  # Our dataset.
+
+        input_channel=1,
+
+        num_steps=75040,  # Total number of steps of training.
+        save_every=75000,
+        eval_every=5000,
+
+        max_seq_len=48,
+        batch_size=20,
+        gpus=[0, 1],
+        loop_per_gpu=1,
+
+        sn_loss_type='increasing',  # ['decreasing', 'fixed', 'increasing']
+        stroke_num_loss_weight=0.02,
+        stroke_num_loss_weight_end=0.0,
+        increase_start_steps=25000,
+        decrease_stop_steps=40000,
+
+        perc_loss_layers=['ReLU1_2', 'ReLU2_2', 'ReLU3_3', 'ReLU5_1'],
+        perc_loss_fuse_type='add',  # ['max', 'add', 'raw_add', 'weighted_sum']
+
+        init_cursor_on_undrawn_pixel=False,
+
+        early_pen_loss_type='move',  # ['head', 'tail', 'move']
+        early_pen_loss_weight=0.1,
+        early_pen_length=7,
+
+        min_width=0.01,
+        min_window_size=32,
+        max_scaling=2.0,
+
+        encode_cursor_type='value',
+
+        image_size_small=128,
+        image_size_large=278,
+
+        cropping_type='v3',  # ['v2', 'v3']
+        pasting_type='v3',  # ['v2', 'v3']
+        pasting_diff=True,
+
+        concat_win_size=True,
+
+        encoder_type='conv13_c3',
+        # ['conv10', 'conv10_deep', 'conv13', 'conv10_c3', 'conv10_deep_c3', 'conv13_c3']
+        # ['conv13_c3_attn']
+        # ['combine33', 'combine43', 'combine53', 'combineFC']
+        vary_thickness=False,
+
+        outside_loss_weight=10.0,
+        win_size_outside_loss_weight=10.0,
+
+        resize_method='AREA',  # ['BILINEAR', 'NEAREST_NEIGHBOR', 'BICUBIC', 'AREA']
+
+        concat_cursor=True,
+
+        use_softargmax=True,
+        soft_beta=10,  # value for the soft argmax
+
+        raster_loss_weight=1.0,
+
+        dec_rnn_size=256,  # Size of decoder.
+        dec_model='hyper',  # Decoder: lstm, layer_norm or hyper.
+        # z_size=128,  # Size of latent vector z. Recommend 32, 64 or 128.
+        bin_gt=True,
+
+        stop_accu_grad=True,
+
+        random_cursor=True,
+        cursor_type='next',
+
+        raster_size=128,
+
+        pix_drop_kp=1.0,  # Dropout keep rate
+        add_coordconv=True,
+        position_format='abs',
+        raster_loss_base_type='perceptual',  # [l1, mse, perceptual]
+
+        grad_clip=1.0,  # Gradient clipping. Recommend leaving at 1.0.
+
+        learning_rate=0.0001,  # Learning rate.
+        decay_rate=0.9999,  # Learning rate decay per minibatch.
+        decay_power=0.9,
+        min_learning_rate=0.000001,  # Minimum learning rate.
+
+        use_recurrent_dropout=True,  # Dropout with memory loss. Recommended
+        recurrent_dropout_prob=0.90,  # Probability of recurrent dropout keep.
+        use_input_dropout=False,  # Input dropout. Recommend leaving False.
+        input_dropout_prob=0.90,  # Probability of input dropout keep.
+        use_output_dropout=False,  # Output dropout. Recommend leaving False.
+        output_dropout_prob=0.90,  # Probability of output dropout keep.
+
+        model_mode='train'  # ['train', 'eval', 'sample']
+    )
+    return hparams
+
+
+def get_default_hparams_rough():
+    """Return default HParams for sketch-rnn."""
+    hparams = tf.contrib.training.HParams(
+        program_name='new_train_rough_sketches',
+        data_set='rough_sketches',  # ['rough_sketches', 'faces']
+
+        input_channel=3,
+
+        num_steps=90040,  # Total number of steps of training.
+        save_every=90000,
+        eval_every=5000,
+
+        max_seq_len=48,
+        batch_size=20,
+        gpus=[0, 1],
+        loop_per_gpu=1,
+
+        sn_loss_type='increasing',  # ['decreasing', 'fixed', 'increasing']
+        stroke_num_loss_weight=0.1,
+        stroke_num_loss_weight_end=0.0,
+        increase_start_steps=25000,
+        decrease_stop_steps=40000,
+
+        photo_prob_type='one',  # ['increasing', 'zero', 'one']
+        photo_prob_start_step=35000,
+
+        perc_loss_layers=['ReLU2_2', 'ReLU3_3', 'ReLU5_1'],
+        perc_loss_fuse_type='add',  # ['max', 'add', 'raw_add', 'weighted_sum']
+
+        early_pen_loss_type='move',  # ['head', 'tail', 'move']
+        early_pen_loss_weight=0.2,
+        early_pen_length=7,
+
+        min_width=0.01,
+        min_window_size=32,
+        max_scaling=2.0,
+
+        encode_cursor_type='value',
+
+        image_size_small=128,
+        image_size_large=278,
+
+        cropping_type='v3',  # ['v2', 'v3']
+        pasting_type='v3',  # ['v2', 'v3']
+        pasting_diff=True,
+
+        concat_win_size=True,
+
+        encoder_type='conv13_c3',
+        # ['conv10', 'conv10_deep', 'conv13', 'conv10_c3', 'conv10_deep_c3', 'conv13_c3']
+        # ['conv13_c3_attn']
+        # ['combine33', 'combine43', 'combine53', 'combineFC']
+
+        outside_loss_weight=10.0,
+        win_size_outside_loss_weight=10.0,
+
+        resize_method='AREA',  # ['BILINEAR', 'NEAREST_NEIGHBOR', 'BICUBIC', 'AREA']
+
+        concat_cursor=True,
+
+        use_softargmax=True,
+        soft_beta=10,  # value for the soft argmax
+
+        raster_loss_weight=1.0,
+
+        dec_rnn_size=256,  # Size of decoder.
+        dec_model='hyper',  # Decoder: lstm, layer_norm or hyper.
+        # z_size=128,  # Size of latent vector z. Recommend 32, 64 or 128.
+        bin_gt=True,
+
+        stop_accu_grad=True,
+
+        random_cursor=True,
+        cursor_type='next',
+
+        raster_size=128,
+
+        pix_drop_kp=1.0,  # Dropout keep rate
+        add_coordconv=True,
+        position_format='abs',
+        raster_loss_base_type='perceptual',  # [l1, mse, perceptual]
+
+        grad_clip=1.0,  # Gradient clipping. Recommend leaving at 1.0.
+
+        learning_rate=0.0001,  # Learning rate.
+        decay_rate=0.9999,  # Learning rate decay per minibatch.
+        decay_power=0.9,
+        min_learning_rate=0.000001,  # Minimum learning rate.
+
+        use_recurrent_dropout=True,  # Dropout with memory loss. Recommended
+        recurrent_dropout_prob=0.90,  # Probability of recurrent dropout keep.
+        use_input_dropout=False,  # Input dropout. Recommend leaving False.
+        input_dropout_prob=0.90,  # Probability of input dropout keep.
+        use_output_dropout=False,  # Output dropout. Recommend leaving False.
+        output_dropout_prob=0.90,  # Probability of output dropout keep.
+
+        model_mode='train'  # ['train', 'eval', 'sample']
+    )
+    return hparams
+
+
+def get_default_hparams_normal():
+    """Return default HParams for sketch-rnn."""
+    hparams = tf.contrib.training.HParams(
+        program_name='new_train_faces',
+        data_set='faces',  # ['rough_sketches', 'faces']
+
+        input_channel=3,
+
+        num_steps=90040,  # Total number of steps of training.
+        save_every=90000,
+        eval_every=5000,
+
+        max_seq_len=48,
+        batch_size=20,
+        gpus=[0, 1],
+        loop_per_gpu=1,
+
+        sn_loss_type='fixed',  # ['decreasing', 'fixed', 'increasing']
+        stroke_num_loss_weight=0.0,
+        stroke_num_loss_weight_end=0.0,
+        increase_start_steps=0,
+        decrease_stop_steps=40000,
+
+        photo_prob_type='interpolate',  # ['increasing', 'zero', 'one', 'interpolate']
+        photo_prob_start_step=30000,
+        photo_prob_end_step=60000,
+
+        perc_loss_layers=['ReLU2_2', 'ReLU3_3', 'ReLU4_2', 'ReLU5_1'],
+        perc_loss_fuse_type='add',  # ['max', 'add', 'raw_add', 'weighted_sum']
+
+        early_pen_loss_type='move',  # ['head', 'tail', 'move']
+        early_pen_loss_weight=0.2,
+        early_pen_length=7,
+
+        min_width=0.01,
+        min_window_size=32,
+        max_scaling=2.0,
+
+        encode_cursor_type='value',
+
+        image_size_small=128,
+        image_size_large=256,
+
+        cropping_type='v3',  # ['v2', 'v3']
+        pasting_type='v3',  # ['v2', 'v3']
+        pasting_diff=True,
+
+        concat_win_size=True,
+
+        encoder_type='conv13_c3',
+        # ['conv10', 'conv10_deep', 'conv13', 'conv10_c3', 'conv10_deep_c3', 'conv13_c3']
+        # ['conv13_c3_attn']
+        # ['combine33', 'combine43', 'combine53', 'combineFC']
+
+        outside_loss_weight=10.0,
+        win_size_outside_loss_weight=10.0,
+
+        resize_method='AREA',  # ['BILINEAR', 'NEAREST_NEIGHBOR', 'BICUBIC', 'AREA']
+
+        concat_cursor=True,
+
+        use_softargmax=True,
+        soft_beta=10,  # value for the soft argmax
+
+        raster_loss_weight=1.0,
+
+        dec_rnn_size=256,  # Size of decoder.
+        dec_model='hyper',  # Decoder: lstm, layer_norm or hyper.
+        # z_size=128,  # Size of latent vector z. Recommend 32, 64 or 128.
+        bin_gt=True,
+
+        stop_accu_grad=True,
+
+        random_cursor=True,
+        cursor_type='next',
+
+        raster_size=128,
+
+        pix_drop_kp=1.0,  # Dropout keep rate
+        add_coordconv=True,
+        position_format='abs',
+        raster_loss_base_type='perceptual',  # [l1, mse, perceptual]
+
+        grad_clip=1.0,  # Gradient clipping. Recommend leaving at 1.0.
+
+        learning_rate=0.0001,  # Learning rate.
+        decay_rate=0.9999,  # Learning rate decay per minibatch.
+        decay_power=0.9,
+        min_learning_rate=0.000001,  # Minimum learning rate.
+
+        use_recurrent_dropout=True,  # Dropout with memory loss. Recommended
+        recurrent_dropout_prob=0.90,  # Probability of recurrent dropout keep.
+        use_input_dropout=False,  # Input dropout. Recommend leaving False.
+        input_dropout_prob=0.90,  # Probability of input dropout keep.
+        use_output_dropout=False,  # Output dropout. Recommend leaving False.
+        output_dropout_prob=0.90,  # Probability of output dropout keep.
+
+        model_mode='train'  # ['train', 'eval', 'sample']
+    )
+    return hparams

launch_gui.bat

@@ -0,0 +1,21 @@
+@echo OFF
+
+REM === Cesta k instalaci Anacondy ===
+set "CONDAPATH=C:\ProgramData\anaconda3"
+
+REM === Název a cesta k prostředí ===
+set "ENVNAME=virtual_sketching"
+set "ENVPATH=%USERPROFILE%\.conda\envs\%ENVNAME%"
+
+REM === Aktivace prostředí ===
+call "%CONDAPATH%\Scripts\activate.bat" "%ENVPATH%"
+
+REM === Spuštění GUI ===
+python virtual_sketch_gui.py
+
+REM === Pozastavení po ukončení ===
+echo.
+pause
+
+REM === Deaktivace prostředí ===
+call conda deactivate

model_common_test.py

@@ -0,0 +1,604 @@
+import rnn
+import tensorflow as tf
+
+from subnet_tf_utils import generative_cnn_encoder, generative_cnn_encoder_deeper, generative_cnn_encoder_deeper13, \
+    generative_cnn_c3_encoder, generative_cnn_c3_encoder_deeper, generative_cnn_c3_encoder_deeper13, \
+    generative_cnn_c3_encoder_combine33, generative_cnn_c3_encoder_combine43, \
+    generative_cnn_c3_encoder_combine53, generative_cnn_c3_encoder_combineFC, \
+    generative_cnn_c3_encoder_deeper13_attn
+
+
+class DiffPastingV3(object):
+    def __init__(self, raster_size):
+        self.patch_canvas = tf.placeholder(dtype=tf.float32,
+                                           shape=(None, None, 1))  # (raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+        self.cursor_pos_a = tf.placeholder(dtype=tf.float32, shape=(2))  # (2), float32, in large size
+        self.image_size_a = tf.placeholder(dtype=tf.int32, shape=())  # ()
+        self.window_size_a = tf.placeholder(dtype=tf.float32, shape=())  # (), float32, with grad
+        self.raster_size_a = float(raster_size)
+
+        self.pasted_image = self.image_pasting_sampling_v3()
+        # (image_size, image_size, 1), [0.0-BG, 1.0-stroke]
+
+    def image_pasting_sampling_v3(self):
+        padding_size = tf.cast(tf.ceil(self.window_size_a / 2.0), tf.int32)
+
+        x1y1_a = self.cursor_pos_a - self.window_size_a / 2.0  # (2), float32
+        x2y2_a = self.cursor_pos_a + self.window_size_a / 2.0  # (2), float32
+
+        x1y1_a_floor = tf.floor(x1y1_a)  # (2)
+        x2y2_a_ceil = tf.ceil(x2y2_a)  # (2)
+
+        cursor_pos_b_oricoord = (x1y1_a_floor + x2y2_a_ceil) / 2.0  # (2)
+        cursor_pos_b = (cursor_pos_b_oricoord - x1y1_a) / self.window_size_a * self.raster_size_a  # (2)
+        raster_size_b = (x2y2_a_ceil - x1y1_a_floor)  # (x, y)
+        image_size_b = self.raster_size_a
+        window_size_b = self.raster_size_a * (raster_size_b / self.window_size_a)  # (x, y)
+
+        cursor_b_x, cursor_b_y = tf.split(cursor_pos_b, 2, axis=-1)  # (1)
+
+        y1_b = cursor_b_y - (window_size_b[1] - 1.) / 2.
+        x1_b = cursor_b_x - (window_size_b[0] - 1.) / 2.
+        y2_b = y1_b + (window_size_b[1] - 1.)
+        x2_b = x1_b + (window_size_b[0] - 1.)
+        boxes_b = tf.concat([y1_b, x1_b, y2_b, x2_b], axis=-1)  # (4)
+        boxes_b = boxes_b / tf.cast(image_size_b - 1, tf.float32)  # with grad to window_size_a
+
+        box_ind_b = tf.ones((1), dtype=tf.int32)  # (1)
+        box_ind_b = tf.cumsum(box_ind_b) - 1
+
+        patch_canvas = tf.expand_dims(self.patch_canvas,
+                                      axis=0)  # (1, raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+        boxes_b = tf.expand_dims(boxes_b, axis=0)  # (1, 4)
+
+        valid_canvas = tf.image.crop_and_resize(patch_canvas, boxes_b, box_ind_b,
+                                                crop_size=[raster_size_b[1], raster_size_b[0]])
+        valid_canvas = valid_canvas[0]  # (raster_size_b, raster_size_b, 1)
+
+        pad_up = tf.cast(x1y1_a_floor[1], tf.int32) + padding_size
+        pad_down = self.image_size_a + padding_size - tf.cast(x2y2_a_ceil[1], tf.int32)
+        pad_left = tf.cast(x1y1_a_floor[0], tf.int32) + padding_size
+        pad_right = self.image_size_a + padding_size - tf.cast(x2y2_a_ceil[0], tf.int32)
+
+        paddings = [[pad_up, pad_down],
+                    [pad_left, pad_right],
+                    [0, 0]]
+        pad_img = tf.pad(valid_canvas, paddings=paddings, mode='CONSTANT',
+                         constant_values=0.0)  # (H_p, W_p, 1), [0.0-BG, 1.0-stroke]
+
+        pasted_image = pad_img[padding_size: padding_size + self.image_size_a,
+                       padding_size: padding_size + self.image_size_a, :]
+        # (image_size, image_size, 1), [0.0-BG, 1.0-stroke]
+        return pasted_image
+
+
+class VirtualSketchingModel(object):
+    def __init__(self, hps, gpu_mode=True, reuse=False):
+        """Initializer for the model.
+
+    Args:
+       hps: a HParams object containing model hyperparameters
+       gpu_mode: a boolean that when True, uses GPU mode.
+       reuse: a boolean that when true, attemps to reuse variables.
+    """
+        self.hps = hps
+        assert hps.model_mode in ['train', 'eval', 'eval_sample', 'sample']
+        # with tf.variable_scope('SCC', reuse=reuse):
+        if not gpu_mode:
+            with tf.device('/cpu:0'):
+                print('Model using cpu.')
+                self.build_model()
+        else:
+            print('-' * 100)
+            print('model_mode:', hps.model_mode)
+            print('Model using gpu.')
+            self.build_model()
+
+    def build_model(self):
+        """Define model architecture."""
+        self.config_model()
+
+        initial_state = self.get_decoder_inputs()
+        self.initial_state = initial_state
+
+        ## use pred as the prev points
+        other_params, pen_ras, final_state = self.get_points_and_raster_image(self.image_size)
+        # other_params: (N * max_seq_len, 6)
+        # pen_ras: (N * max_seq_len, 2), after softmax
+
+        self.other_params = other_params  # (N * max_seq_len, 6)
+        self.pen_ras = pen_ras  # (N * max_seq_len, 2), after softmax
+        self.final_state = final_state
+
+        if not self.hps.use_softargmax:
+            pen_state_soft = pen_ras[:, 1:2]  # (N * max_seq_len, 1)
+        else:
+            pen_state_soft = self.differentiable_argmax(pen_ras, self.hps.soft_beta)  # (N * max_seq_len, 1)
+
+        pred_params = tf.concat([pen_state_soft, other_params], axis=1)  # (N * max_seq_len, 7)
+        self.pred_params = tf.reshape(pred_params, shape=[-1, self.hps.max_seq_len, 7])  # (N, max_seq_len, 7)
+        # pred_params: (N, max_seq_len, 7)
+
+    def config_model(self):
+        if self.hps.model_mode == 'train':
+            self.global_step = tf.Variable(0, name='global_step', trainable=False)
+
+        if self.hps.dec_model == 'lstm':
+            dec_cell_fn = rnn.LSTMCell
+        elif self.hps.dec_model == 'layer_norm':
+            dec_cell_fn = rnn.LayerNormLSTMCell
+        elif self.hps.dec_model == 'hyper':
+            dec_cell_fn = rnn.HyperLSTMCell
+        else:
+            assert False, 'please choose a respectable cell'
+
+        use_recurrent_dropout = self.hps.use_recurrent_dropout
+        use_input_dropout = self.hps.use_input_dropout
+        use_output_dropout = self.hps.use_output_dropout
+
+        dec_cell = dec_cell_fn(
+            self.hps.dec_rnn_size,
+            use_recurrent_dropout=use_recurrent_dropout,
+            dropout_keep_prob=self.hps.recurrent_dropout_prob)
+
+        # dropout:
+        # print('Input dropout mode = %s.' % use_input_dropout)
+        # print('Output dropout mode = %s.' % use_output_dropout)
+        # print('Recurrent dropout mode = %s.' % use_recurrent_dropout)
+        if use_input_dropout:
+            print('Dropout to input w/ keep_prob = %4.4f.' % self.hps.input_dropout_prob)
+            dec_cell = tf.contrib.rnn.DropoutWrapper(
+                dec_cell, input_keep_prob=self.hps.input_dropout_prob)
+        if use_output_dropout:
+            print('Dropout to output w/ keep_prob = %4.4f.' % self.hps.output_dropout_prob)
+            dec_cell = tf.contrib.rnn.DropoutWrapper(
+                dec_cell, output_keep_prob=self.hps.output_dropout_prob)
+        self.dec_cell = dec_cell
+
+        self.input_photo = tf.placeholder(dtype=tf.float32,
+                                          shape=[self.hps.batch_size, None, None, self.hps.input_channel])  # [0.0-stroke, 1.0-BG]
+        self.init_cursor = tf.placeholder(
+            dtype=tf.float32,
+            shape=[self.hps.batch_size, 1, 2])  # (N, 1, 2), in size [0.0, 1.0)
+        self.init_width = tf.placeholder(
+            dtype=tf.float32,
+            shape=[self.hps.batch_size])  # (1), in [0.0, 1.0]
+        self.init_scaling = tf.placeholder(
+            dtype=tf.float32,
+            shape=[self.hps.batch_size])  # (N), in [0.0, 1.0]
+        self.init_window_size = tf.placeholder(
+            dtype=tf.float32,
+            shape=[self.hps.batch_size])  # (N)
+        self.image_size = tf.placeholder(dtype=tf.int32, shape=())  # ()
+
+    ###########################
+
+    def normalize_image_m1to1(self, in_img_0to1):
+        norm_img_m1to1 = tf.multiply(in_img_0to1, 2.0)
+        norm_img_m1to1 = tf.subtract(norm_img_m1to1, 1.0)
+        return norm_img_m1to1
+
+    def add_coords(self, input_tensor):
+        batch_size_tensor = tf.shape(input_tensor)[0]  # get N size
+
+        xx_ones = tf.ones([batch_size_tensor, self.hps.raster_size], dtype=tf.int32)  # e.g. (N, raster_size)
+        xx_ones = tf.expand_dims(xx_ones, -1)  # e.g. (N, raster_size, 1)
+        xx_range = tf.tile(tf.expand_dims(tf.range(self.hps.raster_size), 0),
+                           [batch_size_tensor, 1])  # e.g. (N, raster_size)
+        xx_range = tf.expand_dims(xx_range, 1)  # e.g. (N, 1, raster_size)
+
+        xx_channel = tf.matmul(xx_ones, xx_range)  # e.g. (N, raster_size, raster_size)
+        xx_channel = tf.expand_dims(xx_channel, -1)  # e.g. (N, raster_size, raster_size, 1)
+
+        yy_ones = tf.ones([batch_size_tensor, self.hps.raster_size], dtype=tf.int32)  # e.g. (N, raster_size)
+        yy_ones = tf.expand_dims(yy_ones, 1)  # e.g. (N, 1, raster_size)
+        yy_range = tf.tile(tf.expand_dims(tf.range(self.hps.raster_size), 0),
+                           [batch_size_tensor, 1])  # (N, raster_size)
+        yy_range = tf.expand_dims(yy_range, -1)  # e.g. (N, raster_size, 1)
+
+        yy_channel = tf.matmul(yy_range, yy_ones)  # e.g. (N, raster_size, raster_size)
+        yy_channel = tf.expand_dims(yy_channel, -1)  # e.g. (N, raster_size, raster_size, 1)
+
+        xx_channel = tf.cast(xx_channel, 'float32') / (self.hps.raster_size - 1)
+        yy_channel = tf.cast(yy_channel, 'float32') / (self.hps.raster_size - 1)
+        # xx_channel = xx_channel * 2 - 1  # [-1, 1]
+        # yy_channel = yy_channel * 2 - 1
+
+        ret = tf.concat([
+            input_tensor,
+            xx_channel,
+            yy_channel,
+        ], axis=-1)  # e.g. (N, raster_size, raster_size, 4)
+
+        return ret
+
+    def build_combined_encoder(self, patch_canvas, patch_photo, entire_canvas, entire_photo, cursor_pos,
+                               image_size, window_size):
+        """
+        :param patch_canvas: (N, raster_size, raster_size, 1), [-1.0-stroke, 1.0-BG]
+        :param patch_photo: (N, raster_size, raster_size, 1/3), [-1.0-stroke, 1.0-BG]
+        :param entire_canvas: (N, image_size, image_size, 1), [0.0-stroke, 1.0-BG]
+        :param entire_photo: (N, image_size, image_size, 1/3), [0.0-stroke, 1.0-BG]
+        :param cursor_pos: (N, 1, 2), in size [0.0, 1.0)
+        :param window_size: (N, 1, 1), float, in large size
+        :return:
+        """
+        if self.hps.resize_method == 'BILINEAR':
+            resize_method = tf.image.ResizeMethod.BILINEAR
+        elif self.hps.resize_method == 'NEAREST_NEIGHBOR':
+            resize_method = tf.image.ResizeMethod.NEAREST_NEIGHBOR
+        elif self.hps.resize_method == 'BICUBIC':
+            resize_method = tf.image.ResizeMethod.BICUBIC
+        elif self.hps.resize_method == 'AREA':
+            resize_method = tf.image.ResizeMethod.AREA
+        else:
+            raise Exception('unknown resize_method', self.hps.resize_method)
+
+        patch_photo = tf.stop_gradient(patch_photo)
+        patch_canvas = tf.stop_gradient(patch_canvas)
+        cursor_pos = tf.stop_gradient(cursor_pos)
+        window_size = tf.stop_gradient(window_size)
+
+        entire_photo_small = tf.stop_gradient(tf.image.resize_images(entire_photo,
+                                                                      (self.hps.raster_size, self.hps.raster_size),
+                                                                      method=resize_method))
+        entire_canvas_small = tf.stop_gradient(tf.image.resize_images(entire_canvas,
+                                                                      (self.hps.raster_size, self.hps.raster_size),
+                                                                      method=resize_method))
+        entire_photo_small = self.normalize_image_m1to1(entire_photo_small)  # [-1.0-stroke, 1.0-BG]
+        entire_canvas_small = self.normalize_image_m1to1(entire_canvas_small)  # [-1.0-stroke, 1.0-BG]
+
+        if self.hps.encode_cursor_type == 'value':
+            cursor_pos_norm = tf.expand_dims(cursor_pos, axis=1)  # (N, 1, 1, 2)
+            cursor_pos_norm = tf.tile(cursor_pos_norm, [1, self.hps.raster_size, self.hps.raster_size, 1])
+            cursor_info = cursor_pos_norm
+        else:
+            raise Exception('Unknown encode_cursor_type', self.hps.encode_cursor_type)
+
+        batch_input_combined = tf.concat([patch_photo, patch_canvas, entire_photo_small, entire_canvas_small, cursor_info],
+                                axis=-1)  # [N, raster_size, raster_size, 6/10]
+        batch_input_local = tf.concat([patch_photo, patch_canvas], axis=-1)  # [N, raster_size, raster_size, 2/4]
+        batch_input_global = tf.concat([entire_photo_small, entire_canvas_small, cursor_info],
+                                       axis=-1)  # [N, raster_size, raster_size, 4/6]
+
+        if self.hps.model_mode == 'train':
+            is_training = True
+            dropout_keep_prob = self.hps.pix_drop_kp
+        else:
+            is_training = False
+            dropout_keep_prob = 1.0
+
+        if self.hps.add_coordconv:
+            batch_input_combined = self.add_coords(batch_input_combined)  # (N, in_H, in_W, in_dim + 2)
+            batch_input_local = self.add_coords(batch_input_local)  # (N, in_H, in_W, in_dim + 2)
+            batch_input_global = self.add_coords(batch_input_global)  # (N, in_H, in_W, in_dim + 2)
+
+        if 'combine' in self.hps.encoder_type:
+            if self.hps.encoder_type == 'combine33':
+                image_embedding, _ = generative_cnn_c3_encoder_combine33(batch_input_local, batch_input_global,
+                                                                         is_training, dropout_keep_prob)  # (N, 128)
+            elif self.hps.encoder_type == 'combine43':
+                image_embedding, _ = generative_cnn_c3_encoder_combine43(batch_input_local, batch_input_global,
+                                                                         is_training, dropout_keep_prob)  # (N, 128)
+            elif self.hps.encoder_type == 'combine53':
+                image_embedding, _ = generative_cnn_c3_encoder_combine53(batch_input_local, batch_input_global,
+                                                                         is_training, dropout_keep_prob)  # (N, 128)
+            elif self.hps.encoder_type == 'combineFC':
+                image_embedding, _ = generative_cnn_c3_encoder_combineFC(batch_input_local, batch_input_global,
+                                                                         is_training, dropout_keep_prob)  # (N, 256)
+            else:
+                raise Exception('Unknown encoder_type', self.hps.encoder_type)
+        else:
+            with tf.variable_scope('Combined_Encoder', reuse=tf.AUTO_REUSE):
+                if self.hps.encoder_type == 'conv10':
+                    image_embedding, _ = generative_cnn_encoder(batch_input_combined, is_training, dropout_keep_prob)  # (N, 128)
+                elif self.hps.encoder_type == 'conv10_deep':
+                    image_embedding, _ = generative_cnn_encoder_deeper(batch_input_combined, is_training, dropout_keep_prob)  # (N, 512)
+                elif self.hps.encoder_type == 'conv13':
+                    image_embedding, _ = generative_cnn_encoder_deeper13(batch_input_combined, is_training, dropout_keep_prob)  # (N, 128)
+                elif self.hps.encoder_type == 'conv10_c3':
+                    image_embedding, _ = generative_cnn_c3_encoder(batch_input_combined, is_training, dropout_keep_prob)  # (N, 128)
+                elif self.hps.encoder_type == 'conv10_deep_c3':
+                    image_embedding, _ = generative_cnn_c3_encoder_deeper(batch_input_combined, is_training, dropout_keep_prob)  # (N, 512)
+                elif self.hps.encoder_type == 'conv13_c3':
+                    image_embedding, _ = generative_cnn_c3_encoder_deeper13(batch_input_combined, is_training, dropout_keep_prob)  # (N, 128)
+                elif self.hps.encoder_type == 'conv13_c3_attn':
+                    image_embedding, _ = generative_cnn_c3_encoder_deeper13_attn(batch_input_combined, is_training, dropout_keep_prob)  # (N, 128)
+                else:
+                    raise Exception('Unknown encoder_type', self.hps.encoder_type)
+        return image_embedding
+
+    def build_seq_decoder(self, dec_cell, actual_input_x, initial_state):
+        rnn_output, last_state = self.rnn_decoder(dec_cell, initial_state, actual_input_x)
+        rnn_output_flat = tf.reshape(rnn_output, [-1, self.hps.dec_rnn_size])
+
+        pen_n_out = 2
+        params_n_out = 6
+
+        with tf.variable_scope('DEC_RNN_out_pen', reuse=tf.AUTO_REUSE):
+            output_w_pen = tf.get_variable('output_w', [self.hps.dec_rnn_size, pen_n_out])
+            output_b_pen = tf.get_variable('output_b', [pen_n_out], initializer=tf.constant_initializer(0.0))
+            output_pen = tf.nn.xw_plus_b(rnn_output_flat, output_w_pen, output_b_pen)  # (N, pen_n_out)
+
+        with tf.variable_scope('DEC_RNN_out_params', reuse=tf.AUTO_REUSE):
+            output_w_params = tf.get_variable('output_w', [self.hps.dec_rnn_size, params_n_out])
+            output_b_params = tf.get_variable('output_b', [params_n_out], initializer=tf.constant_initializer(0.0))
+            output_params = tf.nn.xw_plus_b(rnn_output_flat, output_w_params, output_b_params)  # (N, params_n_out)
+
+        output = tf.concat([output_pen, output_params], axis=1)  # (N, n_out)
+
+        return output, last_state
+
+    def get_mixture_coef(self, outputs):
+        z = outputs
+        z_pen_logits = z[:, 0:2]  # (N, 2), pen states
+        z_other_params_logits = z[:, 2:]  # (N, 6)
+
+        z_pen = tf.nn.softmax(z_pen_logits)  # (N, 2)
+        if self.hps.position_format == 'abs':
+            x1y1 = tf.nn.sigmoid(z_other_params_logits[:, 0:2])  # (N, 2)
+            x2y2 = tf.tanh(z_other_params_logits[:, 2:4])  # (N, 2)
+            widths = tf.nn.sigmoid(z_other_params_logits[:, 4:5])  # (N, 1)
+            widths = tf.add(tf.multiply(widths, 1.0 - self.hps.min_width), self.hps.min_width)
+            scaling = tf.nn.sigmoid(z_other_params_logits[:, 5:6]) * self.hps.max_scaling  # (N, 1), [0.0, max_scaling]
+            # scaling = tf.add(tf.multiply(scaling, (self.hps.max_scaling - self.hps.min_scaling) / self.hps.max_scaling),
+            #                  self.hps.min_scaling)
+            z_other_params = tf.concat([x1y1, x2y2, widths, scaling], axis=-1)  # (N, 6)
+        else:  # "rel"
+            raise Exception('Unknown position_format', self.hps.position_format)
+
+        r = [z_other_params, z_pen]
+        return r
+
+    ###########################
+
+    def get_decoder_inputs(self):
+        initial_state = self.dec_cell.zero_state(batch_size=self.hps.batch_size, dtype=tf.float32)
+        return initial_state
+
+    def rnn_decoder(self, dec_cell, initial_state, actual_input_x):
+        with tf.variable_scope("RNN_DEC", reuse=tf.AUTO_REUSE):
+            output, last_state = tf.nn.dynamic_rnn(
+                dec_cell,
+                actual_input_x,
+                initial_state=initial_state,
+                time_major=False,
+                swap_memory=True,
+                dtype=tf.float32)
+        return output, last_state
+
+    ###########################
+
+    def image_padding(self, ori_image, window_size, pad_value):
+        """
+        Pad with (bg)
+        :param ori_image:
+        :return:
+        """
+        paddings = [[0, 0],
+                    [window_size // 2, window_size // 2],
+                    [window_size // 2, window_size // 2],
+                    [0, 0]]
+        pad_img = tf.pad(ori_image, paddings=paddings, mode='CONSTANT', constant_values=pad_value)  # (N, H_p, W_p, k)
+        return pad_img
+
+    def image_cropping_fn(self, fn_inputs):
+        """
+        crop the patch
+        :return:
+        """
+        index_offset = self.hps.input_channel - 1
+        input_image = fn_inputs[:, :, 0:2 + index_offset]  # (image_size, image_size, -), [0.0-BG, 1.0-stroke]
+        cursor_pos = fn_inputs[0, 0, 2 + index_offset:4 + index_offset]  # (2), in [0.0, 1.0)
+        image_size = fn_inputs[0, 0, 4 + index_offset]  # (), float32
+        window_size = tf.cast(fn_inputs[0, 0, 5 + index_offset], tf.int32)  # ()
+
+        input_img_reshape = tf.expand_dims(input_image, axis=0)
+        pad_img = self.image_padding(input_img_reshape, window_size, pad_value=0.0)
+
+        cursor_pos = tf.cast(tf.round(tf.multiply(cursor_pos, image_size)), dtype=tf.int32)
+        x0, x1 = cursor_pos[0], cursor_pos[0] + window_size  # ()
+        y0, y1 = cursor_pos[1], cursor_pos[1] + window_size  # ()
+        patch_image = pad_img[:, y0:y1, x0:x1, :]  # (1, window_size, window_size, 2/4)
+
+        # resize to raster_size
+        patch_image_scaled = tf.image.resize_images(patch_image, (self.hps.raster_size, self.hps.raster_size),
+                                                    method=tf.image.ResizeMethod.AREA)
+        patch_image_scaled = tf.squeeze(patch_image_scaled, axis=0)
+        # patch_canvas_scaled: (raster_size, raster_size, 2/4), [0.0-BG, 1.0-stroke]
+
+        return patch_image_scaled
+
+    def image_cropping(self, cursor_position, input_img, image_size, window_sizes):
+        """
+        :param cursor_position: (N, 1, 2), float type, in size [0.0, 1.0)
+        :param input_img: (N, image_size, image_size, 2/4), [0.0-BG, 1.0-stroke]
+        :param window_sizes: (N, 1, 1), float32, with grad
+        """
+        input_img_ = input_img
+        window_sizes_non_grad = tf.stop_gradient(tf.round(window_sizes))  # (N, 1, 1), no grad
+
+        cursor_position_ = tf.reshape(cursor_position, (-1, 1, 1, 2))  # (N, 1, 1, 2)
+        cursor_position_ = tf.tile(cursor_position_, [1, image_size, image_size, 1])  # (N, image_size, image_size, 2)
+
+        image_size_ = tf.reshape(tf.cast(image_size, tf.float32), (1, 1, 1, 1))  # (1, 1, 1, 1)
+        image_size_ = tf.tile(image_size_, [self.hps.batch_size, image_size, image_size, 1])
+
+        window_sizes_ = tf.reshape(window_sizes_non_grad, (-1, 1, 1, 1))  # (N, 1, 1, 1)
+        window_sizes_ = tf.tile(window_sizes_, [1, image_size, image_size, 1])  # (N, image_size, image_size, 1)
+
+        fn_inputs = tf.concat([input_img_, cursor_position_, image_size_, window_sizes_],
+                              axis=-1)  # (N, image_size, image_size, 2/4 + 4)
+        curr_patch_imgs = tf.map_fn(self.image_cropping_fn, fn_inputs, parallel_iterations=32)  # (N, raster_size, raster_size, -)
+        return curr_patch_imgs
+
+    def image_cropping_v3(self, cursor_position, input_img, image_size, window_sizes):
+        """
+        :param cursor_position: (N, 1, 2), float type, in size [0.0, 1.0)
+        :param input_img: (N, image_size, image_size, k), [0.0-BG, 1.0-stroke]
+        :param window_sizes: (N, 1, 1), float32, with grad
+        """
+        window_sizes_non_grad = tf.stop_gradient(window_sizes)  # (N, 1, 1), no grad
+
+        cursor_pos = tf.multiply(cursor_position, tf.cast(image_size, tf.float32))
+        cursor_x, cursor_y = tf.split(cursor_pos, 2, axis=-1)  # (N, 1, 1)
+
+        y1 = cursor_y - (window_sizes_non_grad - 1.0) / 2
+        x1 = cursor_x - (window_sizes_non_grad - 1.0) / 2
+        y2 = y1 + (window_sizes_non_grad - 1.0)
+        x2 = x1 + (window_sizes_non_grad - 1.0)
+        boxes = tf.concat([y1, x1, y2, x2], axis=-1)  # (N, 1, 4)
+        boxes = tf.squeeze(boxes, axis=1)  # (N, 4)
+        boxes = boxes / tf.cast(image_size - 1, tf.float32)
+
+        box_ind = tf.ones_like(cursor_x)[:, 0, 0]  # (N)
+        box_ind = tf.cast(box_ind, dtype=tf.int32)
+        box_ind = tf.cumsum(box_ind) - 1
+
+        curr_patch_imgs = tf.image.crop_and_resize(input_img, boxes, box_ind,
+                                                   crop_size=[self.hps.raster_size, self.hps.raster_size])
+        #  (N, raster_size, raster_size, k), [0.0-BG, 1.0-stroke]
+        return curr_patch_imgs
+
+    def get_points_and_raster_image(self, image_size):
+        ## generate the other_params and pen_ras and raster image for raster loss
+        prev_state = self.initial_state  # (N, dec_rnn_size * 3)
+
+        prev_width = self.init_width  # (N)
+        prev_width = tf.expand_dims(tf.expand_dims(prev_width, axis=-1), axis=-1)  # (N, 1, 1)
+
+        prev_scaling = self.init_scaling  # (N)
+        prev_scaling = tf.reshape(prev_scaling, (-1, 1, 1))  # (N, 1, 1)
+
+        prev_window_size = self.init_window_size  # (N)
+        prev_window_size = tf.reshape(prev_window_size, (-1, 1, 1))  # (N, 1, 1)
+
+        cursor_position_temp = self.init_cursor
+        self.cursor_position = cursor_position_temp  # (N, 1, 2), in size [0.0, 1.0)
+        cursor_position_loop = self.cursor_position
+
+        other_params_list = []
+        pen_ras_list = []
+
+        curr_canvas_soft = tf.zeros_like(self.input_photo[:, :, :, 0])  # (N, image_size, image_size), [0.0-BG, 1.0-stroke]
+        curr_canvas_hard = tf.zeros_like(curr_canvas_soft)  # [0.0-BG, 1.0-stroke]
+
+        #### sampling part - start ####
+        self.curr_canvas_hard = curr_canvas_hard
+
+        if self.hps.cropping_type == 'v3':
+            cropping_func = self.image_cropping_v3
+        # elif self.hps.cropping_type == 'v2':
+        #     cropping_func = self.image_cropping
+        else:
+            raise Exception('Unknown cropping_type', self.hps.cropping_type)
+
+        for time_i in range(self.hps.max_seq_len):
+            cursor_position_non_grad = tf.stop_gradient(cursor_position_loop)  # (N, 1, 2), in size [0.0, 1.0)
+
+            curr_window_size = tf.multiply(prev_scaling, tf.stop_gradient(prev_window_size))  # float, with grad
+            curr_window_size = tf.maximum(curr_window_size, tf.cast(self.hps.min_window_size, tf.float32))
+            curr_window_size = tf.minimum(curr_window_size, tf.cast(image_size, tf.float32))
+
+            ## patch-level encoding
+            # Here, we make the gradients from canvas_z to curr_canvas_hard be None to avoid recurrent gradient propagation.
+            curr_canvas_hard_non_grad = tf.stop_gradient(self.curr_canvas_hard)
+            curr_canvas_hard_non_grad = tf.expand_dims(curr_canvas_hard_non_grad, axis=-1)
+
+            # input_photo: (N, image_size, image_size, 1/3), [0.0-stroke, 1.0-BG]
+            crop_inputs = tf.concat([1.0 - self.input_photo, curr_canvas_hard_non_grad], axis=-1)  # (N, H_p, W_p, 1+1)
+
+            cropped_outputs = cropping_func(cursor_position_non_grad, crop_inputs, image_size, curr_window_size)
+            index_offset = self.hps.input_channel - 1
+            curr_patch_inputs = cropped_outputs[:, :, :, 0:1 + index_offset]  # [0.0-BG, 1.0-stroke]
+            curr_patch_canvas_hard_non_grad = cropped_outputs[:, :, :, 1 + index_offset:2 + index_offset]
+            # (N, raster_size, raster_size, 1/3), [0.0-BG, 1.0-stroke]
+
+            curr_patch_inputs = 1.0 - curr_patch_inputs  # [0.0-stroke, 1.0-BG]
+            curr_patch_inputs = self.normalize_image_m1to1(curr_patch_inputs)
+            # (N, raster_size, raster_size, 1/3), [-1.0-stroke, 1.0-BG]
+
+            # Normalizing image
+            curr_patch_canvas_hard_non_grad = 1.0 - curr_patch_canvas_hard_non_grad  # [0.0-stroke, 1.0-BG]
+            curr_patch_canvas_hard_non_grad = self.normalize_image_m1to1(curr_patch_canvas_hard_non_grad)  # [-1.0-stroke, 1.0-BG]
+
+            ## image-level encoding
+            combined_z = self.build_combined_encoder(
+                curr_patch_canvas_hard_non_grad,
+                curr_patch_inputs,
+                1.0 - curr_canvas_hard_non_grad,
+                self.input_photo,
+                cursor_position_non_grad,
+                image_size,
+                curr_window_size)  # (N, z_size)
+            combined_z = tf.expand_dims(combined_z, axis=1)  # (N, 1, z_size)
+
+            curr_window_size_top_side_norm_non_grad = \
+                tf.stop_gradient(curr_window_size / tf.cast(image_size, tf.float32))
+            curr_window_size_bottom_side_norm_non_grad = \
+                tf.stop_gradient(curr_window_size / tf.cast(self.hps.min_window_size, tf.float32))
+            if not self.hps.concat_win_size:
+                combined_z = tf.concat([tf.stop_gradient(prev_width), combined_z], 2)  # (N, 1, 2+z_size)
+            else:
+                combined_z = tf.concat([tf.stop_gradient(prev_width),
+                                        curr_window_size_top_side_norm_non_grad,
+                                        curr_window_size_bottom_side_norm_non_grad,
+                                        combined_z],
+                                       2)  # (N, 1, 2+z_size)
+
+            if self.hps.concat_cursor:
+                prev_input_x = tf.concat([cursor_position_non_grad, combined_z], 2)  # (N, 1, 2+2+z_size)
+            else:
+                prev_input_x = combined_z  # (N, 1, 2+z_size)
+
+            h_output, next_state = self.build_seq_decoder(self.dec_cell, prev_input_x, prev_state)
+            # h_output: (N * 1, n_out), next_state: (N, dec_rnn_size * 3)
+            [o_other_params, o_pen_ras] = self.get_mixture_coef(h_output)
+            # o_other_params: (N * 1, 6)
+            # o_pen_ras: (N * 1, 2), after softmax
+
+            o_other_params = tf.reshape(o_other_params, [-1, 1, 6])  # (N, 1, 6)
+            o_pen_ras_raw = tf.reshape(o_pen_ras, [-1, 1, 2])  # (N, 1, 2)
+
+            other_params_list.append(o_other_params)
+            pen_ras_list.append(o_pen_ras_raw)
+
+            #### sampling part - end ####
+
+            prev_state = next_state
+
+        other_params_ = tf.reshape(tf.concat(other_params_list, axis=1), [-1, 6])  # (N * max_seq_len, 6)
+        pen_ras_ = tf.reshape(tf.concat(pen_ras_list, axis=1), [-1, 2])  # (N * max_seq_len, 2)
+
+        return other_params_, pen_ras_, prev_state
+
+    def differentiable_argmax(self, input_pen, soft_beta):
+        """
+        Differentiable argmax trick.
+        :param input_pen: (N, n_class)
+        :return: pen_state: (N, 1)
+        """
+        def sign_onehot(x):
+            """
+            :param x: (N, n_class)
+            :return:  (N, n_class)
+            """
+            y = tf.sign(tf.reduce_max(x, axis=-1, keepdims=True) - x)
+            y = (y - 1) * (-1)
+            return y
+
+        def softargmax(x, beta=1e2):
+            """
+            :param x: (N, n_class)
+            :param beta: 1e10 is the best. 1e2 is acceptable.
+            :return:  (N)
+            """
+            x_range = tf.cumsum(tf.ones_like(x), axis=1)  # (N, 2)
+            return tf.reduce_sum(tf.nn.softmax(x * beta) * x_range, axis=1) - 1
+
+        ## Better to use softargmax(beta=1e2). The sign_onehot's gradient is close to zero.
+        # pen_onehot = sign_onehot(input_pen)  # one-hot form, (N * max_seq_len, 2)
+        # pen_state = pen_onehot[:, 1:2]  # (N * max_seq_len, 1)
+        pen_state = softargmax(input_pen, soft_beta)
+        pen_state = tf.expand_dims(pen_state, axis=1)  # (N * max_seq_len, 1)
+        return pen_state

model_common_train.py

@@ -0,0 +1,1193 @@
+import rnn
+import tensorflow as tf
+
+from subnet_tf_utils import generative_cnn_encoder, generative_cnn_encoder_deeper, generative_cnn_encoder_deeper13, \
+    generative_cnn_c3_encoder, generative_cnn_c3_encoder_deeper, generative_cnn_c3_encoder_deeper13, \
+    generative_cnn_c3_encoder_combine33, generative_cnn_c3_encoder_combine43, \
+    generative_cnn_c3_encoder_combine53, generative_cnn_c3_encoder_combineFC, \
+    generative_cnn_c3_encoder_deeper13_attn
+from rasterization_utils.NeuralRenderer import NeuralRasterizorStep
+from vgg_utils.VGG16 import vgg_net_slim
+
+
+class VirtualSketchingModel(object):
+    def __init__(self, hps, gpu_mode=True, reuse=False):
+        """Initializer for the model.
+
+    Args:
+       hps: a HParams object containing model hyperparameters
+       gpu_mode: a boolean that when True, uses GPU mode.
+       reuse: a boolean that when true, attemps to reuse variables.
+    """
+        self.hps = hps
+        assert hps.model_mode in ['train', 'eval', 'eval_sample', 'sample']
+        # with tf.variable_scope('SCC', reuse=reuse):
+        if not gpu_mode:
+            with tf.device('/cpu:0'):
+                print('Model using cpu.')
+                self.build_model()
+        else:
+            print('-' * 100)
+            print('model_mode:', hps.model_mode)
+            print('Model using gpu.')
+            self.build_model()
+
+    def build_model(self):
+        """Define model architecture."""
+        self.config_model()
+
+        initial_state = self.get_decoder_inputs()
+        self.initial_state = initial_state
+        self.initial_state_list = tf.split(self.initial_state, self.total_loop, axis=0)
+
+        total_loss_list = []
+        ras_loss_list = []
+        perc_relu_raw_list = []
+        perc_relu_norm_list = []
+        sn_loss_list = []
+        cursor_outside_loss_list = []
+        win_size_outside_loss_list = []
+        early_state_loss_list = []
+
+        tower_grads = []
+
+        pred_raster_imgs_list = []
+        pred_raster_imgs_rgb_list = []
+
+        for t_i in range(self.total_loop):
+            gpu_idx = t_i // self.hps.loop_per_gpu
+            gpu_i = self.hps.gpus[gpu_idx]
+            print(self.hps.model_mode, 'model, gpu:', gpu_i, ', loop:', t_i % self.hps.loop_per_gpu)
+            with tf.device('/gpu:%d' % gpu_i):
+                with tf.name_scope('GPU_%d' % gpu_i) as scope:
+                    if t_i > 0:
+                        tf.get_variable_scope().reuse_variables()
+                    else:
+                        total_loss_list.clear()
+                        ras_loss_list.clear()
+                        perc_relu_raw_list.clear()
+                        perc_relu_norm_list.clear()
+                        sn_loss_list.clear()
+                        cursor_outside_loss_list.clear()
+                        win_size_outside_loss_list.clear()
+                        early_state_loss_list.clear()
+                        tower_grads.clear()
+                        pred_raster_imgs_list.clear()
+                        pred_raster_imgs_rgb_list.clear()
+
+                    split_input_photo = self.input_photo_list[t_i]
+                    split_image_size = self.image_size[t_i]
+                    split_init_cursor = self.init_cursor_list[t_i]
+                    split_initial_state = self.initial_state_list[t_i]
+                    if self.hps.input_channel == 1:
+                        split_target_sketch = split_input_photo
+                    else:
+                        split_target_sketch = self.target_sketch_list[t_i]
+
+                    ## use pred as the prev points
+                    other_params, pen_ras, final_state, pred_raster_images, pred_raster_images_rgb, \
+                    pos_before_max_min, win_size_before_max_min \
+                        = self.get_points_and_raster_image(split_initial_state, split_init_cursor, split_input_photo,
+                                                           split_image_size)
+                    # other_params: (N * max_seq_len, 6)
+                    # pen_ras: (N * max_seq_len, 2), after softmax
+                    # pos_before_max_min: (N, max_seq_len, 2), in image_size
+                    # win_size_before_max_min: (N, max_seq_len, 1), in image_size
+
+                    pred_raster_imgs = 1.0 - pred_raster_images  # (N, image_size, image_size), [0.0-stroke, 1.0-BG]
+                    pred_raster_imgs_rgb = 1.0 - pred_raster_images_rgb  # (N, image_size, image_size, 3)
+                    pred_raster_imgs_list.append(pred_raster_imgs)
+                    pred_raster_imgs_rgb_list.append(pred_raster_imgs_rgb)
+
+                    if not self.hps.use_softargmax:
+                        pen_state_soft = pen_ras[:, 1:2]  # (N * max_seq_len, 1)
+                    else:
+                        pen_state_soft = self.differentiable_argmax(pen_ras, self.hps.soft_beta)  # (N * max_seq_len, 1)
+
+                    pred_params = tf.concat([pen_state_soft, other_params], axis=1)  # (N * max_seq_len, 7)
+                    pred_params = tf.reshape(pred_params, shape=[-1, self.hps.max_seq_len, 7])  # (N, max_seq_len, 7)
+                    # pred_params: (N, max_seq_len, 7)
+
+                    if self.hps.model_mode == 'train' or self.hps.model_mode == 'eval':
+                        raster_cost, sn_cost, cursor_outside_cost, winsize_outside_cost, \
+                        early_pen_states_cost, \
+                        perc_relu_loss_raw, perc_relu_loss_norm = \
+                            self.build_losses(split_target_sketch, pred_raster_imgs, pred_params,
+                                              pos_before_max_min, win_size_before_max_min,
+                                              split_image_size)
+                        # perc_relu_loss_raw, perc_relu_loss_norm: (n_layers)
+
+                        ras_loss_list.append(raster_cost)
+                        perc_relu_raw_list.append(perc_relu_loss_raw)
+                        perc_relu_norm_list.append(perc_relu_loss_norm)
+                        sn_loss_list.append(sn_cost)
+                        cursor_outside_loss_list.append(cursor_outside_cost)
+                        win_size_outside_loss_list.append(winsize_outside_cost)
+                        early_state_loss_list.append(early_pen_states_cost)
+
+                        if self.hps.model_mode == 'train':
+                            total_cost_split, grads_and_vars_split = self.build_training_op_split(
+                                raster_cost, sn_cost, cursor_outside_cost, winsize_outside_cost,
+                                early_pen_states_cost)
+                            total_loss_list.append(total_cost_split)
+                            tower_grads.append(grads_and_vars_split)
+
+        self.raster_cost = tf.reduce_mean(tf.stack(ras_loss_list, axis=0))
+        self.perc_relu_losses_raw = tf.reduce_mean(tf.stack(perc_relu_raw_list, axis=0), axis=0)  # (n_layers)
+        self.perc_relu_losses_norm = tf.reduce_mean(tf.stack(perc_relu_norm_list, axis=0), axis=0)  # (n_layers)
+        self.stroke_num_cost = tf.reduce_mean(tf.stack(sn_loss_list, axis=0))
+        self.pos_outside_cost = tf.reduce_mean(tf.stack(cursor_outside_loss_list, axis=0))
+        self.win_size_outside_cost = tf.reduce_mean(tf.stack(win_size_outside_loss_list, axis=0))
+        self.early_pen_states_cost = tf.reduce_mean(tf.stack(early_state_loss_list, axis=0))
+        self.cost = tf.reduce_mean(tf.stack(total_loss_list, axis=0))
+
+        self.pred_raster_imgs = tf.concat(pred_raster_imgs_list, axis=0)  # (N, image_size, image_size), [0.0-stroke, 1.0-BG]
+        self.pred_raster_imgs_rgb = tf.concat(pred_raster_imgs_rgb_list, axis=0)  # (N, image_size, image_size, 3)
+
+        if self.hps.model_mode == 'train':
+            self.build_training_op(tower_grads)
+
+    def config_model(self):
+        if self.hps.model_mode == 'train':
+            self.global_step = tf.Variable(0, name='global_step', trainable=False)
+
+        if self.hps.dec_model == 'lstm':
+            dec_cell_fn = rnn.LSTMCell
+        elif self.hps.dec_model == 'layer_norm':
+            dec_cell_fn = rnn.LayerNormLSTMCell
+        elif self.hps.dec_model == 'hyper':
+            dec_cell_fn = rnn.HyperLSTMCell
+        else:
+            assert False, 'please choose a respectable cell'
+
+        use_recurrent_dropout = self.hps.use_recurrent_dropout
+        use_input_dropout = self.hps.use_input_dropout
+        use_output_dropout = self.hps.use_output_dropout
+
+        dec_cell = dec_cell_fn(
+            self.hps.dec_rnn_size,
+            use_recurrent_dropout=use_recurrent_dropout,
+            dropout_keep_prob=self.hps.recurrent_dropout_prob)
+
+        # dropout:
+        # print('Input dropout mode = %s.' % use_input_dropout)
+        # print('Output dropout mode = %s.' % use_output_dropout)
+        # print('Recurrent dropout mode = %s.' % use_recurrent_dropout)
+        if use_input_dropout:
+            print('Dropout to input w/ keep_prob = %4.4f.' % self.hps.input_dropout_prob)
+            dec_cell = tf.contrib.rnn.DropoutWrapper(
+                dec_cell, input_keep_prob=self.hps.input_dropout_prob)
+        if use_output_dropout:
+            print('Dropout to output w/ keep_prob = %4.4f.' % self.hps.output_dropout_prob)
+            dec_cell = tf.contrib.rnn.DropoutWrapper(
+                dec_cell, output_keep_prob=self.hps.output_dropout_prob)
+        self.dec_cell = dec_cell
+
+        self.total_loop = len(self.hps.gpus) * self.hps.loop_per_gpu
+
+        self.init_cursor = tf.placeholder(
+            dtype=tf.float32,
+            shape=[self.hps.batch_size, 1, 2])  # (N, 1, 2), in size [0.0, 1.0)
+        self.init_width = tf.placeholder(
+            dtype=tf.float32,
+            shape=[1])  # (1), in [0.0, 1.0]
+        self.image_size = tf.placeholder(dtype=tf.int32, shape=(self.total_loop))  # ()
+
+        self.init_cursor_list = tf.split(self.init_cursor, self.total_loop, axis=0)
+        self.input_photo_list = []
+        for loop_i in range(self.total_loop):
+            input_photo_i = tf.placeholder(dtype=tf.float32, shape=[None, None, None, self.hps.input_channel])  # [0.0-stroke, 1.0-BG]
+            self.input_photo_list.append(input_photo_i)
+
+        if self.hps.input_channel == 3:
+            self.target_sketch_list = []
+            for loop_i in range(self.total_loop):
+                target_sketch_i = tf.placeholder(dtype=tf.float32, shape=[None, None, None, 1])  # [0.0-stroke, 1.0-BG]
+                self.target_sketch_list.append(target_sketch_i)
+
+        if self.hps.model_mode == 'train' or self.hps.model_mode == 'eval':
+            self.stroke_num_loss_weight = tf.Variable(0.0, trainable=False)
+            self.early_pen_loss_start_idx = tf.Variable(0, dtype=tf.int32, trainable=False)
+            self.early_pen_loss_end_idx = tf.Variable(0, dtype=tf.int32, trainable=False)
+
+        if self.hps.model_mode == 'train':
+            self.perc_loss_mean_list = []
+            for loop_i in range(len(self.hps.perc_loss_layers)):
+                relu_loss_mean = tf.Variable(0.0, trainable=False)
+                self.perc_loss_mean_list.append(relu_loss_mean)
+            self.last_step_num = tf.Variable(0.0, trainable=False)
+
+            with tf.variable_scope('train_op', reuse=tf.AUTO_REUSE):
+                self.lr = tf.Variable(self.hps.learning_rate, trainable=False)
+                self.optimizer = tf.train.AdamOptimizer(self.lr)
+
+    ###########################
+
+    def normalize_image_m1to1(self, in_img_0to1):
+        norm_img_m1to1 = tf.multiply(in_img_0to1, 2.0)
+        norm_img_m1to1 = tf.subtract(norm_img_m1to1, 1.0)
+        return norm_img_m1to1
+
+    def add_coords(self, input_tensor):
+        batch_size_tensor = tf.shape(input_tensor)[0]  # get N size
+
+        xx_ones = tf.ones([batch_size_tensor, self.hps.raster_size], dtype=tf.int32)  # e.g. (N, raster_size)
+        xx_ones = tf.expand_dims(xx_ones, -1)  # e.g. (N, raster_size, 1)
+        xx_range = tf.tile(tf.expand_dims(tf.range(self.hps.raster_size), 0),
+                           [batch_size_tensor, 1])  # e.g. (N, raster_size)
+        xx_range = tf.expand_dims(xx_range, 1)  # e.g. (N, 1, raster_size)
+
+        xx_channel = tf.matmul(xx_ones, xx_range)  # e.g. (N, raster_size, raster_size)
+        xx_channel = tf.expand_dims(xx_channel, -1)  # e.g. (N, raster_size, raster_size, 1)
+
+        yy_ones = tf.ones([batch_size_tensor, self.hps.raster_size], dtype=tf.int32)  # e.g. (N, raster_size)
+        yy_ones = tf.expand_dims(yy_ones, 1)  # e.g. (N, 1, raster_size)
+        yy_range = tf.tile(tf.expand_dims(tf.range(self.hps.raster_size), 0),
+                           [batch_size_tensor, 1])  # (N, raster_size)
+        yy_range = tf.expand_dims(yy_range, -1)  # e.g. (N, raster_size, 1)
+
+        yy_channel = tf.matmul(yy_range, yy_ones)  # e.g. (N, raster_size, raster_size)
+        yy_channel = tf.expand_dims(yy_channel, -1)  # e.g. (N, raster_size, raster_size, 1)
+
+        xx_channel = tf.cast(xx_channel, 'float32') / (self.hps.raster_size - 1)
+        yy_channel = tf.cast(yy_channel, 'float32') / (self.hps.raster_size - 1)
+        # xx_channel = xx_channel * 2 - 1  # [-1, 1]
+        # yy_channel = yy_channel * 2 - 1
+
+        ret = tf.concat([
+            input_tensor,
+            xx_channel,
+            yy_channel,
+        ], axis=-1)  # e.g. (N, raster_size, raster_size, 4)
+
+        return ret
+
+    def build_combined_encoder(self, patch_canvas, patch_photo, entire_canvas, entire_photo, cursor_pos,
+                               image_size, window_size):
+        """
+        :param patch_canvas: (N, raster_size, raster_size, 1), [-1.0-stroke, 1.0-BG]
+        :param patch_photo: (N, raster_size, raster_size, 1/3), [-1.0-stroke, 1.0-BG]
+        :param entire_canvas: (N, image_size, image_size, 1), [0.0-stroke, 1.0-BG]
+        :param entire_photo: (N, image_size, image_size, 1/3), [0.0-stroke, 1.0-BG]
+        :param cursor_pos: (N, 1, 2), in size [0.0, 1.0)
+        :param window_size: (N, 1, 1), float, in large size
+        :return:
+        """
+        if self.hps.resize_method == 'BILINEAR':
+            resize_method = tf.image.ResizeMethod.BILINEAR
+        elif self.hps.resize_method == 'NEAREST_NEIGHBOR':
+            resize_method = tf.image.ResizeMethod.NEAREST_NEIGHBOR
+        elif self.hps.resize_method == 'BICUBIC':
+            resize_method = tf.image.ResizeMethod.BICUBIC
+        elif self.hps.resize_method == 'AREA':
+            resize_method = tf.image.ResizeMethod.AREA
+        else:
+            raise Exception('unknown resize_method', self.hps.resize_method)
+
+        patch_photo = tf.stop_gradient(patch_photo)
+        patch_canvas = tf.stop_gradient(patch_canvas)
+        cursor_pos = tf.stop_gradient(cursor_pos)
+        window_size = tf.stop_gradient(window_size)
+
+        entire_photo_small = tf.stop_gradient(tf.image.resize_images(entire_photo,
+                                                                      (self.hps.raster_size, self.hps.raster_size),
+                                                                      method=resize_method))
+        entire_canvas_small = tf.stop_gradient(tf.image.resize_images(entire_canvas,
+                                                                      (self.hps.raster_size, self.hps.raster_size),
+                                                                      method=resize_method))
+        entire_photo_small = self.normalize_image_m1to1(entire_photo_small)  # [-1.0-stroke, 1.0-BG]
+        entire_canvas_small = self.normalize_image_m1to1(entire_canvas_small)  # [-1.0-stroke, 1.0-BG]
+
+        if self.hps.encode_cursor_type == 'value':
+            cursor_pos_norm = tf.expand_dims(cursor_pos, axis=1)  # (N, 1, 1, 2)
+            cursor_pos_norm = tf.tile(cursor_pos_norm, [1, self.hps.raster_size, self.hps.raster_size, 1])
+            cursor_info = cursor_pos_norm
+        else:
+            raise Exception('Unknown encode_cursor_type', self.hps.encode_cursor_type)
+
+        batch_input_combined = tf.concat([patch_photo, patch_canvas, entire_photo_small, entire_canvas_small, cursor_info],
+                                axis=-1)  # [N, raster_size, raster_size, 6/10]
+        batch_input_local = tf.concat([patch_photo, patch_canvas], axis=-1)  # [N, raster_size, raster_size, 2/4]
+        batch_input_global = tf.concat([entire_photo_small, entire_canvas_small, cursor_info],
+                                       axis=-1)  # [N, raster_size, raster_size, 4/6]
+
+        if self.hps.model_mode == 'train':
+            is_training = True
+            dropout_keep_prob = self.hps.pix_drop_kp
+        else:
+            is_training = False
+            dropout_keep_prob = 1.0
+
+        if self.hps.add_coordconv:
+            batch_input_combined = self.add_coords(batch_input_combined)  # (N, in_H, in_W, in_dim + 2)
+            batch_input_local = self.add_coords(batch_input_local)  # (N, in_H, in_W, in_dim + 2)
+            batch_input_global = self.add_coords(batch_input_global)  # (N, in_H, in_W, in_dim + 2)
+
+        if 'combine' in self.hps.encoder_type:
+            if self.hps.encoder_type == 'combine33':
+                image_embedding, _ = generative_cnn_c3_encoder_combine33(batch_input_local, batch_input_global,
+                                                                         is_training, dropout_keep_prob)  # (N, 128)
+            elif self.hps.encoder_type == 'combine43':
+                image_embedding, _ = generative_cnn_c3_encoder_combine43(batch_input_local, batch_input_global,
+                                                                         is_training, dropout_keep_prob)  # (N, 128)
+            elif self.hps.encoder_type == 'combine53':
+                image_embedding, _ = generative_cnn_c3_encoder_combine53(batch_input_local, batch_input_global,
+                                                                         is_training, dropout_keep_prob)  # (N, 128)
+            elif self.hps.encoder_type == 'combineFC':
+                image_embedding, _ = generative_cnn_c3_encoder_combineFC(batch_input_local, batch_input_global,
+                                                                         is_training, dropout_keep_prob)  # (N, 256)
+            else:
+                raise Exception('Unknown encoder_type', self.hps.encoder_type)
+        else:
+            with tf.variable_scope('Combined_Encoder', reuse=tf.AUTO_REUSE):
+                if self.hps.encoder_type == 'conv10':
+                    image_embedding, _ = generative_cnn_encoder(batch_input_combined, is_training, dropout_keep_prob)  # (N, 128)
+                elif self.hps.encoder_type == 'conv10_deep':
+                    image_embedding, _ = generative_cnn_encoder_deeper(batch_input_combined, is_training, dropout_keep_prob)  # (N, 512)
+                elif self.hps.encoder_type == 'conv13':
+                    image_embedding, _ = generative_cnn_encoder_deeper13(batch_input_combined, is_training, dropout_keep_prob)  # (N, 128)
+                elif self.hps.encoder_type == 'conv10_c3':
+                    image_embedding, _ = generative_cnn_c3_encoder(batch_input_combined, is_training, dropout_keep_prob)  # (N, 128)
+                elif self.hps.encoder_type == 'conv10_deep_c3':
+                    image_embedding, _ = generative_cnn_c3_encoder_deeper(batch_input_combined, is_training, dropout_keep_prob)  # (N, 512)
+                elif self.hps.encoder_type == 'conv13_c3':
+                    image_embedding, _ = generative_cnn_c3_encoder_deeper13(batch_input_combined, is_training, dropout_keep_prob)  # (N, 128)
+                elif self.hps.encoder_type == 'conv13_c3_attn':
+                    image_embedding, _ = generative_cnn_c3_encoder_deeper13_attn(batch_input_combined, is_training, dropout_keep_prob)  # (N, 128)
+                else:
+                    raise Exception('Unknown encoder_type', self.hps.encoder_type)
+        return image_embedding
+
+    def build_seq_decoder(self, dec_cell, actual_input_x, initial_state):
+        rnn_output, last_state = self.rnn_decoder(dec_cell, initial_state, actual_input_x)
+        rnn_output_flat = tf.reshape(rnn_output, [-1, self.hps.dec_rnn_size])
+
+        pen_n_out = 2
+        params_n_out = 6
+
+        with tf.variable_scope('DEC_RNN_out_pen', reuse=tf.AUTO_REUSE):
+            output_w_pen = tf.get_variable('output_w', [self.hps.dec_rnn_size, pen_n_out])
+            output_b_pen = tf.get_variable('output_b', [pen_n_out], initializer=tf.constant_initializer(0.0))
+            output_pen = tf.nn.xw_plus_b(rnn_output_flat, output_w_pen, output_b_pen)  # (N, pen_n_out)
+
+        with tf.variable_scope('DEC_RNN_out_params', reuse=tf.AUTO_REUSE):
+            output_w_params = tf.get_variable('output_w', [self.hps.dec_rnn_size, params_n_out])
+            output_b_params = tf.get_variable('output_b', [params_n_out], initializer=tf.constant_initializer(0.0))
+            output_params = tf.nn.xw_plus_b(rnn_output_flat, output_w_params, output_b_params)  # (N, params_n_out)
+
+        output = tf.concat([output_pen, output_params], axis=1)  # (N, n_out)
+
+        return output, last_state
+
+    def get_mixture_coef(self, outputs):
+        z = outputs
+        z_pen_logits = z[:, 0:2]  # (N, 2), pen states
+        z_other_params_logits = z[:, 2:]  # (N, 6)
+
+        z_pen = tf.nn.softmax(z_pen_logits)  # (N, 2)
+        if self.hps.position_format == 'abs':
+            x1y1 = tf.nn.sigmoid(z_other_params_logits[:, 0:2])  # (N, 2)
+            x2y2 = tf.tanh(z_other_params_logits[:, 2:4])  # (N, 2)
+            widths = tf.nn.sigmoid(z_other_params_logits[:, 4:5])  # (N, 1)
+            widths = tf.add(tf.multiply(widths, 1.0 - self.hps.min_width), self.hps.min_width)
+            scaling = tf.nn.sigmoid(z_other_params_logits[:, 5:6]) * self.hps.max_scaling  # (N, 1), [0.0, max_scaling]
+            # scaling = tf.add(tf.multiply(scaling, (self.hps.max_scaling - self.hps.min_scaling) / self.hps.max_scaling),
+            #                  self.hps.min_scaling)
+            z_other_params = tf.concat([x1y1, x2y2, widths, scaling], axis=-1)  # (N, 6)
+        else:  # "rel"
+            raise Exception('Unknown position_format', self.hps.position_format)
+
+        r = [z_other_params, z_pen]
+        return r
+
+    ###########################
+
+    def get_decoder_inputs(self):
+        initial_state = self.dec_cell.zero_state(batch_size=self.hps.batch_size, dtype=tf.float32)
+        return initial_state
+
+    def rnn_decoder(self, dec_cell, initial_state, actual_input_x):
+        with tf.variable_scope("RNN_DEC", reuse=tf.AUTO_REUSE):
+            output, last_state = tf.nn.dynamic_rnn(
+                dec_cell,
+                actual_input_x,
+                initial_state=initial_state,
+                time_major=False,
+                swap_memory=True,
+                dtype=tf.float32)
+        return output, last_state
+
+    ###########################
+
+    def image_padding(self, ori_image, window_size, pad_value):
+        """
+        Pad with (bg)
+        :param ori_image:
+        :return:
+        """
+        paddings = [[0, 0],
+                    [window_size // 2, window_size // 2],
+                    [window_size // 2, window_size // 2],
+                    [0, 0]]
+        pad_img = tf.pad(ori_image, paddings=paddings, mode='CONSTANT', constant_values=pad_value)  # (N, H_p, W_p, k)
+        return pad_img
+
+    def image_cropping_fn(self, fn_inputs):
+        """
+        crop the patch
+        :return:
+        """
+        index_offset = self.hps.input_channel - 1
+        input_image = fn_inputs[:, :, 0:2 + index_offset]  # (image_size, image_size, 2), [0.0-BG, 1.0-stroke]
+        cursor_pos = fn_inputs[0, 0, 2 + index_offset:4 + index_offset]  # (2), in [0.0, 1.0)
+        image_size = fn_inputs[0, 0, 4 + index_offset]  # (), float32
+        window_size = tf.cast(fn_inputs[0, 0, 5 + index_offset], tf.int32)  # ()
+
+        input_img_reshape = tf.expand_dims(input_image, axis=0)
+        pad_img = self.image_padding(input_img_reshape, window_size, pad_value=0.0)
+
+        cursor_pos = tf.cast(tf.round(tf.multiply(cursor_pos, image_size)), dtype=tf.int32)
+        x0, x1 = cursor_pos[0], cursor_pos[0] + window_size  # ()
+        y0, y1 = cursor_pos[1], cursor_pos[1] + window_size  # ()
+        patch_image = pad_img[:, y0:y1, x0:x1, :]  # (1, window_size, window_size, 2/4)
+
+        # resize to raster_size
+        patch_image_scaled = tf.image.resize_images(patch_image, (self.hps.raster_size, self.hps.raster_size),
+                                                    method=tf.image.ResizeMethod.AREA)
+        patch_image_scaled = tf.squeeze(patch_image_scaled, axis=0)
+        # patch_canvas_scaled: (raster_size, raster_size, 2/4), [0.0-BG, 1.0-stroke]
+
+        return patch_image_scaled
+
+    def image_cropping(self, cursor_position, input_img, image_size, window_sizes):
+        """
+        :param cursor_position: (N, 1, 2), float type, in size [0.0, 1.0)
+        :param input_img: (N, image_size, image_size, 2/4), [0.0-BG, 1.0-stroke]
+        :param window_sizes: (N, 1, 1), float32, with grad
+        """
+        input_img_ = input_img
+        window_sizes_non_grad = tf.stop_gradient(tf.round(window_sizes))  # (N, 1, 1), no grad
+
+        cursor_position_ = tf.reshape(cursor_position, (-1, 1, 1, 2))  # (N, 1, 1, 2)
+        cursor_position_ = tf.tile(cursor_position_, [1, image_size, image_size, 1])  # (N, image_size, image_size, 2)
+
+        image_size_ = tf.reshape(tf.cast(image_size, tf.float32), (1, 1, 1, 1))  # (1, 1, 1, 1)
+        image_size_ = tf.tile(image_size_, [self.hps.batch_size // self.total_loop, image_size, image_size, 1])
+
+        window_sizes_ = tf.reshape(window_sizes_non_grad, (-1, 1, 1, 1))  # (N, 1, 1, 1)
+        window_sizes_ = tf.tile(window_sizes_, [1, image_size, image_size, 1])  # (N, image_size, image_size, 1)
+
+        fn_inputs = tf.concat([input_img_, cursor_position_, image_size_, window_sizes_],
+                              axis=-1)  # (N, image_size, image_size, 2/4 + 4)
+        curr_patch_imgs = tf.map_fn(self.image_cropping_fn, fn_inputs, parallel_iterations=32)  # (N, raster_size, raster_size, -)
+        return curr_patch_imgs
+
+    def image_cropping_v3(self, cursor_position, input_img, image_size, window_sizes):
+        """
+        :param cursor_position: (N, 1, 2), float type, in size [0.0, 1.0)
+        :param input_img: (N, image_size, image_size, k), [0.0-BG, 1.0-stroke]
+        :param window_sizes: (N, 1, 1), float32, with grad
+        """
+        window_sizes_non_grad = tf.stop_gradient(window_sizes)  # (N, 1, 1), no grad
+
+        cursor_pos = tf.multiply(cursor_position, tf.cast(image_size, tf.float32))
+        cursor_x, cursor_y = tf.split(cursor_pos, 2, axis=-1)  # (N, 1, 1)
+
+        y1 = cursor_y - (window_sizes_non_grad - 1.0) / 2
+        x1 = cursor_x - (window_sizes_non_grad - 1.0) / 2
+        y2 = y1 + (window_sizes_non_grad - 1.0)
+        x2 = x1 + (window_sizes_non_grad - 1.0)
+        boxes = tf.concat([y1, x1, y2, x2], axis=-1)  # (N, 1, 4)
+        boxes = tf.squeeze(boxes, axis=1)  # (N, 4)
+        boxes = boxes / tf.cast(image_size - 1, tf.float32)
+
+        box_ind = tf.ones_like(cursor_x)[:, 0, 0]  # (N)
+        box_ind = tf.cast(box_ind, dtype=tf.int32)
+        box_ind = tf.cumsum(box_ind) - 1
+
+        curr_patch_imgs = tf.image.crop_and_resize(input_img, boxes, box_ind,
+                                                   crop_size=[self.hps.raster_size, self.hps.raster_size])
+        #  (N, raster_size, raster_size, k), [0.0-BG, 1.0-stroke]
+        return curr_patch_imgs
+
+    def get_pixel_value(self, img, x, y):
+        """
+        Utility function to get pixel value for coordinate vectors x and y from a  4D tensor image.
+
+        Input
+        -----
+        - img: tensor of shape (B, H, W, C)
+        - x: flattened tensor of shape (B, H', W')
+        - y: flattened tensor of shape (B, H', W')
+
+        Returns
+        -------
+        - output: tensor of shape (B, H', W', C)
+        """
+        shape = tf.shape(x)
+        batch_size = shape[0]
+        height = shape[1]
+        width = shape[2]
+
+        batch_idx = tf.range(0, batch_size)
+        batch_idx = tf.reshape(batch_idx, (batch_size, 1, 1))
+        b = tf.tile(batch_idx, (1, height, width))
+
+        indices = tf.stack([b, y, x], 3)
+
+        return tf.gather_nd(img, indices)
+
+    def image_pasting_nondiff_single(self, fn_inputs):
+        patch_image = fn_inputs[:, :, 0:1]  # (raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+        cursor_pos = fn_inputs[0, 0, 1:3]  # (2), in large size
+        image_size = tf.cast(fn_inputs[0, 0, 3], tf.int32)  # ()
+        window_size = tf.cast(fn_inputs[0, 0, 4], tf.int32)  # ()
+
+        patch_image_scaled = tf.expand_dims(patch_image, axis=0)  # (1, raster_size, raster_size, 1)
+        patch_image_scaled = tf.image.resize_images(patch_image_scaled, (window_size, window_size),
+                                                    method=tf.image.ResizeMethod.BILINEAR)
+        patch_image_scaled = tf.squeeze(patch_image_scaled, axis=0)
+        # patch_canvas_scaled: (window_size, window_size, 1)
+
+        cursor_pos = tf.cast(tf.round(cursor_pos), dtype=tf.int32)  # (2)
+        cursor_x, cursor_y = cursor_pos[0], cursor_pos[1]
+
+        pad_up = cursor_y
+        pad_down = image_size - cursor_y
+        pad_left = cursor_x
+        pad_right = image_size - cursor_x
+
+        paddings = [[pad_up, pad_down],
+                    [pad_left, pad_right],
+                    [0, 0]]
+        pad_img = tf.pad(patch_image_scaled, paddings=paddings, mode='CONSTANT',
+                         constant_values=0.0)  # (H_p, W_p, 1), [0.0-BG, 1.0-stroke]
+
+        crop_start = window_size // 2
+        pasted_image = pad_img[crop_start: crop_start + image_size, crop_start: crop_start + image_size, :]
+        return pasted_image
+
+    def image_pasting_diff_single(self, fn_inputs):
+        patch_canvas = fn_inputs[:, :, 0:1]  # (raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+        cursor_pos = fn_inputs[0, 0, 1:3]  # (2), in large size
+        image_size = tf.cast(fn_inputs[0, 0, 3], tf.int32)  # ()
+        window_size = tf.cast(fn_inputs[0, 0, 4], tf.int32)  # ()
+        cursor_x, cursor_y = cursor_pos[0], cursor_pos[1]
+
+        patch_canvas_scaled = tf.expand_dims(patch_canvas, axis=0)  # (1, raster_size, raster_size, 1)
+        patch_canvas_scaled = tf.image.resize_images(patch_canvas_scaled, (window_size, window_size),
+                                                     method=tf.image.ResizeMethod.BILINEAR)
+        # patch_canvas_scaled: (1, window_size, window_size, 1)
+
+        valid_canvas = self.image_pasting_diff_batch(patch_canvas_scaled,
+                                                     tf.expand_dims(tf.expand_dims(cursor_pos, axis=0), axis=0),
+                                                     window_size)
+        valid_canvas = tf.squeeze(valid_canvas, axis=0)
+        # (window_size + 1, window_size + 1, 1)
+
+        pad_up = tf.cast(tf.floor(cursor_y), tf.int32)
+        pad_down = image_size - 1 - tf.cast(tf.floor(cursor_y), tf.int32)
+        pad_left = tf.cast(tf.floor(cursor_x), tf.int32)
+        pad_right = image_size - 1 - tf.cast(tf.floor(cursor_x), tf.int32)
+
+        paddings = [[pad_up, pad_down],
+                    [pad_left, pad_right],
+                    [0, 0]]
+        pad_img = tf.pad(valid_canvas, paddings=paddings, mode='CONSTANT',
+                         constant_values=0.0)  # (H_p, W_p, 1), [0.0-BG, 1.0-stroke]
+
+        crop_start = window_size // 2
+        pasted_image = pad_img[crop_start: crop_start + image_size, crop_start: crop_start + image_size, :]
+        return pasted_image
+
+    def image_pasting_diff_single_v3(self, fn_inputs):
+        patch_canvas = fn_inputs[:, :, 0:1]  # (raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+        cursor_pos_a = fn_inputs[0, 0, 1:3]  # (2), float32, in large size
+        image_size_a = tf.cast(fn_inputs[0, 0, 3], tf.int32)  # ()
+        window_size_a = fn_inputs[0, 0, 4]  # (), float32, with grad
+        raster_size_a = float(self.hps.raster_size)
+
+        padding_size = tf.cast(tf.ceil(window_size_a / 2.0), tf.int32)
+
+        x1y1_a = cursor_pos_a - window_size_a / 2.0  # (2), float32
+        x2y2_a = cursor_pos_a + window_size_a / 2.0  # (2), float32
+
+        x1y1_a_floor = tf.floor(x1y1_a)  # (2)
+        x2y2_a_ceil = tf.ceil(x2y2_a)  # (2)
+
+        cursor_pos_b_oricoord = (x1y1_a_floor + x2y2_a_ceil) / 2.0  # (2)
+        cursor_pos_b = (cursor_pos_b_oricoord - x1y1_a) / window_size_a * raster_size_a  # (2)
+        raster_size_b = (x2y2_a_ceil - x1y1_a_floor)  # (x, y)
+        image_size_b = raster_size_a
+        window_size_b = raster_size_a * (raster_size_b / window_size_a)  # (x, y)
+
+        cursor_b_x, cursor_b_y = tf.split(cursor_pos_b, 2, axis=-1)  # (1)
+
+        y1_b = cursor_b_y - (window_size_b[1] - 1.) / 2.
+        x1_b = cursor_b_x - (window_size_b[0] - 1.) / 2.
+        y2_b = y1_b + (window_size_b[1] - 1.)
+        x2_b = x1_b + (window_size_b[0] - 1.)
+        boxes_b = tf.concat([y1_b, x1_b, y2_b, x2_b], axis=-1)  # (4)
+        boxes_b = boxes_b / tf.cast(image_size_b - 1, tf.float32)  # with grad to window_size_a
+
+        box_ind_b = tf.ones((1), dtype=tf.int32)  # (1)
+        box_ind_b = tf.cumsum(box_ind_b) - 1
+
+        patch_canvas = tf.expand_dims(patch_canvas, axis=0)  # (1, raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+        boxes_b = tf.expand_dims(boxes_b, axis=0)  # (1, 4)
+
+        valid_canvas = tf.image.crop_and_resize(patch_canvas, boxes_b, box_ind_b,
+                                                crop_size=[raster_size_b[1], raster_size_b[0]])
+        valid_canvas = valid_canvas[0]  # (raster_size_b, raster_size_b, 1)
+
+        pad_up = tf.cast(x1y1_a_floor[1], tf.int32) + padding_size
+        pad_down = image_size_a + padding_size - tf.cast(x2y2_a_ceil[1], tf.int32)
+        pad_left = tf.cast(x1y1_a_floor[0], tf.int32) + padding_size
+        pad_right = image_size_a + padding_size - tf.cast(x2y2_a_ceil[0], tf.int32)
+
+        paddings = [[pad_up, pad_down],
+                    [pad_left, pad_right],
+                    [0, 0]]
+        pad_img = tf.pad(valid_canvas, paddings=paddings, mode='CONSTANT',
+                         constant_values=0.0)  # (H_p, W_p, 1), [0.0-BG, 1.0-stroke]
+
+        pasted_image = pad_img[padding_size: padding_size + image_size_a, padding_size: padding_size + image_size_a, :]
+        return pasted_image
+
+    def image_pasting_diff_batch(self, patch_image, cursor_position, window_size):
+        """
+        :param patch_img: (N, window_size, window_size, 1), [0.0-BG, 1.0-stroke]
+        :param cursor_position: (N, 1, 2), in large size
+        :return:
+        """
+        paddings1 = [[0, 0],
+                     [1, 1],
+                     [1, 1],
+                     [0, 0]]
+        patch_image_pad1 = tf.pad(patch_image, paddings=paddings1, mode='CONSTANT',
+                                  constant_values=0.0)  # (N, window_size+2, window_size+2, 1), [0.0-BG, 1.0-stroke]
+
+        cursor_x, cursor_y = cursor_position[:, :, 0:1], cursor_position[:, :, 1:2]  # (N, 1, 1)
+        cursor_x_f, cursor_y_f = tf.floor(cursor_x), tf.floor(cursor_y)
+        patch_x, patch_y = 1.0 - (cursor_x - cursor_x_f), 1.0 - (cursor_y - cursor_y_f)  # (N, 1, 1)
+
+        x_ones = tf.ones_like(patch_x, dtype=tf.float32)  # (N, 1, 1)
+        x_ones = tf.tile(x_ones, [1, 1, window_size])  # (N, 1, window_size)
+        patch_x = tf.concat([patch_x, x_ones], axis=-1)  # (N, 1, window_size + 1)
+        patch_x = tf.tile(patch_x, [1, window_size + 1, 1])  # (N, window_size + 1, window_size + 1)
+        patch_x = tf.cumsum(patch_x, axis=-1)  # (N, window_size + 1, window_size + 1)
+        patch_x0 = tf.cast(tf.floor(patch_x), tf.int32)  # (N, window_size + 1, window_size + 1)
+        patch_x1 = patch_x0 + 1  # (N, window_size + 1, window_size + 1)
+
+        y_ones = tf.ones_like(patch_y, dtype=tf.float32)  # (N, 1, 1)
+        y_ones = tf.tile(y_ones, [1, window_size, 1])  # (N, window_size, 1)
+        patch_y = tf.concat([patch_y, y_ones], axis=1)  # (N, window_size + 1, 1)
+        patch_y = tf.tile(patch_y, [1, 1, window_size + 1])  # (N, window_size + 1, window_size + 1)
+        patch_y = tf.cumsum(patch_y, axis=1)  # (N, window_size + 1, window_size + 1)
+        patch_y0 = tf.cast(tf.floor(patch_y), tf.int32)  # (N, window_size + 1, window_size + 1)
+        patch_y1 = patch_y0 + 1  # (N, window_size + 1, window_size + 1)
+
+        # get pixel value at corner coords
+        valid_canvas_patch_a = self.get_pixel_value(patch_image_pad1, patch_x0, patch_y0)
+        valid_canvas_patch_b = self.get_pixel_value(patch_image_pad1, patch_x0, patch_y1)
+        valid_canvas_patch_c = self.get_pixel_value(patch_image_pad1, patch_x1, patch_y0)
+        valid_canvas_patch_d = self.get_pixel_value(patch_image_pad1, patch_x1, patch_y1)
+        # (N, window_size + 1, window_size + 1, 1)
+
+        patch_x0 = tf.cast(patch_x0, tf.float32)
+        patch_x1 = tf.cast(patch_x1, tf.float32)
+        patch_y0 = tf.cast(patch_y0, tf.float32)
+        patch_y1 = tf.cast(patch_y1, tf.float32)
+
+        # calculate deltas
+        wa = (patch_x1 - patch_x) * (patch_y1 - patch_y)
+        wb = (patch_x1 - patch_x) * (patch_y - patch_y0)
+        wc = (patch_x - patch_x0) * (patch_y1 - patch_y)
+        wd = (patch_x - patch_x0) * (patch_y - patch_y0)
+        # (N, window_size + 1, window_size + 1)
+
+        # add dimension for addition
+        wa = tf.expand_dims(wa, axis=3)
+        wb = tf.expand_dims(wb, axis=3)
+        wc = tf.expand_dims(wc, axis=3)
+        wd = tf.expand_dims(wd, axis=3)
+        # (N, window_size + 1, window_size + 1, 1)
+
+        # compute output
+        valid_canvas_patch_ = tf.add_n([wa * valid_canvas_patch_a,
+                                        wb * valid_canvas_patch_b,
+                                        wc * valid_canvas_patch_c,
+                                        wd * valid_canvas_patch_d])  # (N, window_size + 1, window_size + 1, 1)
+        return valid_canvas_patch_
+
+    def image_pasting(self, cursor_position_norm, patch_img, image_size, window_sizes, is_differentiable=False):
+        """
+        paste the patch_img to padded size based on cursor_position
+        :param cursor_position_norm: (N, 1, 2), float type, in size [0.0, 1.0)
+        :param patch_img: (N, raster_size, raster_size), [0.0-BG, 1.0-stroke]
+        :param window_sizes: (N, 1, 1), float32, with grad
+        :return:
+        """
+        cursor_position = tf.multiply(cursor_position_norm, tf.cast(image_size, tf.float32))  # in large size
+        window_sizes_r = tf.round(window_sizes)  # (N, 1, 1), no grad
+
+        patch_img_ = tf.expand_dims(patch_img, axis=-1)  # (N, raster_size, raster_size, 1)
+        cursor_position_step = tf.reshape(cursor_position, (-1, 1, 1, 2))  # (N, 1, 1, 2)
+        cursor_position_step = tf.tile(cursor_position_step, [1, self.hps.raster_size, self.hps.raster_size,
+                                                              1])  # (N, raster_size, raster_size, 2)
+        image_size_tile = tf.reshape(tf.cast(image_size, tf.float32), (1, 1, 1, 1))  # (N, 1, 1, 1)
+        image_size_tile = tf.tile(image_size_tile, [self.hps.batch_size // self.total_loop, self.hps.raster_size,
+                                                    self.hps.raster_size, 1])
+        window_sizes_tile = tf.reshape(window_sizes_r, (-1, 1, 1, 1))  # (N, 1, 1, 1)
+        window_sizes_tile = tf.tile(window_sizes_tile, [1, self.hps.raster_size, self.hps.raster_size, 1])
+
+        pasting_inputs = tf.concat([patch_img_, cursor_position_step, image_size_tile, window_sizes_tile],
+                                   axis=-1)  # (N, raster_size, raster_size, 5)
+
+        if is_differentiable:
+            curr_paste_imgs = tf.map_fn(self.image_pasting_diff_single, pasting_inputs,
+                                        parallel_iterations=32)  # (N, image_size, image_size, 1)
+        else:
+            curr_paste_imgs = tf.map_fn(self.image_pasting_nondiff_single, pasting_inputs,
+                                        parallel_iterations=32)  # (N, image_size, image_size, 1)
+        curr_paste_imgs = tf.squeeze(curr_paste_imgs, axis=-1)  # (N, image_size, image_size)
+        return curr_paste_imgs
+
+    def image_pasting_v3(self, cursor_position_norm, patch_img, image_size, window_sizes, is_differentiable=False):
+        """
+        paste the patch_img to padded size based on cursor_position
+        :param cursor_position_norm: (N, 1, 2), float type, in size [0.0, 1.0)
+        :param patch_img: (N, raster_size, raster_size), [0.0-BG, 1.0-stroke]
+        :param window_sizes: (N, 1, 1), float32, with grad
+        :return:
+        """
+        cursor_position = tf.multiply(cursor_position_norm, tf.cast(image_size, tf.float32))  # in large size
+
+        if is_differentiable:
+            patch_img_ = tf.expand_dims(patch_img, axis=-1)  # (N, raster_size, raster_size, 1)
+            cursor_position_step = tf.reshape(cursor_position, (-1, 1, 1, 2))  # (N, 1, 1, 2)
+            cursor_position_step = tf.tile(cursor_position_step, [1, self.hps.raster_size, self.hps.raster_size,
+                                           1])  # (N, raster_size, raster_size, 2)
+            image_size_tile = tf.reshape(tf.cast(image_size, tf.float32), (1, 1, 1, 1))  # (N, 1, 1, 1)
+            image_size_tile = tf.tile(image_size_tile, [self.hps.batch_size // self.total_loop, self.hps.raster_size,
+                                      self.hps.raster_size, 1])
+            window_sizes_tile = tf.reshape(window_sizes, (-1, 1, 1, 1))  # (N, 1, 1, 1)
+            window_sizes_tile = tf.tile(window_sizes_tile, [1, self.hps.raster_size, self.hps.raster_size, 1])
+
+            pasting_inputs = tf.concat([patch_img_, cursor_position_step, image_size_tile, window_sizes_tile],
+                                       axis=-1)  # (N, raster_size, raster_size, 5)
+            curr_paste_imgs = tf.map_fn(self.image_pasting_diff_single_v3, pasting_inputs,
+                                        parallel_iterations=32)  # (N, image_size, image_size, 1)
+        else:
+            raise Exception('Unfinished...')
+        curr_paste_imgs = tf.squeeze(curr_paste_imgs, axis=-1)  # (N, image_size, image_size)
+        return curr_paste_imgs
+
+    def get_points_and_raster_image(self, initial_state, init_cursor, input_photo, image_size):
+        ## generate the other_params and pen_ras and raster image for raster loss
+        prev_state = initial_state  # (N, dec_rnn_size * 3)
+
+        prev_width = self.init_width  # (1)
+        prev_width = tf.expand_dims(tf.expand_dims(prev_width, axis=0), axis=0)  # (1, 1, 1)
+        prev_width = tf.tile(prev_width, [self.hps.batch_size // self.total_loop, 1, 1])  # (N, 1, 1)
+
+        prev_scaling = tf.ones((self.hps.batch_size // self.total_loop, 1, 1))  # (N, 1, 1)
+        prev_window_size = tf.ones((self.hps.batch_size // self.total_loop, 1, 1),
+                                   dtype=tf.float32) * float(self.hps.raster_size)  # (N, 1, 1)
+
+        cursor_position_temp = init_cursor
+        self.cursor_position = cursor_position_temp  # (N, 1, 2), in size [0.0, 1.0)
+        cursor_position_loop = self.cursor_position
+
+        other_params_list = []
+        pen_ras_list = []
+
+        pos_before_max_min_list = []
+        win_size_before_max_min_list = []
+
+        curr_canvas_soft = tf.zeros_like(input_photo[:, :, :, 0])  # (N, image_size, image_size), [0.0-BG, 1.0-stroke]
+        curr_canvas_soft_rgb = tf.tile(tf.zeros_like(input_photo[:, :, :, 0:1]), [1, 1, 1, 3])  # (N, image_size, image_size, 3), [0.0-BG, 1.0-stroke]
+        curr_canvas_hard = tf.zeros_like(curr_canvas_soft)  # [0.0-BG, 1.0-stroke]
+
+        #### sampling part - start ####
+        self.curr_canvas_hard = curr_canvas_hard
+
+        rasterizor_st = NeuralRasterizorStep(
+            raster_size=self.hps.raster_size,
+            position_format=self.hps.position_format)
+
+        if self.hps.cropping_type == 'v3':
+            cropping_func = self.image_cropping_v3
+        # elif self.hps.cropping_type == 'v2':
+        #     cropping_func = self.image_cropping
+        else:
+            raise Exception('Unknown cropping_type', self.hps.cropping_type)
+
+        if self.hps.pasting_type == 'v3':
+            pasting_func = self.image_pasting_v3
+        # elif self.hps.pasting_type == 'v2':
+        #     pasting_func = self.image_pasting
+        else:
+            raise Exception('Unknown pasting_type', self.hps.pasting_type)
+
+        for time_i in range(self.hps.max_seq_len):
+            cursor_position_non_grad = tf.stop_gradient(cursor_position_loop)  # (N, 1, 2), in size [0.0, 1.0)
+
+            curr_window_size = tf.multiply(prev_scaling, tf.stop_gradient(prev_window_size))  # float, with grad
+            curr_window_size = tf.maximum(curr_window_size, tf.cast(self.hps.min_window_size, tf.float32))
+            curr_window_size = tf.minimum(curr_window_size, tf.cast(image_size, tf.float32))
+
+            ## patch-level encoding
+            # Here, we make the gradients from canvas_z to curr_canvas_hard be None to avoid recurrent gradient propagation.
+            curr_canvas_hard_non_grad = tf.stop_gradient(self.curr_canvas_hard)
+            curr_canvas_hard_non_grad = tf.expand_dims(curr_canvas_hard_non_grad, axis=-1)
+
+            # input_photo: (N, image_size, image_size, 1/3), [0.0-stroke, 1.0-BG]
+            crop_inputs = tf.concat([1.0 - input_photo, curr_canvas_hard_non_grad], axis=-1)  # (N, H_p, W_p, 1/3+1)
+
+            cropped_outputs = cropping_func(cursor_position_non_grad, crop_inputs, image_size, curr_window_size)
+            index_offset = self.hps.input_channel - 1
+            curr_patch_inputs = cropped_outputs[:, :, :, 0:1 + index_offset]  # [0.0-BG, 1.0-stroke]
+            curr_patch_canvas_hard_non_grad = cropped_outputs[:, :, :, 1 + index_offset:2 + index_offset]
+            # (N, raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+
+            curr_patch_inputs = 1.0 - curr_patch_inputs  # [0.0-stroke, 1.0-BG]
+            curr_patch_inputs = self.normalize_image_m1to1(curr_patch_inputs)
+            # (N, raster_size, raster_size, 1/3), [-1.0-stroke, 1.0-BG]
+
+            # Normalizing image
+            curr_patch_canvas_hard_non_grad = 1.0 - curr_patch_canvas_hard_non_grad  # [0.0-stroke, 1.0-BG]
+            curr_patch_canvas_hard_non_grad = self.normalize_image_m1to1(curr_patch_canvas_hard_non_grad)  # [-1.0-stroke, 1.0-BG]
+
+            ## image-level encoding
+            combined_z = self.build_combined_encoder(
+                curr_patch_canvas_hard_non_grad,
+                curr_patch_inputs,
+                1.0 - curr_canvas_hard_non_grad,
+                input_photo,
+                cursor_position_non_grad,
+                image_size,
+                curr_window_size)  # (N, z_size)
+            combined_z = tf.expand_dims(combined_z, axis=1)  # (N, 1, z_size)
+
+            curr_window_size_top_side_norm_non_grad = \
+                tf.stop_gradient(curr_window_size / tf.cast(image_size, tf.float32))
+            curr_window_size_bottom_side_norm_non_grad = \
+                tf.stop_gradient(curr_window_size / tf.cast(self.hps.min_window_size, tf.float32))
+            if not self.hps.concat_win_size:
+                combined_z = tf.concat([tf.stop_gradient(prev_width), combined_z], 2)  # (N, 1, 2+z_size)
+            else:
+                combined_z = tf.concat([tf.stop_gradient(prev_width),
+                                        curr_window_size_top_side_norm_non_grad,
+                                        curr_window_size_bottom_side_norm_non_grad,
+                                        combined_z],
+                                       2)  # (N, 1, 2+z_size)
+
+            if self.hps.concat_cursor:
+                prev_input_x = tf.concat([cursor_position_non_grad, combined_z], 2)  # (N, 1, 2+2+z_size)
+            else:
+                prev_input_x = combined_z  # (N, 1, 2+z_size)
+
+            h_output, next_state = self.build_seq_decoder(self.dec_cell, prev_input_x, prev_state)
+            # h_output: (N * 1, n_out), next_state: (N, dec_rnn_size * 3)
+            [o_other_params, o_pen_ras] = self.get_mixture_coef(h_output)
+            # o_other_params: (N * 1, 6)
+            # o_pen_ras: (N * 1, 2), after softmax
+
+            o_other_params = tf.reshape(o_other_params, [-1, 1, 6])  # (N, 1, 6)
+            o_pen_ras_raw = tf.reshape(o_pen_ras, [-1, 1, 2])  # (N, 1, 2)
+
+            other_params_list.append(o_other_params)
+            pen_ras_list.append(o_pen_ras_raw)
+
+            #### sampling part - end ####
+
+            if self.hps.model_mode == 'train' or self.hps.model_mode == 'eval' or self.hps.model_mode == 'eval_sample':
+                # use renderer here to convert the strokes to image
+                curr_other_params = tf.squeeze(o_other_params, axis=1)  # (N, 6), (x1, y1)=[0.0, 1.0], (x2, y2)=[-1.0, 1.0]
+                x1y1, x2y2, width2, scaling = curr_other_params[:, 0:2], curr_other_params[:, 2:4],\
+                                              curr_other_params[:, 4:5], curr_other_params[:, 5:6]
+                x0y0 = tf.zeros_like(x2y2)  # (N, 2), [-1.0, 1.0]
+                x0y0 = tf.div(tf.add(x0y0, 1.0), 2.0)  # (N, 2), [0.0, 1.0]
+                x2y2 = tf.div(tf.add(x2y2, 1.0), 2.0)  # (N, 2), [0.0, 1.0]
+                widths = tf.concat([tf.squeeze(prev_width, axis=1), width2], axis=1)  # (N, 2)
+                curr_other_params = tf.concat([x0y0, x1y1, x2y2, widths], axis=-1)  # (N, 8), (x0, y0)&(x2, y2)=[0.0, 1.0]
+                curr_stroke_image = rasterizor_st.raster_func_stroke_abs(curr_other_params)
+                # (N, raster_size, raster_size), [0.0-BG, 1.0-stroke]
+
+                curr_stroke_image_large = pasting_func(cursor_position_loop, curr_stroke_image,
+                                                             image_size, curr_window_size,
+                                                             is_differentiable=self.hps.pasting_diff)
+                # (N, image_size, image_size), [0.0-BG, 1.0-stroke]
+
+                ## soft
+                if not self.hps.use_softargmax:
+                    curr_state_soft = o_pen_ras[:, 1:2]  # (N, 1)
+                else:
+                    curr_state_soft = self.differentiable_argmax(o_pen_ras, self.hps.soft_beta)  # (N, 1)
+
+                curr_state_soft = tf.expand_dims(curr_state_soft, axis=1)  # (N, 1, 1)
+
+                filter_curr_stroke_image_soft = tf.multiply(tf.subtract(1.0, curr_state_soft), curr_stroke_image_large)
+                # (N, image_size, image_size), [0.0-BG, 1.0-stroke]
+                curr_canvas_soft = tf.add(curr_canvas_soft, filter_curr_stroke_image_soft)  # [0.0-BG, 1.0-stroke]
+
+                ## hard
+                curr_state_hard = tf.expand_dims(tf.cast(tf.argmax(o_pen_ras_raw, axis=-1), dtype=tf.float32),
+                                                     axis=-1)  # (N, 1, 1)
+                filter_curr_stroke_image_hard = tf.multiply(tf.subtract(1.0, curr_state_hard), curr_stroke_image_large)
+                # (N, image_size, image_size), [0.0-BG, 1.0-stroke]
+                self.curr_canvas_hard = tf.add(self.curr_canvas_hard, filter_curr_stroke_image_hard)  # [0.0-BG, 1.0-stroke]
+                self.curr_canvas_hard = tf.clip_by_value(self.curr_canvas_hard, 0.0, 1.0)  # [0.0-BG, 1.0-stroke]
+
+            next_width = o_other_params[:, :, 4:5]
+            next_scaling = o_other_params[:, :, 5:6]
+            next_window_size = tf.multiply(next_scaling, tf.stop_gradient(curr_window_size))  # float, with grad
+            window_size_before_max_min = next_window_size  # (N, 1, 1), large-level
+            win_size_before_max_min_list.append(window_size_before_max_min)
+            next_window_size = tf.maximum(next_window_size, tf.cast(self.hps.min_window_size, tf.float32))
+            next_window_size = tf.minimum(next_window_size, tf.cast(image_size, tf.float32))
+
+            prev_state = next_state
+            prev_width = next_width * curr_window_size / next_window_size  # (N, 1, 1)
+            prev_scaling = next_scaling  # (N, 1, 1))
+            prev_window_size = curr_window_size
+
+            # update the cursor position
+            new_cursor_offsets = tf.multiply(o_other_params[:, :, 2:4],
+                                             tf.divide(curr_window_size, 2.0))  # (N, 1, 2), window-level
+            new_cursor_offset_next = new_cursor_offsets
+            new_cursor_offset_next = tf.concat([new_cursor_offset_next[:, :, 1:2], new_cursor_offset_next[:, :, 0:1]], axis=-1)
+
+            cursor_position_loop_large = tf.multiply(cursor_position_loop, tf.cast(image_size, tf.float32))
+
+            if self.hps.stop_accu_grad:
+                stroke_position_next = tf.stop_gradient(cursor_position_loop_large) + new_cursor_offset_next  # (N, 1, 2), large-level
+            else:
+                stroke_position_next = cursor_position_loop_large + new_cursor_offset_next  # (N, 1, 2), large-level
+
+            stroke_position_before_max_min = stroke_position_next  # (N, 1, 2), large-level
+            pos_before_max_min_list.append(stroke_position_before_max_min)
+
+            if self.hps.cursor_type == 'next':
+                cursor_position_loop_large = stroke_position_next  # (N, 1, 2), large-level
+            else:
+                raise Exception('Unknown cursor_type')
+
+            cursor_position_loop_large = tf.maximum(cursor_position_loop_large, 0.0)
+            cursor_position_loop_large = tf.minimum(cursor_position_loop_large, tf.cast(image_size - 1, tf.float32))
+            cursor_position_loop = tf.div(cursor_position_loop_large, tf.cast(image_size, tf.float32))
+
+        curr_canvas_soft = tf.clip_by_value(curr_canvas_soft, 0.0, 1.0)  # (N, raster_size, raster_size), [0.0-BG, 1.0-stroke]
+
+        other_params_ = tf.reshape(tf.concat(other_params_list, axis=1), [-1, 6])  # (N * max_seq_len, 6)
+        pen_ras_ = tf.reshape(tf.concat(pen_ras_list, axis=1), [-1, 2])  # (N * max_seq_len, 2)
+        pos_before_max_min_ = tf.concat(pos_before_max_min_list, axis=1)  # (N, max_seq_len, 2)
+        win_size_before_max_min_ = tf.concat(win_size_before_max_min_list, axis=1)  # (N, max_seq_len, 1)
+
+        return other_params_, pen_ras_, prev_state, curr_canvas_soft, curr_canvas_soft_rgb, \
+               pos_before_max_min_, win_size_before_max_min_
+
+    def differentiable_argmax(self, input_pen, soft_beta):
+        """
+        Differentiable argmax trick.
+        :param input_pen: (N, n_class)
+        :return: pen_state: (N, 1)
+        """
+        def sign_onehot(x):
+            """
+            :param x: (N, n_class)
+            :return:  (N, n_class)
+            """
+            y = tf.sign(tf.reduce_max(x, axis=-1, keepdims=True) - x)
+            y = (y - 1) * (-1)
+            return y
+
+        def softargmax(x, beta=1e2):
+            """
+            :param x: (N, n_class)
+            :param beta: 1e10 is the best. 1e2 is acceptable.
+            :return:  (N)
+            """
+            x_range = tf.cumsum(tf.ones_like(x), axis=1)  # (N, 2)
+            return tf.reduce_sum(tf.nn.softmax(x * beta) * x_range, axis=1) - 1
+
+        ## Better to use softargmax(beta=1e2). The sign_onehot's gradient is close to zero.
+        # pen_onehot = sign_onehot(input_pen)  # one-hot form, (N * max_seq_len, 2)
+        # pen_state = pen_onehot[:, 1:2]  # (N * max_seq_len, 1)
+        pen_state = softargmax(input_pen, soft_beta)
+        pen_state = tf.expand_dims(pen_state, axis=1)  # (N * max_seq_len, 1)
+        return pen_state
+
+    def build_losses(self, target_sketch, pred_raster_imgs, pred_params,
+                     pos_before_max_min, win_size_before_max_min, image_size):
+        def get_raster_loss(pred_imgs, gt_imgs, loss_type):
+            perc_layer_losses_raw = []
+            perc_layer_losses_weighted = []
+            perc_layer_losses_norm = []
+
+            if loss_type == 'l1':
+                ras_cost = tf.reduce_mean(tf.abs(tf.subtract(gt_imgs, pred_imgs)))  # ()
+            elif loss_type == 'l1_small':
+                gt_imgs_small = tf.image.resize_images(tf.expand_dims(gt_imgs, axis=3), (32, 32))
+                pred_imgs_small = tf.image.resize_images(tf.expand_dims(pred_imgs, axis=3), (32, 32))
+                ras_cost = tf.reduce_mean(tf.abs(tf.subtract(gt_imgs_small, pred_imgs_small)))  # ()
+            elif loss_type == 'mse':
+                ras_cost = tf.reduce_mean(tf.pow(tf.subtract(gt_imgs, pred_imgs), 2))  # ()
+            elif loss_type == 'perceptual':
+                return_map_pred = vgg_net_slim(pred_imgs, image_size)
+                return_map_gt = vgg_net_slim(gt_imgs, image_size)
+                perc_loss_type = 'l1'  # [l1, mse]
+                weighted_map = {'ReLU1_1': 100.0, 'ReLU1_2': 100.0,
+                                'ReLU2_1': 100.0, 'ReLU2_2': 100.0,
+                                'ReLU3_1': 10.0, 'ReLU3_2': 10.0, 'ReLU3_3': 10.0,
+                                'ReLU4_1': 1.0, 'ReLU4_2': 1.0, 'ReLU4_3': 1.0,
+                                'ReLU5_1': 1.0, 'ReLU5_2': 1.0, 'ReLU5_3': 1.0}
+
+                for perc_layer in self.hps.perc_loss_layers:
+                    if perc_loss_type == 'l1':
+                        perc_layer_loss = tf.reduce_mean(tf.abs(tf.subtract(return_map_pred[perc_layer],
+                                                                            return_map_gt[perc_layer])))  # ()
+                    elif perc_loss_type == 'mse':
+                        perc_layer_loss = tf.reduce_mean(tf.pow(tf.subtract(return_map_pred[perc_layer],
+                                                                            return_map_gt[perc_layer]), 2))  # ()
+                    else:
+                        raise NameError('Unknown perceptual loss type:', perc_loss_type)
+                    perc_layer_losses_raw.append(perc_layer_loss)
+
+                    assert perc_layer in weighted_map
+                    perc_layer_losses_weighted.append(perc_layer_loss * weighted_map[perc_layer])
+
+                for loop_i in range(len(self.hps.perc_loss_layers)):
+                    perc_relu_loss_raw = perc_layer_losses_raw[loop_i]  # ()
+
+                    if self.hps.model_mode == 'train':
+                        curr_relu_mean = (self.perc_loss_mean_list[loop_i] * self.last_step_num + perc_relu_loss_raw) / (self.last_step_num + 1.0)
+                        relu_cost_norm = perc_relu_loss_raw / curr_relu_mean
+                    else:
+                        relu_cost_norm = perc_relu_loss_raw
+                    perc_layer_losses_norm.append(relu_cost_norm)
+
+                perc_layer_losses_raw = tf.stack(perc_layer_losses_raw, axis=0)
+                perc_layer_losses_norm = tf.stack(perc_layer_losses_norm, axis=0)
+
+                if self.hps.perc_loss_fuse_type == 'max':
+                    ras_cost = tf.reduce_max(perc_layer_losses_norm)
+                elif self.hps.perc_loss_fuse_type == 'add':
+                    ras_cost = tf.reduce_mean(perc_layer_losses_norm)
+                elif self.hps.perc_loss_fuse_type == 'raw_add':
+                    ras_cost = tf.reduce_mean(perc_layer_losses_raw)
+                elif self.hps.perc_loss_fuse_type == 'weighted_sum':
+                    ras_cost = tf.reduce_mean(perc_layer_losses_weighted)
+                else:
+                    raise NameError('Unknown perc_loss_fuse_type:', self.hps.perc_loss_fuse_type)
+
+            elif loss_type == 'triplet':
+                raise Exception('Solution for triplet loss is coming soon.')
+            else:
+                raise NameError('Unknown loss type:', loss_type)
+
+            if loss_type != 'perceptual':
+                for perc_layer_i in self.hps.perc_loss_layers:
+                    perc_layer_losses_raw.append(tf.constant(0.0))
+                    perc_layer_losses_norm.append(tf.constant(0.0))
+
+                perc_layer_losses_raw = tf.stack(perc_layer_losses_raw, axis=0)
+                perc_layer_losses_norm = tf.stack(perc_layer_losses_norm, axis=0)
+
+            return ras_cost, perc_layer_losses_raw, perc_layer_losses_norm
+
+        gt_raster_images = tf.squeeze(target_sketch, axis=3)  # (N, raster_h, raster_w), [0.0-stroke, 1.0-BG]
+        raster_cost, perc_relu_losses_raw, perc_relu_losses_norm = \
+            get_raster_loss(pred_raster_imgs, gt_raster_images, loss_type=self.hps.raster_loss_base_type)
+
+        def get_stroke_num_loss(input_strokes):
+            ending_state = input_strokes[:, :, 0]  # (N, seq_len)
+            stroke_num_loss_pre = tf.reduce_mean(ending_state)  # larger is better, [0.0, 1.0]
+            stroke_num_loss = 1.0 - stroke_num_loss_pre  # lower is better, [0.0, 1.0]
+            return stroke_num_loss
+
+        stroke_num_cost = get_stroke_num_loss(pred_params)  # lower is better
+
+        def get_pos_outside_loss(pos_before_max_min_):
+            pos_after_max_min = tf.maximum(pos_before_max_min_, 0.0)
+            pos_after_max_min = tf.minimum(pos_after_max_min, tf.cast(image_size - 1, tf.float32))  # (N, max_seq_len, 2)
+            pos_outside_loss = tf.reduce_mean(tf.abs(pos_before_max_min_ - pos_after_max_min))
+            return pos_outside_loss
+
+        pos_outside_cost = get_pos_outside_loss(pos_before_max_min)  # lower is better
+
+        def get_win_size_outside_loss(win_size_before_max_min_, min_window_size):
+            win_size_outside_top_loss = tf.divide(
+                tf.maximum(win_size_before_max_min_ - tf.cast(image_size, tf.float32), 0.0),
+                tf.cast(image_size, tf.float32))  # (N, max_seq_len, 1)
+            win_size_outside_bottom_loss = tf.divide(
+                tf.maximum(tf.cast(min_window_size, tf.float32) - win_size_before_max_min_, 0.0),
+                tf.cast(min_window_size, tf.float32))  # (N, max_seq_len, 1)
+            win_size_outside_loss = tf.reduce_mean(win_size_outside_top_loss + win_size_outside_bottom_loss)
+            return win_size_outside_loss
+
+        win_size_outside_cost = get_win_size_outside_loss(win_size_before_max_min, self.hps.min_window_size)  # lower is better
+
+        def get_early_pen_states_loss(input_strokes, curr_start, curr_end):
+            # input_strokes: (N, max_seq_len, 7)
+            pred_early_pen_states = input_strokes[:, curr_start:curr_end, 0]  # (N, curr_early_len)
+            pred_early_pen_states_min = tf.reduce_min(pred_early_pen_states, axis=1)  # (N), should not be 1
+            early_pen_states_loss = tf.reduce_mean(pred_early_pen_states_min)  # lower is better
+            return early_pen_states_loss
+
+        early_pen_states_cost = get_early_pen_states_loss(pred_params,
+                                                          self.early_pen_loss_start_idx, self.early_pen_loss_end_idx)
+
+        return raster_cost, stroke_num_cost, pos_outside_cost, win_size_outside_cost, \
+               early_pen_states_cost, \
+               perc_relu_losses_raw, perc_relu_losses_norm
+
+    def build_training_op_split(self, raster_cost, sn_cost, cursor_outside_cost, win_size_outside_cost,
+                                early_pen_states_cost):
+        total_cost = self.hps.raster_loss_weight * raster_cost + \
+                self.hps.early_pen_loss_weight * early_pen_states_cost + \
+                self.stroke_num_loss_weight * sn_cost + \
+                self.hps.outside_loss_weight * cursor_outside_cost + \
+                self.hps.win_size_outside_loss_weight * win_size_outside_cost
+
+        tvars = [var for var in tf.trainable_variables()
+                 if 'raster_unit' not in var.op.name and 'VGG16' not in var.op.name]
+        gvs = self.optimizer.compute_gradients(total_cost, var_list=tvars)
+        return total_cost, gvs
+
+    def build_training_op(self, grad_list):
+        with tf.variable_scope('train_op', reuse=tf.AUTO_REUSE):
+            gvs = self.average_gradients(grad_list)
+            g = self.hps.grad_clip
+
+            for grad, var in gvs:
+                print('>>', var.op.name)
+                if grad is None:
+                    print('  >> None value')
+
+            capped_gvs = [(tf.clip_by_value(grad, -g, g), var) for grad, var in gvs]
+
+            self.train_op = self.optimizer.apply_gradients(
+                capped_gvs, global_step=self.global_step, name='train_step')
+
+    def average_gradients(self, grads_list):
+        """
+        Compute the average gradients.
+        :param grads_list: list(of length N_GPU) of list(grad, var)
+        :return:
+        """
+        avg_grads = []
+        for grad_and_vars in zip(*grads_list):
+            grads = []
+            for g, _ in grad_and_vars:
+                expanded_g = tf.expand_dims(g, 0)
+                grads.append(expanded_g)
+            grad = tf.concat(grads, axis=0)
+            grad = tf.reduce_mean(grad, axis=0)
+
+            v = grad_and_vars[0][1]
+            grad_and_var = (grad, v)
+            avg_grads.append(grad_and_var)
+
+        return avg_grads

outputs/sampling/clean_line_drawings__pretrain_clean_line_drawings/seq_data/muten_0.npz

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+size 52374

outputs/sampling/faces__pretrain_faces/seq_data/1390_0.npz

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+size 6926

outputs/sampling/rough_sketches__pretrain_rough_sketches/seq_data/rocket_0.npz

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