diff --git a/README.md b/README.md index 449c4ea9..46c04ab3 100644 --- a/README.md +++ b/README.md @@ -1 +1,201 @@ -# talkingface-kit \ No newline at end of file +# DaGAN 人脸说话视频生成模型复现 + +本项目复现了CVPR2022论文《Depth-Aware Generative Adversarial Network for Talking Head Video Generation》中的DaGAN模型。该模型通过引入深度感知机制来生成更加自然的人脸说话视频。 + +## 复现效果 + +下面的gif图链接展示的是复现效果的对比动图，从上到下依次是训练300轮的结果（2x4090RTX）、训练365轮的结果（在300轮的基础上使用4x4090RTX）、作者提供的最佳效果的checkpoint的生成结果和原视频，可点击链接或在dagan/assets目录下查看 + +- [gif图链接](https://github.com/nova728/talkingface-kit/blob/main/dagan/assets/result.gif) + +## 复现效果 + +下面的gif图链接展示的是复现效果的对比动图，从上到下依次是训练300轮的结果（2x4090RTX）、训练365轮的结果（在300轮的基础上使用4x4090RTX）、作者提供的最佳效果的checkpoint的生成结果和原视频，可点击链接或在dagan/assets目录下查看 + +- [gif图链接](https://github.com/nova728/talkingface-kit/blob/main/dagan/assets/result.gif) + + +## 环境要求 + +- Python >= 3.7（复现使用环境3.8.10） +- CUDA >= 11.0（复现使用CUDA11.3） +- Docker Engine >= 27.3 +- NVIDIA GPU（复现使用4090RTX） + +## 预训练模型 + +预训练模型下载链接: + +- [人脸深度网络模型](https://hkustconnect-my.sharepoint.com/:f:/g/personal/fhongac_connect_ust_hk/EkxzfH7zbGJNr-WVmPU6fcABWAMq_WJoExAl4SttKK6hBQ?e=fbtGlX) +- [DaGAN生成器模型](https://hkustconnect-my.sharepoint.com/:f:/g/personal/fhongac_connect_ust_hk/EjfeXuzwo3JMn7s0oOPN_q0B81P5Wgu_kbYJAh7uSAKS2w?e=XNZl3K) + +下载后请将模型文件放置在depth/models/weights_19目录下。 + +## Checkpoint下载 + +从以下链接下载： + +- [checkpoints](https://drive.google.com/drive/folders/1jcMVCvl4eYK5P5mCRftnVmk-zzquDpt_?usp=drive_link) + +链接中包含 + +- 00000299-checkpoint.pth.tar（训练300轮得到结果） +- 00000364-checkpoint.pth.tar（训练365轮得到结果） +- DaGAN_vox_adv_256.pth.tar（原作者提供的最优训练结果） + +## Docker使用说明 + +本项目提供了3个Docker镜像: +1. 效果展示镜像(dagan) +2. 评估图像生成镜像(generate-image) +3. 模型评估镜像(evaluation-image) + + + +### 1.生成测试镜像 + +#### 构建镜像 + +``` +docker build -f Dockerfile -t dagan . +``` + +#### 运行镜像 + +```bash +docker run --gpus all ` + -v ${PWD}/input_image:/app/input_image ` + -v ${PWD}/input_video:/app/input_video ` + -v ${PWD}/output_dir:/app/output_dir ` + -v ${PWD}/checkpoints:/app/checkpoints ` + dagan ` + --config config/vox-adv-256.yaml ` + --driving_video /app/input_video/driving.mp4 ` + --source_image /app/input_image/src.png ` + --checkpoint /app/checkpoints/00000299-checkpoint.pth.tar ` + --relative ` + --adapt_scale ` + --kp_num 15 ` + --generator DepthAwareGenerator ` + --result_video /app/output_dir/result.mp4 +``` + +#### 参数说明 + +- driving_video：驱动视频的路径 +- source_image：源图像的路径 +- checkpoints：可选checkpoints文件夹下使用哪个checkpoint(需要下载) +- output_dir：生成视频输出路径 + +#### 可能的错误 + +如果输入视频过长或者size不是256 x 256可能会出现中断的情况，但是在云服务器中是可以实现的。 + +### 2. 生成测试数据集 + +#### 数据集下载 + +构建docker镜像之前需要先下载驱动和源图像数据集： + +- [评估使用的数据集](https://drive.google.com/drive/folders/1ZGL0uu-xuju7fACQSp0Us6Mc8aRnInSf?usp=drive_link) + +分享的链接中结构如下： + +```tex +├─generate_299（00000299-checkpoint.pth.tar的交叉验证集生成图像） +├─generate_299_self（00000299-checkpoint.pth.tar的自重演验证集生成图像） +├─generate_364（00000364-checkpoint.pth.tar的交叉验证集生成图像） +├─generate_364_self（00000364-checkpoint.pth.tar的自重演验证集生成图像） +├─generate_form（DaGAN_vox_adv_256的交叉验证集生成图像） +├─generate_form_self（DaGAN_vox_adv_256的自重演验证集生成图像） +├─gt_cross（交叉验证集驱动图像） +├─gt_self（自重演验证集驱动图像） +├─source_cross（交叉验证集源图像） +└─source_self（自重演验证集源图像） +``` + +其中generate为前缀的是已经生成完成的图像集。 + +#### 构建&运行镜像(可选) + +```bash +docker build -f Dockerfile.generate -t generate-image . +``` + +如果直接下载生成图像的文件夹则**不用构建此镜像**，下载好的文件以原名放置到evaluation_set目录下。运行语句如下： + +```bash +docker run --gpus all ` +-v "$(pwd)/config:/app/config" ` +-v "$(pwd)/evaluation_set:/app/evaluation_set" ` +-v "$(pwd)/results:/app/results" ` +-v "$(pwd)/checkpoints:/app/checkpoints" ` +generate-image ` +--config "/app/config/vox-adv-256.yaml" ` +--source_dir "/app/evaluation_set/source_cross" ` +--driving_dir "/app/evaluation_set/gt_cross" ` +--save_folder "/app/results" ` +--generator "DepthAwareGenerator" ` +--checkpoint "/app/checkpoints/00000299-checkpoint.pth.tar" ` +--kp_num 15 +``` + +#### 参数说明: + +- source_dir: 源图像路径 +- driving_dir: 驱动图像路径 +- output: 输出目录路径 +- relative: 使用相对坐标(推荐) +- adapt_scale: 自适应缩放(推荐) +- kp_num: 关键点数量，默认15(使用其他数值报错) + +#### + +### 3. 模型评估 + +#### 构建镜像 + +```bash +docker build -f Dockerfile.evaluate -t evaluation-image . +``` + +#### 运行镜像 + +```bash +docker run --gpus all ` + -v "$(pwd)/evaluation_set:/app/evaluation_set" ` + -v "$(pwd)/evaluation_results:/app/evaluation_result" ` + evaluation-image ` + --base_dir /app/evaluation_set ` + --output_path /app/evaluation_result ` + --device cuda ` + --batch_size 1 ` + --generate_dirs generate_299_self +``` + +#### 参数说明: + +- 必选参数： + + - base_dir: 数据集路径（evaluation_set） + + - output_path: 评估结果输出路径 + + - generate_dirs:使用evaluation_set中的哪个生成图像数据集，请**务必使用上一节中提到的目录结构的原名称**，代码根据名称自动匹配使用交叉验证集还是自重演验证集的驱动、源图像文件夹。 + + > 代码逻辑是后缀为_self匹配到自重演数据集，否则匹配到交叉验证数据集。 + +- 可选参数： + + - save_visuals：单独保存所有源图像、驱动图像和生成图像对。 + - max_samples：选择评估数据集大小（<=2000） + +评估指标包括: +- NIQE (无参考图像质量评估) +- PSNR (峰值信噪比) +- SSIM (结构相似性) +- FID (Fréchet Inception Distance)【fid的运算由于evaluation.py中有误，后来在calculate_fid.py中实现】 +- LSE-C (内容一致性评估) +- LSE-D (驱动一致性评估) + +评估结果将以JSON格式保存在output目录下,并以txt的格式保存最终结果。 diff --git a/dagan/.gitignore b/dagan/.gitignore new file mode 100644 index 00000000..c596314f Binary files /dev/null and b/dagan/.gitignore differ diff --git a/dagan/.idea/.gitignore b/dagan/.idea/.gitignore new file mode 100644 index 00000000..35410cac --- /dev/null +++ b/dagan/.idea/.gitignore @@ -0,0 +1,8 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml +# 基于编辑器的 HTTP 客户端请求 +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/dagan/.idea/dagan.iml b/dagan/.idea/dagan.iml new file mode 100644 index 00000000..4c883db6 --- /dev/null +++ b/dagan/.idea/dagan.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/dagan/.idea/deployment.xml b/dagan/.idea/deployment.xml new file mode 100644 index 00000000..74fc2294 --- /dev/null +++ b/dagan/.idea/deployment.xml @@ -0,0 +1,21 @@ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/dagan/.idea/inspectionProfiles/Project_Default.xml b/dagan/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 00000000..19aedb0b --- /dev/null +++ b/dagan/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,162 @@ + + + + \ No newline at end of file diff --git a/dagan/.idea/inspectionProfiles/profiles_settings.xml b/dagan/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 00000000..105ce2da --- /dev/null +++ b/dagan/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/dagan/.idea/misc.xml b/dagan/.idea/misc.xml new file mode 100644 index 00000000..bd602152 --- /dev/null +++ b/dagan/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/dagan/.idea/modules.xml b/dagan/.idea/modules.xml new file mode 100644 index 00000000..4268173b --- /dev/null +++ b/dagan/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/dagan/.idea/vcs.xml b/dagan/.idea/vcs.xml new file mode 100644 index 00000000..6c0b8635 --- /dev/null +++ b/dagan/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/dagan/Dockerfile b/dagan/Dockerfile new file mode 100644 index 00000000..ae4f787f --- /dev/null +++ b/dagan/Dockerfile @@ -0,0 +1,54 @@ +FROM silverlogic/python3.8:latest + +# 设置环境变量 +ENV PYTHONUNBUFFERED=1 +ENV PYTHONPATH=/app +ENV TORCH_HOME=/app/.torch + +# 使用清华源 +RUN sed -i 's/deb.debian.org/mirrors.tuna.tsinghua.edu.cn/g' /etc/apt/sources.list && \ + pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple/ && \ + pip config set install.trusted-host pypi.tuna.tsinghua.edu.cn + +# 设置工作目录 +WORKDIR /app + +# 复制需要的文件 +COPY requirements.txt /app/ +COPY . /app/ + +# 安装 PyTorch 和相关依赖 +RUN pip install torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html + +# 安装其他关键依赖 +RUN pip install \ + numpy==1.21.6 \ + scipy==1.7.3 \ + scikit-image==0.16.2 \ + opencv-python==4.6.0.66 \ + face-alignment==1.3.4 \ + matplotlib==3.5.1 \ + Pillow==9.1.0 \ + PyYAML==5.4.1 \ + tqdm==4.64.0 \ + imageio==2.17.0 \ + imageio-ffmpeg==0.4.7 \ + networkx==2.6.3 \ + scikit-learn==1.0.2 + +# 安装 requirements.txt 中的依赖 +RUN pip install -r requirements.txt + +# 安装 face-alignment +RUN cd face-alignment && \ + python setup.py install + +# 创建必要的目录 +RUN mkdir -p /app/input_image \ + /app/input_video \ + /app/output_dir \ + /app/checkpoints + +# 设置默认命令 +ENTRYPOINT ["python", "demo.py"] +CMD ["--help"] \ No newline at end of file diff --git a/dagan/Dockerfile.evaluate b/dagan/Dockerfile.evaluate new file mode 100644 index 00000000..1768f33a --- /dev/null +++ b/dagan/Dockerfile.evaluate @@ -0,0 +1,54 @@ +FROM silverlogic/python3.8:latest + +# 设置环境变量 +ENV PYTHONUNBUFFERED=1 +ENV PYTHONPATH=/app +ENV TORCH_HOME=/app/.torch + +# 使用清华源 +RUN sed -i 's/deb.debian.org/mirrors.tuna.tsinghua.edu.cn/g' /etc/apt/sources.list && \ + pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple/ && \ + pip config set install.trusted-host pypi.tuna.tsinghua.edu.cn + +# 设置工作目录 +WORKDIR /app + +# 安装评估所需的核心依赖 +RUN pip install torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html + +# 安装评估必需的依赖包 +RUN pip install \ + numpy==1.21.6 \ + scipy==1.7.3 \ + scikit-image==0.16.2 \ + matplotlib==3.5.1 \ + Pillow==9.1.0 \ + tqdm==4.64.0 \ + lpips==0.1.4 \ + pyyaml==6.0.1 \ + psutil==5.9.5 \ + opencv-python-headless==4.5.5.64 + +# 创建评估相关目录 +RUN mkdir -p /app/evaluation_set \ + /app/evaluation_set/source_cross \ + /app/evaluation_set/source_self \ + /app/evaluation_set/gt_corss \ + /app/evaluation_set/gt_self \ + /app/evaluation_result + +# 复制评估所需的Python文件 +COPY evaluation_metrics.py /app/ +COPY evaluate_docker.py /app/ +COPY evaluation_dataset.py /app/ +COPY modules/ /app/modules/ +COPY sync_batchnorm/ /app/sync_batchnorm/ +COPY depth/ /app/depth/ +COPY evaluation_set/ /app/evaluation_set/ + +# 设置工作目录 +WORKDIR /app + +# 设置默认命令 +ENTRYPOINT ["python", "evaluate_docker.py"] +CMD ["--help"] \ No newline at end of file diff --git a/dagan/Dockerfile.generate b/dagan/Dockerfile.generate new file mode 100644 index 00000000..f473672f --- /dev/null +++ b/dagan/Dockerfile.generate @@ -0,0 +1,60 @@ +FROM silverlogic/python3.8:latest + +# 设置环境变量 +ENV PYTHONUNBUFFERED=1 +ENV PYTHONPATH=/app +ENV TORCH_HOME=/app/.torch + +# 使用清华源 +RUN sed -i 's/deb.debian.org/mirrors.tuna.tsinghua.edu.cn/g' /etc/apt/sources.list && \ + pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple/ && \ + pip config set install.trusted-host pypi.tuna.tsinghua.edu.cn + +# 设置工作目录 +WORKDIR /app + +# 复制需要的文件 +COPY requirements.txt /app/ +COPY entrypoint.sh /app/ +COPY . /app/ + +# 安装 PyTorch 和相关依赖 +RUN pip install torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html + +# 安装其他关键依赖 +RUN pip install \ + numpy==1.21.6 \ + scipy==1.7.3 \ + scikit-image==0.16.2 \ + opencv-python==4.6.0.66 \ + face-alignment==1.3.4 \ + matplotlib==3.5.1 \ + Pillow==9.1.0 \ + PyYAML==5.4.1 \ + tqdm==4.64.0 \ + imageio==2.17.0 \ + imageio-ffmpeg==0.4.7 \ + networkx==2.6.3 \ + scikit-learn==1.0.2 + +# 安装 requirements.txt 中的依赖 +RUN pip install -r requirements.txt + +# 安装 face-alignment +RUN cd face-alignment && \ + python setup.py install + +# 创建必要的目录 +RUN mkdir -p /app/source_images \ + /app/driving_images \ + /app/results \ + /app/configs \ + /app/checkpoints + + +# 设置工作目录 +WORKDIR /app + +# 设置默认命令 +ENTRYPOINT ["python", "generate.py"] +CMD ["--help"] \ No newline at end of file diff --git a/dagan/LICENSE.md b/dagan/LICENSE.md new file mode 100644 index 00000000..5cdc3b28 --- /dev/null +++ b/dagan/LICENSE.md @@ -0,0 +1,185 @@ +## creative commons + +# Attribution-NonCommercial 4.0 International + +Creative Commons 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Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at [creativecommons.org/policies](http://creativecommons.org/policies), Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. +> +> Creative Commons may be contacted at creativecommons.org + +--------------------------- LICENSE FOR Synchronized-BatchNorm-PyTorch -------------------------------- + +MIT License + +Copyright (c) 2018 Jiayuan MAO + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/dagan/README.md b/dagan/README.md new file mode 100644 index 00000000..feb801b5 --- /dev/null +++ b/dagan/README.md @@ -0,0 +1,157 @@ + +## :book: Depth-Aware Generative Adversarial Network for Talking Head Video Generation (CVPR 2022) +

+ :fire: If DaGAN is helpful in your photos/projects, please help to :star: it or recommend it to your friends. Thanks:fire: +

+

+:fire: Seeking for the collaboration and internship opportunities. :fire: +

+ +> [[Paper](https://arxiv.org/abs/2203.06605)]   [[Project Page](https://harlanhong.github.io/publications/dagan.html)]   [[Demo](https://huggingface.co/spaces/HarlanHong/DaGAN)]   [[Poster Video](https://www.youtube.com/watch?v=nahsJNjWzGo&t=1s)]
+ + +> [Fa-Ting Hong](https://harlanhong.github.io), [Longhao Zhang](), [Li Shen](), [Dan Xu](https://www.danxurgb.net)
+> The Hong Kong University of Science and Technology
+> Alibaba Cloud + +### Cartoon Sample +https://user-images.githubusercontent.com/19970321/162151632-0195292f-30b8-4122-8afd-9b1698f1e4fe.mp4 + +### Human Sample +https://user-images.githubusercontent.com/19970321/162151327-f2930231-42e3-40f2-bfca-a88529599f0f.mp4 + +### Voxceleb1 Dataset +

+ +

+ +:triangular_flag_on_post: **Updates** +- :fire::fire::white_check_mark: July 20 2023: Our new talking head work **[MCNet](https://harlanhong.github.io/publications/mcnet.html) was accpted by ICCV2023. There's no need to train a facial depth network, which makes it more convenient for users to test and fine-tune. +- :fire::fire::white_check_mark: July 26, 2022: The normal dataparallel training scripts were released since some researchers informed me they ran into **DistributedDataParallel** problems. Please try to train your own model using this [command](#dataparallel). Also, we deleted the command line "with torch.autograd.set_detect_anomaly(True)" to boost the training speed. +- :fire::fire::white_check_mark: June 26, 2022: The repo of our face depth network is released, please refer to [Face-Depth-Network](https://github.com/harlanhong/Face-Depth-Network) and feel free to email me if you meet any problem. +- :fire::fire::white_check_mark: June 21, 2022: [Digression] I am looking for research intern/research assistant opportunities in European next year. Please contact me If you think I'm qualified for your position. +- :fire::fire::white_check_mark: May 19, 2022: The depth face model (50 layers) trained on Voxceleb2 is released! (The corresponding checkpoint of DaGAN will release soon). Click the [LINK](https://hkustconnect-my.sharepoint.com/:f:/g/personal/fhongac_connect_ust_hk/EkxzfH7zbGJNr-WVmPU6fcABWAMq_WJoExAl4SttKK6hBQ?e=fbtGlX) + +- :fire::fire::white_check_mark: April 25, 2022: Integrated into Huggingface Spaces 🤗 using Gradio. Try out the web demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/HarlanHong/DaGAN) (GPU version will come soon!) +- :fire::fire::white_check_mark: Add **[SPADE model](https://hkustconnect-my.sharepoint.com/:f:/g/personal/fhongac_connect_ust_hk/EjfeXuzwo3JMn7s0oOPN_q0B81P5Wgu_kbYJAh7uSAKS2w?e=XNZl3K)**, which produces **more natural** results. + + +## :wrench: Dependencies and Installation + +- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)) +- [PyTorch >= 1.7](https://pytorch.org/) +- Option: NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads) +- Option: Linux + +### Installation +We now provide a *clean* version of DaGAN, which does not require customized CUDA extensions.
+ +1. Clone repo + + ```bash + git clone https://github.com/harlanhong/CVPR2022-DaGAN.git + cd CVPR2022-DaGAN + ``` + +2. Install dependent packages + + ```bash + pip install -r requirements.txt + + ## Install the Face Alignment lib + cd face-alignment + pip install -r requirements.txt + python setup.py install + ``` +## :zap: Quick Inference + +We take the paper version for an example. More models can be found [here](https://hkustconnect-my.sharepoint.com/:f:/g/personal/fhongac_connect_ust_hk/EjfeXuzwo3JMn7s0oOPN_q0B81P5Wgu_kbYJAh7uSAKS2w?e=KaQcPk). + +### YAML configs +See ```config/vox-adv-256.yaml``` to get description of each parameter. + +### Pre-trained checkpoint +The pre-trained checkpoint of face depth network and our DaGAN checkpoints can be found under following link: [OneDrive](https://hkustconnect-my.sharepoint.com/:f:/g/personal/fhongac_connect_ust_hk/EjfeXuzwo3JMn7s0oOPN_q0B81P5Wgu_kbYJAh7uSAKS2w?e=KaQcPk). + +**Inference!** +To run a demo, download checkpoint and run the following command: + +```bash +CUDA_VISIBLE_DEVICES=0 python demo.py --config config/vox-adv-256.yaml --driving_video path/to/driving --source_image path/to/source_self --checkpoint path/to/checkpoint --relative --adapt_scale --kp_num 15 --generator DepthAwareGenerator +``` +The result will be stored in ```result.mp4```. The driving videos and source images should be cropped before it can be used in our method. To obtain some semi-automatic crop suggestions you can use ```python crop-video.py --inp some_youtube_video.mp4```. It will generate commands for crops using ffmpeg. + + + + +## :computer: Training + + +### Datasets + +1) **VoxCeleb**. Please follow the instruction from https://github.com/AliaksandrSiarohin/video-preprocessing. + +### Train on VoxCeleb +To train a model on specific dataset run: +``` +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_addr="0.0.0.0" --master_port=12348 run.py --config config/vox-adv-256.yaml --name DaGAN --rgbd --batchsize 12 --kp_num 15 --generator DepthAwareGenerator +``` +
Or
+ +``` +CUDA_VISIBLE_DEVICES=0,1,2,3 python run_dataparallel.py --config config/vox-adv-256.yaml --device_ids 0,1,2,3 --name DaGAN_voxceleb2_depth --rgbd --batchsize 48 --kp_num 15 --generator DepthAwareGenerator +``` + + + + +The code will create a folder in the log directory (each run will create a new name-specific directory). +Checkpoints will be saved to this folder. +To check the loss values during training see ```log.txt```. +By default the batch size is tunned to run on 8 GeForce RTX 3090 gpu (You can obtain the best performance after about 150 epochs). You can change the batch size in the train_params in ```.yaml``` file. + + +Also, you can watch the training loss by running the following command: +```bash +tensorboard --logdir log/DaGAN/log +``` +When you kill your process for some reasons in the middle of training, a zombie process may occur, you can kill it using our provided tool: + ```bash +python kill_port.py PORT +``` + +### Training on your own dataset +1) Resize all the videos to the same size e.g 256x256, the videos can be in '.gif', '.mp4' or folder with images. +We recommend the later, for each video make a separate folder with all the frames in '.png' format. This format is loss-less, and it has better i/o performance. + +2) Create a folder ```data/dataset_name``` with 2 subfolders ```train``` and ```test```, put training videos in the ```train``` and testing in the ```test```. + +3) Create a config ```config/dataset_name.yaml```, in dataset_params specify the root dir the ```root_dir: data/dataset_name```. Also adjust the number of epoch in train_params. + + + +## :scroll: Acknowledgement + + Our DaGAN implementation is inspired by [FOMM](https://github.com/AliaksandrSiarohin/first-order-model). We appreciate the authors of [FOMM](https://github.com/AliaksandrSiarohin/first-order-model) for making their codes available to public. + +## :scroll: BibTeX + +``` +@inproceedings{hong2022depth, + title={Depth-Aware Generative Adversarial Network for Talking Head Video Generation}, + author={Hong, Fa-Ting and Zhang, Longhao and Shen, Li and Xu, Dan}, + journal={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2022} + } + +@article{hong2023dagan, + title={DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation}, + author={Hong, Fa-Ting and and Shen, Li and Xu, Dan}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, + year={2023} + } +``` + +### :e-mail: Contact + +If you have any question or collaboration need (research purpose or commercial purpose), please email `fhongac@cse.ust.hk`. diff --git a/dagan/animate.py b/dagan/animate.py new file mode 100644 index 00000000..46672b76 --- /dev/null +++ b/dagan/animate.py @@ -0,0 +1,119 @@ +import os +from tqdm import tqdm + +import torch +from torch.utils.data import DataLoader + +from frames_dataset import PairedDataset +from logger import Logger, Visualizer +import imageio +from scipy.spatial import ConvexHull +import numpy as np +import depth +from sync_batchnorm import DataParallelWithCallback + + +def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, + use_relative_movement=False, use_relative_jacobian=False): + if adapt_movement_scale: + source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume + driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume + adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) + else: + adapt_movement_scale = 1 + + kp_new = {k: v for k, v in kp_driving.items()} + + if use_relative_movement: + kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) + kp_value_diff *= adapt_movement_scale + kp_new['value'] = kp_value_diff + kp_source['value'] + + if use_relative_jacobian: + jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) + kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) + return kp_new + + +def animate(config, generator, kp_detector, checkpoint, log_dir, dataset,opt): + log_dir = os.path.join(log_dir, 'animation') + png_dir = os.path.join(log_dir, 'png') + animate_params = config['animate_params'] + + dataset = PairedDataset(initial_dataset=dataset, number_of_pairs=animate_params['num_pairs']) + dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1) + + if checkpoint is not None: + Logger.load_cpk(checkpoint, generator=generator, kp_detector=kp_detector) + else: + raise AttributeError("Checkpoint should be specified for mode='animate'.") + + if not os.path.exists(log_dir): + os.makedirs(log_dir) + + if not os.path.exists(png_dir): + os.makedirs(png_dir) + + + depth_encoder = depth.ResnetEncoder(18, False).cuda() + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_decoder.eval() + depth_encoder.eval() + generator.eval() + kp_detector.eval() + + for it, x in tqdm(enumerate(dataloader)): + with torch.no_grad(): + predictions = [] + visualizations = [] + + driving_video = x['driving_video'].cuda() + source_frame = x['source_video'][:, :, 0, :, :].cuda() + + outputs = depth_decoder(depth_encoder(source_frame)) + depth_source = outputs[("disp", 0)] + outputs = depth_decoder(depth_encoder(driving_video[:, :, 0])) + depth_driving = outputs[("disp", 0)] + + source = torch.cat((source_frame,depth_source),1) + driving = torch.cat((driving_video[:, :, 0],depth_driving),1) + + kp_source = kp_detector(source) + kp_driving_initial = kp_detector(driving) + + for frame_idx in range(driving_video.shape[2]): + driving_frame = driving_video[:, :, frame_idx].cuda() + outputs = depth_decoder(depth_encoder(driving_frame)) + depth_map = outputs[("disp", 0)] + driving = torch.cat((driving_frame,depth_map),1) + kp_driving = kp_detector(driving) + + kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving, + kp_driving_initial=kp_driving_initial, **animate_params['normalization_params']) + out = generator(source_frame, kp_source=kp_source, kp_driving=kp_norm) + + out['kp_driving'] = kp_driving + out['kp_source'] = kp_source + out['kp_norm'] = kp_norm + + del out['sparse_deformed'] + + predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) + + visualization = Visualizer(**config['visualizer_params']).visualize(source=source_frame, + driving=driving_frame, out=out) + visualization = visualization + visualizations.append(visualization) + + predictions = np.concatenate(predictions, axis=1) + result_name = "-".join([x['driving_name'][0], x['source_name'][0]]) + imageio.imsave(os.path.join(png_dir, result_name + '.png'), (255 * predictions).astype(np.uint8)) + + image_name = result_name + animate_params['format'] + imageio.mimsave(os.path.join(log_dir, image_name), visualizations) + diff --git a/dagan/assets/result-1734448896966-4.gif b/dagan/assets/result-1734448896966-4.gif new file mode 100644 index 00000000..7fa9eccc Binary files /dev/null and b/dagan/assets/result-1734448896966-4.gif differ diff --git a/dagan/assets/result.gif b/dagan/assets/result.gif new file mode 100644 index 00000000..7fa9eccc Binary files /dev/null and b/dagan/assets/result.gif differ diff --git a/dagan/augmentation.py b/dagan/augmentation.py new file mode 100644 index 00000000..94312722 --- /dev/null +++ b/dagan/augmentation.py @@ -0,0 +1,346 @@ +""" +Code from https://github.com/hassony2/torch_videovision +""" + +import numbers + +import random +import numpy as np +import PIL + +from skimage.transform import resize, rotate +# from skimage.util import pad +# import numpy.pad as pad +import torchvision + +import warnings + +from skimage import img_as_ubyte, img_as_float + + +def crop_clip(clip, min_h, min_w, h, w): + if isinstance(clip[0], np.ndarray): + cropped = [img[min_h:min_h + h, min_w:min_w + w, :] for img in clip] + + elif isinstance(clip[0], PIL.Image.Image): + cropped = [ + img.crop((min_w, min_h, min_w + w, min_h + h)) for img in clip + ] + else: + raise TypeError('Expected numpy.ndarray or PIL.Image' + + 'but got list of {0}'.format(type(clip[0]))) + return cropped + + +def pad_clip(clip, h, w): + im_h, im_w = clip[0].shape[:2] + pad_h = (0, 0) if h < im_h else ((h - im_h) // 2, (h - im_h + 1) // 2) + pad_w = (0, 0) if w < im_w else ((w - im_w) // 2, (w - im_w + 1) // 2) + + return np.pad(clip, ((0, 0), pad_h, pad_w, (0, 0)), mode='edge') + + +def resize_clip(clip, size, interpolation='bilinear'): + if isinstance(clip[0], np.ndarray): + if isinstance(size, numbers.Number): + im_h, im_w, im_c = clip[0].shape + # Min spatial dim already matches minimal size + if (im_w <= im_h and im_w == size) or (im_h <= im_w + and im_h == size): + return clip + new_h, new_w = get_resize_sizes(im_h, im_w, size) + size = (new_w, new_h) + else: + size = size[1], size[0] + + scaled = [ + resize(img, size, order=1 if interpolation == 'bilinear' else 0, preserve_range=True, + mode='constant', anti_aliasing=True) for img in clip + ] + elif isinstance(clip[0], PIL.Image.Image): + if isinstance(size, numbers.Number): + im_w, im_h = clip[0].size + # Min spatial dim already matches minimal size + if (im_w <= im_h and im_w == size) or (im_h <= im_w + and im_h == size): + return clip + new_h, new_w = get_resize_sizes(im_h, im_w, size) + size = (new_w, new_h) + else: + size = size[1], size[0] + if interpolation == 'bilinear': + pil_inter = PIL.Image.NEAREST + else: + pil_inter = PIL.Image.BILINEAR + scaled = [img.resize(size, pil_inter) for img in clip] + else: + raise TypeError('Expected numpy.ndarray or PIL.Image' + + 'but got list of {0}'.format(type(clip[0]))) + return scaled + + +def get_resize_sizes(im_h, im_w, size): + if im_w < im_h: + ow = size + oh = int(size * im_h / im_w) + else: + oh = size + ow = int(size * im_w / im_h) + return oh, ow + + +class RandomFlip(object): + def __init__(self, time_flip=False, horizontal_flip=False): + self.time_flip = time_flip + self.horizontal_flip = horizontal_flip + + def __call__(self, clip): + if random.random() < 0.5 and self.time_flip: + return clip[::-1] + if random.random() < 0.5 and self.horizontal_flip: + return [np.fliplr(img) for img in clip] + + return clip + + +class RandomResize(object): + """Resizes a list of (H x W x C) numpy.ndarray to the final size + The larger the original image is, the more times it takes to + interpolate + Args: + interpolation (str): Can be one of 'nearest', 'bilinear' + defaults to nearest + size (tuple): (widht, height) + """ + + def __init__(self, ratio=(3. / 4., 4. / 3.), interpolation='nearest'): + self.ratio = ratio + self.interpolation = interpolation + + def __call__(self, clip): + scaling_factor = random.uniform(self.ratio[0], self.ratio[1]) + + if isinstance(clip[0], np.ndarray): + im_h, im_w, im_c = clip[0].shape + elif isinstance(clip[0], PIL.Image.Image): + im_w, im_h = clip[0].size + + new_w = int(im_w * scaling_factor) + new_h = int(im_h * scaling_factor) + new_size = (new_w, new_h) + resized = resize_clip( + clip, new_size, interpolation=self.interpolation) + + return resized + + +class RandomCrop(object): + """Extract random crop at the same location for a list of videos + Args: + size (sequence or int): Desired output size for the + crop in format (h, w) + """ + + def __init__(self, size): + if isinstance(size, numbers.Number): + size = (size, size) + + self.size = size + + def __call__(self, clip): + """ + Args: + img (PIL.Image or numpy.ndarray): List of videos to be cropped + in format (h, w, c) in numpy.ndarray + Returns: + PIL.Image or numpy.ndarray: Cropped list of videos + """ + h, w = self.size + if isinstance(clip[0], np.ndarray): + im_h, im_w, im_c = clip[0].shape + elif isinstance(clip[0], PIL.Image.Image): + im_w, im_h = clip[0].size + else: + raise TypeError('Expected numpy.ndarray or PIL.Image' + + 'but got list of {0}'.format(type(clip[0]))) + + clip = pad_clip(clip, h, w) + im_h, im_w = clip.shape[1:3] + x1 = 0 if h == im_h else random.randint(0, im_w - w) + y1 = 0 if w == im_w else random.randint(0, im_h - h) + cropped = crop_clip(clip, y1, x1, h, w) + + return cropped + + +class RandomRotation(object): + """Rotate entire clip randomly by a random angle within + given bounds + Args: + degrees (sequence or int): Range of degrees to select from + If degrees is a number instead of sequence like (min, max), + the range of degrees, will be (-degrees, +degrees). + """ + + def __init__(self, degrees): + if isinstance(degrees, numbers.Number): + if degrees < 0: + raise ValueError('If degrees is a single number,' + 'must be positive') + degrees = (-degrees, degrees) + else: + if len(degrees) != 2: + raise ValueError('If degrees is a sequence,' + 'it must be of len 2.') + + self.degrees = degrees + + def __call__(self, clip): + """ + Args: + img (PIL.Image or numpy.ndarray): List of videos to be cropped + in format (h, w, c) in numpy.ndarray + Returns: + PIL.Image or numpy.ndarray: Cropped list of videos + """ + angle = random.uniform(self.degrees[0], self.degrees[1]) + if isinstance(clip[0], np.ndarray): + rotated = [rotate(image=img, angle=angle, preserve_range=True) for img in clip] + elif isinstance(clip[0], PIL.Image.Image): + rotated = [img.rotate(angle) for img in clip] + else: + raise TypeError('Expected numpy.ndarray or PIL.Image' + + 'but got list of {0}'.format(type(clip[0]))) + + return rotated + + +class ColorJitter(object): + """Randomly change the brightness, contrast and saturation and hue of the clip + Args: + brightness (float): How much to jitter brightness. brightness_factor + is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]. + contrast (float): How much to jitter contrast. contrast_factor + is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]. + saturation (float): How much to jitter saturation. saturation_factor + is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]. + hue(float): How much to jitter hue. hue_factor is chosen uniformly from + [-hue, hue]. Should be >=0 and <= 0.5. + """ + + def __init__(self, brightness=0, contrast=0, saturation=0, hue=0): + self.brightness = brightness + self.contrast = contrast + self.saturation = saturation + self.hue = hue + + def get_params(self, brightness, contrast, saturation, hue): + if brightness > 0: + brightness_factor = random.uniform( + max(0, 1 - brightness), 1 + brightness) + else: + brightness_factor = None + + if contrast > 0: + contrast_factor = random.uniform( + max(0, 1 - contrast), 1 + contrast) + else: + contrast_factor = None + + if saturation > 0: + saturation_factor = random.uniform( + max(0, 1 - saturation), 1 + saturation) + else: + saturation_factor = None + + if hue > 0: + hue_factor = random.uniform(-hue, hue) + else: + hue_factor = None + return brightness_factor, contrast_factor, saturation_factor, hue_factor + + def __call__(self, clip): + """ + Args: + clip (list): list of PIL.Image + Returns: + list PIL.Image : list of transformed PIL.Image + """ + if isinstance(clip[0], np.ndarray): + brightness, contrast, saturation, hue = self.get_params( + self.brightness, self.contrast, self.saturation, self.hue) + + # Create img transform function sequence + img_transforms = [] + if brightness is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_brightness(img, brightness)) + if saturation is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_saturation(img, saturation)) + if hue is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_hue(img, hue)) + if contrast is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_contrast(img, contrast)) + random.shuffle(img_transforms) + img_transforms = [img_as_ubyte, torchvision.transforms.ToPILImage()] + img_transforms + [np.array, + img_as_float] + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + jittered_clip = [] + for img in clip: + jittered_img = img + for func in img_transforms: + jittered_img = func(jittered_img) + jittered_clip.append(jittered_img.astype('float32')) + elif isinstance(clip[0], PIL.Image.Image): + brightness, contrast, saturation, hue = self.get_params( + self.brightness, self.contrast, self.saturation, self.hue) + + # Create img transform function sequence + img_transforms = [] + if brightness is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_brightness(img, brightness)) + if saturation is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_saturation(img, saturation)) + if hue is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_hue(img, hue)) + if contrast is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_contrast(img, contrast)) + random.shuffle(img_transforms) + + # Apply to all videos + jittered_clip = [] + for img in clip: + for func in img_transforms: + jittered_img = func(img) + jittered_clip.append(jittered_img) + + else: + raise TypeError('Expected numpy.ndarray or PIL.Image' + + 'but got list of {0}'.format(type(clip[0]))) + return jittered_clip + + +class AllAugmentationTransform: + def __init__(self, resize_param=None, rotation_param=None, flip_param=None, crop_param=None, jitter_param=None): + self.transforms = [] + + if flip_param is not None: + self.transforms.append(RandomFlip(**flip_param)) + + if rotation_param is not None: + self.transforms.append(RandomRotation(**rotation_param)) + + if resize_param is not None: + self.transforms.append(RandomResize(**resize_param)) + + if crop_param is not None: + self.transforms.append(RandomCrop(**crop_param)) + + if jitter_param is not None: + self.transforms.append(ColorJitter(**jitter_param)) + + def __call__(self, clip): + for t in self.transforms: + clip = t(clip) + return clip diff --git a/dagan/calculate_fid.py b/dagan/calculate_fid.py new file mode 100644 index 00000000..45710264 --- /dev/null +++ b/dagan/calculate_fid.py @@ -0,0 +1,11 @@ +from pytorch_fid import fid_score + +# 计算 FID +fid_value = fid_score.calculate_fid_given_paths( + ['/root/autodl-tmp/dagan/evaluation_set/source_cross', '/root/autodl-tmp/dagan/evaluation_set/generate_form'], # 路径列表 + batch_size=50, # 批量大小 + device='cuda', # 使用 GPU + dims=2048 # Inception v3 的特征维度 +) + +print(f"FID: {fid_value}") \ No newline at end of file diff --git a/dagan/check_dataset.py b/dagan/check_dataset.py new file mode 100644 index 00000000..e69de29b diff --git a/dagan/checkdataset.py b/dagan/checkdataset.py new file mode 100644 index 00000000..713d0724 --- /dev/null +++ b/dagan/checkdataset.py @@ -0,0 +1,132 @@ +import os +import cv2 +import numpy as np +from tqdm import tqdm +import torch +from torch.utils.data import DataLoader +import logging + +logging.basicConfig(level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + filename='dataset_check.log', + filemode='w') + +def check_dataset_structure(root_dir): + """检查数据集的基本结构""" + logging.info(f"Checking dataset structure in {root_dir}") + + # 检查train和test文件夹是否存在 + required_dirs = ['train', 'test'] + for dir_name in required_dirs: + dir_path = os.path.join(root_dir, dir_name) + if not os.path.exists(dir_path): + logging.error(f"Missing required directory: {dir_path}") + return False + + return True + +def check_video_frames(video_dir): + """检查视频帧的完整性""" + try: + frames = sorted(os.listdir(video_dir)) + if len(frames) == 0: + logging.error(f"Empty directory: {video_dir}") + return False + + # 检查每一帧 + for frame in frames: + frame_path = os.path.join(video_dir, frame) + if not frame.lower().endswith(('.png', '.jpg', '.jpeg')): + logging.error(f"Invalid frame format: {frame_path}") + return False + + # 尝试读取图像 + img = cv2.imread(frame_path) + if img is None: + logging.error(f"Cannot read frame: {frame_path}") + return False + + # 检查图像尺寸 + if img.shape[:2] != (256, 256): # 假设需要256x256 + logging.warning(f"Incorrect image size in {frame_path}: {img.shape}") + + return True + except Exception as e: + logging.error(f"Error checking {video_dir}: {str(e)}") + return False + +def check_dataset_samples(root_dir): + """检查所有样本""" + logging.info("Starting dataset samples check...") + + total_videos = 0 + corrupted_videos = [] + + for split in ['train', 'test']: + split_dir = os.path.join(root_dir, split) + videos = os.listdir(split_dir) + + logging.info(f"Checking {split} split: {len(videos)} videos") + + for video in tqdm(videos, desc=f"Checking {split}"): + video_dir = os.path.join(split_dir, video) + if os.path.isdir(video_dir): + total_videos += 1 + if not check_video_frames(video_dir): + corrupted_videos.append(video_dir) + + logging.info(f"Dataset check completed:") + logging.info(f"Total videos: {total_videos}") + logging.info(f"Corrupted videos: {len(corrupted_videos)}") + + if corrupted_videos: + logging.info("Corrupted videos list:") + for video in corrupted_videos: + logging.info(video) + + return corrupted_videos + +def check_dataloader(dataset_path, batch_size=4, num_workers=2): + """测试数据加载器""" + try: + from frames_dataset import FramesDataset # 导入你的数据集类 + + logging.info("Testing DataLoader...") + + dataset = FramesDataset(dataset_path) # 可能需要添加其他参数 + dataloader = DataLoader(dataset, + batch_size=batch_size, + shuffle=True, + num_workers=num_workers) + + # 测试几个batch的加载 + for i, batch in enumerate(tqdm(dataloader, desc="Testing DataLoader")): + if i >= 10: # 测试10个batch + break + + logging.info("DataLoader test completed successfully") + return True + except Exception as e: + logging.error(f"DataLoader test failed: {str(e)}") + return False + +if __name__ == "__main__": + dataset_root = "root/autodl-tmp/vox-png" # 修改为你的数据集路径 + + # 1. 检查数据集结构 + if not check_dataset_structure(dataset_root): + logging.error("Dataset structure check failed!") + exit(1) + + # 2. 检查样本完整性 + corrupted_videos = check_dataset_samples(dataset_root) + + # 3. 测试数据加载器 + if not check_dataloader(dataset_root): + logging.error("DataLoader test failed!") + exit(1) + + if not corrupted_videos: + logging.info("All checks passed successfully!") + else: + logging.warning("Some videos are corrupted. Check the log file for details.") \ No newline at end of file diff --git a/dagan/checkpoints/log.txt b/dagan/checkpoints/log.txt new file mode 100644 index 00000000..e7397f7d --- /dev/null +++ b/dagan/checkpoints/log.txt @@ -0,0 +1,876 @@ +00000000) perceptual - 135.45325; gen_gan - 0.35222; feature_matching - 3.50610; equivariance_value - 0.38566; equivariance_jacobian - 0.71901; kp_distance - 4.03942; disc_gan - 0.46968 +00000000) perceptual - 135.31346; gen_gan - 0.35533; feature_matching - 3.49987; equivariance_value - 0.38870; equivariance_jacobian - 0.72128; kp_distance - 4.06377; disc_gan - 0.46827 +00000000) perceptual - 146.35619; gen_gan - 0.45100; feature_matching - 4.58783; equivariance_value - 0.58141; equivariance_jacobian - 0.89409; kp_distance - 8.30365; disc_gan - 0.38303 +00000000) perceptual - 145.30005; gen_gan - 0.45148; feature_matching - 4.55097; equivariance_value - 0.57344; equivariance_jacobian - 0.89123; kp_distance - 8.27100; disc_gan - 0.38327 +00000001) perceptual - 131.26965; gen_gan - 0.50305; feature_matching - 4.54689; equivariance_value - 0.48248; equivariance_jacobian - 0.81443; kp_distance - 6.12471; disc_gan - 0.33157 +00000001) perceptual - 131.05579; gen_gan - 0.50015; feature_matching - 4.53824; equivariance_value - 0.48259; equivariance_jacobian - 0.81480; kp_distance - 6.13069; disc_gan - 0.33353 +00000002) perceptual - 125.94286; gen_gan - 0.53367; feature_matching - 4.91300; equivariance_value - 0.44985; equivariance_jacobian - 0.76993; kp_distance - 5.03567; disc_gan - 0.30449 +00000002) perceptual - 126.24563; gen_gan - 0.53885; feature_matching - 4.91350; equivariance_value - 0.45749; equivariance_jacobian - 0.77747; kp_distance - 5.01185; disc_gan - 0.30356 +00000003) perceptual - 123.27035; gen_gan - 0.52181; feature_matching - 4.81185; equivariance_value - 0.38170; equivariance_jacobian - 0.74154; kp_distance - 5.27068; disc_gan - 0.31445 +00000003) perceptual - 122.63519; gen_gan - 0.52226; feature_matching - 4.79986; equivariance_value - 0.38359; equivariance_jacobian - 0.73994; kp_distance - 5.28012; disc_gan - 0.31281 +00000004) perceptual - 117.21639; gen_gan - 0.53075; feature_matching - 4.83816; equivariance_value - 0.38138; equivariance_jacobian - 0.71151; kp_distance - 3.65349; disc_gan - 0.31037 +00000004) perceptual - 116.88112; gen_gan - 0.52905; feature_matching - 4.82750; equivariance_value - 0.38103; equivariance_jacobian - 0.71339; kp_distance - 3.66166; disc_gan - 0.30873 +00000005) perceptual - 114.13416; gen_gan - 0.47149; feature_matching - 4.17294; equivariance_value - 0.35946; equivariance_jacobian - 0.69155; kp_distance - 2.96015; disc_gan - 0.35983 +00000005) perceptual - 113.77738; gen_gan - 0.46792; feature_matching - 4.15145; equivariance_value - 0.35967; equivariance_jacobian - 0.69025; kp_distance - 2.96687; disc_gan - 0.35987 +00000006) perceptual - 113.20919; gen_gan - 0.50095; feature_matching - 4.45237; equivariance_value - 0.37138; equivariance_jacobian - 0.69936; kp_distance - 2.87565; disc_gan - 0.32299 +00000006) perceptual - 113.16264; gen_gan - 0.50458; feature_matching - 4.45837; equivariance_value - 0.37023; equivariance_jacobian - 0.70784; kp_distance - 2.86772; disc_gan - 0.32199 +00000007) perceptual - 111.85662; gen_gan - 0.54465; feature_matching - 4.89524; equivariance_value - 0.37537; equivariance_jacobian - 0.70010; kp_distance - 3.74911; disc_gan - 0.29975 +00000007) perceptual - 112.05468; gen_gan - 0.54214; feature_matching - 4.90065; equivariance_value - 0.37387; equivariance_jacobian - 0.69623; kp_distance - 3.74028; disc_gan - 0.30087 +00000008) perceptual - 110.54012; gen_gan - 0.49875; feature_matching - 4.43975; equivariance_value - 0.34838; equivariance_jacobian - 0.67108; kp_distance - 3.75837; disc_gan - 0.33836 +00000008) perceptual - 110.44120; gen_gan - 0.49869; feature_matching - 4.44203; equivariance_value - 0.34590; equivariance_jacobian - 0.67243; kp_distance - 3.76013; disc_gan - 0.33631 +00000009) perceptual - 108.07990; gen_gan - 0.53937; feature_matching - 4.71837; equivariance_value - 0.41879; equivariance_jacobian - 0.65545; kp_distance - 3.93892; disc_gan - 0.29817 +00000009) perceptual - 108.51338; gen_gan - 0.53947; feature_matching - 4.73802; equivariance_value - 0.42227; equivariance_jacobian - 0.65470; kp_distance - 3.93414; disc_gan - 0.29841 +00000010) perceptual - 106.16889; gen_gan - 0.53863; feature_matching - 4.74410; equivariance_value - 0.37978; equivariance_jacobian - 0.61724; kp_distance - 4.14487; disc_gan - 0.30225 +00000010) perceptual - 105.99093; gen_gan - 0.53821; feature_matching - 4.74084; equivariance_value - 0.37933; equivariance_jacobian - 0.61558; kp_distance - 4.16435; disc_gan - 0.30517 +00000011) perceptual - 104.39850; gen_gan - 0.54080; feature_matching - 4.71172; equivariance_value - 0.27283; equivariance_jacobian - 0.56841; kp_distance - 4.08339; disc_gan - 0.30205 +00000011) perceptual - 104.94316; gen_gan - 0.54515; feature_matching - 4.73661; equivariance_value - 0.27164; equivariance_jacobian - 0.56498; kp_distance - 4.09246; disc_gan - 0.29695 +00000012) perceptual - 106.48573; gen_gan - 0.56681; feature_matching - 4.86860; equivariance_value - 0.25734; equivariance_jacobian - 0.55813; kp_distance - 4.08082; disc_gan - 0.28567 +00000012) perceptual - 106.30597; gen_gan - 0.56313; feature_matching - 4.86392; equivariance_value - 0.25816; equivariance_jacobian - 0.56063; kp_distance - 4.07442; disc_gan - 0.28335 +00000013) perceptual - 102.44250; gen_gan - 0.55162; feature_matching - 4.71806; equivariance_value - 0.25469; equivariance_jacobian - 0.53525; kp_distance - 4.10091; disc_gan - 0.29277 +00000013) perceptual - 102.59724; gen_gan - 0.55555; feature_matching - 4.72737; equivariance_value - 0.25367; equivariance_jacobian - 0.53452; kp_distance - 4.10133; disc_gan - 0.29169 +00000014) perceptual - 100.72943; gen_gan - 0.55090; feature_matching - 4.67100; equivariance_value - 0.24009; equivariance_jacobian - 0.51953; kp_distance - 4.10540; disc_gan - 0.29334 +00000014) perceptual - 101.20450; gen_gan - 0.55436; feature_matching - 4.68613; equivariance_value - 0.23935; equivariance_jacobian - 0.52120; kp_distance - 4.11540; disc_gan - 0.29142 +00000015) perceptual - 128.87592; gen_gan - 0.52971; feature_matching - 4.98695; equivariance_value - 1.03338; equivariance_jacobian - 1.11311; kp_distance - 3.06219; disc_gan - 0.32256 +00000015) perceptual - 129.35895; gen_gan - 0.52955; feature_matching - 5.00623; equivariance_value - 1.03586; equivariance_jacobian - 1.34561; kp_distance - 3.06633; disc_gan - 0.32316 +00000016) perceptual - 130.05957; gen_gan - 0.65437; feature_matching - 6.06072; equivariance_value - 1.20987; equivariance_jacobian - 1.09387; kp_distance - 2.49750; disc_gan - 0.23166 +00000016) perceptual - 130.40375; gen_gan - 0.65667; feature_matching - 6.07142; equivariance_value - 1.21581; equivariance_jacobian - 1.11901; kp_distance - 2.50535; disc_gan - 0.22989 +00000017) perceptual - 127.53539; gen_gan - 0.65713; feature_matching - 6.06636; equivariance_value - 1.24447; equivariance_jacobian - 0.95929; kp_distance - 2.55362; disc_gan - 0.23302 +00000017) perceptual - 128.02312; gen_gan - 0.66149; feature_matching - 6.09086; equivariance_value - 1.24765; equivariance_jacobian - 0.97049; kp_distance - 2.55746; disc_gan - 0.23179 +00000018) perceptual - 124.46388; gen_gan - 0.65467; feature_matching - 5.93011; equivariance_value - 1.08616; equivariance_jacobian - 0.84692; kp_distance - 2.51573; disc_gan - 0.23395 +00000018) perceptual - 124.31076; gen_gan - 0.65204; feature_matching - 5.91323; equivariance_value - 1.08522; equivariance_jacobian - 0.84514; kp_distance - 2.52279; disc_gan - 0.23515 +00000019) perceptual - 121.03796; gen_gan - 0.64724; feature_matching - 5.76753; equivariance_value - 0.96302; equivariance_jacobian - 0.82639; kp_distance - 2.07546; disc_gan - 0.23765 +00000019) perceptual - 120.30312; gen_gan - 0.64753; feature_matching - 5.73829; equivariance_value - 0.96164; equivariance_jacobian - 0.82543; kp_distance - 2.07566; disc_gan - 0.23743 +00000020) perceptual - 117.94888; gen_gan - 0.63994; feature_matching - 5.61747; equivariance_value - 0.81629; equivariance_jacobian - 0.80276; kp_distance - 1.95911; disc_gan - 0.23916 +00000020) perceptual - 117.21817; gen_gan - 0.64239; feature_matching - 5.59170; equivariance_value - 0.82388; equivariance_jacobian - 0.80425; kp_distance - 1.95957; disc_gan - 0.24163 +00000021) perceptual - 115.12337; gen_gan - 0.63653; feature_matching - 5.47828; equivariance_value - 0.68233; equivariance_jacobian - 0.77934; kp_distance - 2.11074; disc_gan - 0.24148 +00000021) perceptual - 114.93673; gen_gan - 0.63396; feature_matching - 5.47666; equivariance_value - 0.67806; equivariance_jacobian - 0.77786; kp_distance - 2.11695; disc_gan - 0.24473 +00000022) perceptual - 111.36124; gen_gan - 0.48509; feature_matching - 3.88990; equivariance_value - 0.56014; equivariance_jacobian - 0.75619; kp_distance - 2.06676; disc_gan - 0.36198 +00000022) perceptual - 111.55884; gen_gan - 0.48494; feature_matching - 3.90448; equivariance_value - 0.56371; equivariance_jacobian - 0.75721; kp_distance - 2.06365; disc_gan - 0.35893 +00000023) perceptual - 108.12660; gen_gan - 0.58225; feature_matching - 4.88006; equivariance_value - 0.48791; equivariance_jacobian - 0.72478; kp_distance - 1.90038; disc_gan - 0.27018 +00000023) perceptual - 109.24778; gen_gan - 0.58413; feature_matching - 4.91244; equivariance_value - 0.48910; equivariance_jacobian - 0.72769; kp_distance - 1.90501; disc_gan - 0.26890 +00000024) perceptual - 106.23830; gen_gan - 0.60047; feature_matching - 5.04061; equivariance_value - 0.44349; equivariance_jacobian - 0.70249; kp_distance - 1.64574; disc_gan - 0.26557 +00000024) perceptual - 106.63914; gen_gan - 0.59406; feature_matching - 5.05087; equivariance_value - 0.44188; equivariance_jacobian - 0.70063; kp_distance - 1.64649; disc_gan - 0.26350 +00000025) perceptual - 103.95882; gen_gan - 0.58846; feature_matching - 4.91732; equivariance_value - 0.41789; equivariance_jacobian - 0.68374; kp_distance - 1.35683; disc_gan - 0.27068 +00000025) perceptual - 103.68403; gen_gan - 0.58901; feature_matching - 4.91025; equivariance_value - 0.41278; equivariance_jacobian - 0.68111; kp_distance - 1.35686; disc_gan - 0.27103 +00000026) perceptual - 101.84003; gen_gan - 0.58564; feature_matching - 4.82319; equivariance_value - 0.37677; equivariance_jacobian - 0.65226; kp_distance - 1.09428; disc_gan - 0.27438 +00000026) perceptual - 101.56052; gen_gan - 0.58219; feature_matching - 4.81646; equivariance_value - 0.38066; equivariance_jacobian - 0.65505; kp_distance - 1.09520; disc_gan - 0.27292 +00000027) perceptual - 100.81283; gen_gan - 0.58320; feature_matching - 4.77917; equivariance_value - 0.40499; equivariance_jacobian - 0.66544; kp_distance - 1.23898; disc_gan - 0.27553 +00000027) perceptual - 100.80454; gen_gan - 0.58115; feature_matching - 4.77571; equivariance_value - 0.40792; equivariance_jacobian - 0.66570; kp_distance - 1.22874; disc_gan - 0.27377 +00000028) perceptual - 99.53973; gen_gan - 0.58191; feature_matching - 4.70569; equivariance_value - 0.39962; equivariance_jacobian - 0.64952; kp_distance - 1.33527; disc_gan - 0.27560 +00000028) perceptual - 99.73794; gen_gan - 0.58067; feature_matching - 4.70869; equivariance_value - 0.39786; equivariance_jacobian - 0.64919; kp_distance - 1.33480; disc_gan - 0.27577 +00000029) perceptual - 98.25854; gen_gan - 0.57907; feature_matching - 4.65589; equivariance_value - 0.39074; equivariance_jacobian - 0.62817; kp_distance - 1.21511; disc_gan - 0.27791 +00000029) perceptual - 98.71594; gen_gan - 0.58025; feature_matching - 4.67633; equivariance_value - 0.39092; equivariance_jacobian - 0.62726; kp_distance - 1.21157; disc_gan - 0.27458 +00000030) perceptual - 97.47443; gen_gan - 0.58093; feature_matching - 4.63118; equivariance_value - 0.39726; equivariance_jacobian - 0.62279; kp_distance - 1.17481; disc_gan - 0.27543 +00000030) perceptual - 97.55717; gen_gan - 0.58205; feature_matching - 4.63633; equivariance_value - 0.39897; equivariance_jacobian - 0.62201; kp_distance - 1.17480; disc_gan - 0.27544 +00000031) perceptual - 96.86504; gen_gan - 0.57912; feature_matching - 4.59537; equivariance_value - 0.37038; equivariance_jacobian - 0.60718; kp_distance - 1.25625; disc_gan - 0.27773 +00000031) perceptual - 97.20613; gen_gan - 0.58320; feature_matching - 4.61630; equivariance_value - 0.37201; equivariance_jacobian - 0.60908; kp_distance - 1.25659; disc_gan - 0.27438 +00000032) perceptual - 96.29295; gen_gan - 0.57819; feature_matching - 4.57350; equivariance_value - 0.36811; equivariance_jacobian - 0.59690; kp_distance - 1.30660; disc_gan - 0.27728 +00000032) perceptual - 96.53693; gen_gan - 0.58107; feature_matching - 4.58717; equivariance_value - 0.36596; equivariance_jacobian - 0.59195; kp_distance - 1.31130; disc_gan - 0.27550 +00000033) perceptual - 96.52097; gen_gan - 0.58564; feature_matching - 4.56878; equivariance_value - 0.37733; equivariance_jacobian - 0.58653; kp_distance - 1.14848; disc_gan - 0.27279 +00000033) perceptual - 96.26933; gen_gan - 0.58103; feature_matching - 4.55775; equivariance_value - 0.37946; equivariance_jacobian - 0.58731; kp_distance - 1.15204; disc_gan - 0.27609 +00000034) perceptual - 96.02912; gen_gan - 0.58427; feature_matching - 4.55437; equivariance_value - 0.37904; equivariance_jacobian - 0.58329; kp_distance - 1.20847; disc_gan - 0.27346 +00000034) perceptual - 95.99323; gen_gan - 0.58579; feature_matching - 4.55547; equivariance_value - 0.37812; equivariance_jacobian - 0.58383; kp_distance - 1.20862; disc_gan - 0.27302 +00000035) perceptual - 94.92921; gen_gan - 0.58204; feature_matching - 4.51677; equivariance_value - 0.34629; equivariance_jacobian - 0.57323; kp_distance - 1.36466; disc_gan - 0.27466 +00000035) perceptual - 95.03706; gen_gan - 0.58302; feature_matching - 4.52879; equivariance_value - 0.34785; equivariance_jacobian - 0.57723; kp_distance - 1.36319; disc_gan - 0.27433 +00000036) perceptual - 94.37000; gen_gan - 0.58173; feature_matching - 4.49775; equivariance_value - 0.32913; equivariance_jacobian - 0.56018; kp_distance - 1.32486; disc_gan - 0.27205 +00000036) perceptual - 94.47472; gen_gan - 0.58429; feature_matching - 4.50253; equivariance_value - 0.32708; equivariance_jacobian - 0.55817; kp_distance - 1.32741; disc_gan - 0.27666 +00000037) perceptual - 94.70090; gen_gan - 0.58664; feature_matching - 4.52004; equivariance_value - 0.33779; equivariance_jacobian - 0.56365; kp_distance - 1.24630; disc_gan - 0.26881 +00000037) perceptual - 94.70662; gen_gan - 0.58877; feature_matching - 4.51513; equivariance_value - 0.33713; equivariance_jacobian - 0.56200; kp_distance - 1.24377; disc_gan - 0.27308 +00000038) perceptual - 93.50075; gen_gan - 0.58149; feature_matching - 4.47376; equivariance_value - 0.36374; equivariance_jacobian - 0.56202; kp_distance - 1.10804; disc_gan - 0.27448 +00000038) perceptual - 94.40589; gen_gan - 0.58931; feature_matching - 4.50613; equivariance_value - 0.36229; equivariance_jacobian - 0.56014; kp_distance - 1.11135; disc_gan - 0.27090 +00000039) perceptual - 93.81376; gen_gan - 0.58744; feature_matching - 4.48749; equivariance_value - 0.35556; equivariance_jacobian - 0.55769; kp_distance - 1.21149; disc_gan - 0.27076 +00000039) perceptual - 93.35188; gen_gan - 0.58760; feature_matching - 4.47763; equivariance_value - 0.35832; equivariance_jacobian - 0.56011; kp_distance - 1.20878; disc_gan - 0.27166 +00000040) perceptual - 93.37076; gen_gan - 0.58684; feature_matching - 4.47368; equivariance_value - 0.35872; equivariance_jacobian - 0.55197; kp_distance - 1.20001; disc_gan - 0.27039 +00000040) perceptual - 92.57749; gen_gan - 0.58925; feature_matching - 4.44994; equivariance_value - 0.36012; equivariance_jacobian - 0.55543; kp_distance - 1.20399; disc_gan - 0.27191 +00000041) perceptual - 92.71574; gen_gan - 0.59025; feature_matching - 4.45654; equivariance_value - 0.33839; equivariance_jacobian - 0.54156; kp_distance - 1.17754; disc_gan - 0.26998 +00000041) perceptual - 92.67255; gen_gan - 0.58873; feature_matching - 4.45249; equivariance_value - 0.33367; equivariance_jacobian - 0.54052; kp_distance - 1.17647; disc_gan - 0.27028 +00000042) perceptual - 96.49410; gen_gan - 0.61047; feature_matching - 4.62476; equivariance_value - 0.32567; equivariance_jacobian - 0.55248; kp_distance - 1.29823; disc_gan - 0.25378 +00000042) perceptual - 96.62260; gen_gan - 0.61278; feature_matching - 4.62915; equivariance_value - 0.33102; equivariance_jacobian - 0.55638; kp_distance - 1.28738; disc_gan - 0.25624 +00000043) perceptual - 92.37478; gen_gan - 0.59434; feature_matching - 4.47712; equivariance_value - 0.34257; equivariance_jacobian - 0.54119; kp_distance - 1.13113; disc_gan - 0.26688 +00000043) perceptual - 93.04169; gen_gan - 0.59792; feature_matching - 4.49391; equivariance_value - 0.34037; equivariance_jacobian - 0.53877; kp_distance - 1.13388; disc_gan - 0.26552 +00000044) perceptual - 92.18797; gen_gan - 0.59361; feature_matching - 4.46721; equivariance_value - 0.32334; equivariance_jacobian - 0.53322; kp_distance - 1.09392; disc_gan - 0.26621 +00000044) perceptual - 92.00785; gen_gan - 0.59662; feature_matching - 4.47050; equivariance_value - 0.32319; equivariance_jacobian - 0.53302; kp_distance - 1.09478; disc_gan - 0.26636 +00000045) perceptual - 91.75401; gen_gan - 0.59446; feature_matching - 4.46326; equivariance_value - 0.34031; equivariance_jacobian - 0.53560; kp_distance - 1.12372; disc_gan - 0.26623 +00000045) perceptual - 92.14054; gen_gan - 0.60019; feature_matching - 4.48125; equivariance_value - 0.34286; equivariance_jacobian - 0.53760; kp_distance - 1.12892; disc_gan - 0.26329 +00000046) perceptual - 92.04379; gen_gan - 0.60038; feature_matching - 4.49168; equivariance_value - 0.33445; equivariance_jacobian - 0.52889; kp_distance - 1.17078; disc_gan - 0.26302 +00000046) perceptual - 92.16534; gen_gan - 0.59701; feature_matching - 4.48080; equivariance_value - 0.33715; equivariance_jacobian - 0.53051; kp_distance - 1.16903; disc_gan - 0.26576 +00000047) perceptual - 91.74655; gen_gan - 0.59785; feature_matching - 4.48000; equivariance_value - 0.31436; equivariance_jacobian - 0.52583; kp_distance - 1.05441; disc_gan - 0.26397 +00000047) perceptual - 91.54530; gen_gan - 0.60058; feature_matching - 4.47516; equivariance_value - 0.31381; equivariance_jacobian - 0.52446; kp_distance - 1.05168; disc_gan - 0.26273 +00000048) perceptual - 91.42581; gen_gan - 0.59548; feature_matching - 4.46985; equivariance_value - 0.30535; equivariance_jacobian - 0.51541; kp_distance - 1.02372; disc_gan - 0.26524 +00000048) perceptual - 91.31342; gen_gan - 0.59952; feature_matching - 4.46591; equivariance_value - 0.30624; equivariance_jacobian - 0.51685; kp_distance - 1.02838; disc_gan - 0.26371 +00000049) perceptual - 91.09330; gen_gan - 0.59701; feature_matching - 4.45592; equivariance_value - 0.30247; equivariance_jacobian - 0.51650; kp_distance - 1.03745; disc_gan - 0.26370 +00000049) perceptual - 91.00615; gen_gan - 0.59750; feature_matching - 4.45495; equivariance_value - 0.29982; equivariance_jacobian - 0.51412; kp_distance - 1.03704; disc_gan - 0.26637 +00000050) perceptual - 90.41241; gen_gan - 0.59406; feature_matching - 4.44579; equivariance_value - 0.31158; equivariance_jacobian - 0.52123; kp_distance - 0.97381; disc_gan - 0.26708 +00000050) perceptual - 90.83887; gen_gan - 0.59829; feature_matching - 4.45288; equivariance_value - 0.31103; equivariance_jacobian - 0.51917; kp_distance - 0.97963; disc_gan - 0.26368 +00000051) perceptual - 90.83434; gen_gan - 0.43039; feature_matching - 3.10215; equivariance_value - 0.29932; equivariance_jacobian - 0.51532; kp_distance - 1.07631; disc_gan - 0.39231 +00000051) perceptual - 90.83709; gen_gan - 0.42996; feature_matching - 3.09787; equivariance_value - 0.29845; equivariance_jacobian - 0.51593; kp_distance - 1.06942; disc_gan - 0.39305 +00000052) perceptual - 90.37754; gen_gan - 0.60125; feature_matching - 4.44905; equivariance_value - 0.30423; equivariance_jacobian - 0.51486; kp_distance - 1.03710; disc_gan - 0.25977 +00000052) perceptual - 90.48320; gen_gan - 0.60213; feature_matching - 4.44728; equivariance_value - 0.30296; equivariance_jacobian - 0.51053; kp_distance - 1.03465; disc_gan - 0.25902 +00000053) perceptual - 90.26171; gen_gan - 0.59557; feature_matching - 4.45703; equivariance_value - 0.31719; equivariance_jacobian - 0.51751; kp_distance - 1.16707; disc_gan - 0.26346 +00000053) perceptual - 90.44562; gen_gan - 0.60305; feature_matching - 4.46998; equivariance_value - 0.31561; equivariance_jacobian - 0.51584; kp_distance - 1.16565; disc_gan - 0.26101 +00000054) perceptual - 90.18138; gen_gan - 0.59537; feature_matching - 4.43101; equivariance_value - 0.35037; equivariance_jacobian - 0.52195; kp_distance - 1.08213; disc_gan - 0.26417 +00000054) perceptual - 90.07044; gen_gan - 0.59665; feature_matching - 4.42946; equivariance_value - 0.34725; equivariance_jacobian - 0.52015; kp_distance - 1.08093; disc_gan - 0.26558 +00000055) perceptual - 90.43215; gen_gan - 0.59521; feature_matching - 4.43906; equivariance_value - 0.41754; equivariance_jacobian - 0.53248; kp_distance - 1.13155; disc_gan - 0.26691 +00000055) perceptual - 90.56779; gen_gan - 0.59373; feature_matching - 4.45346; equivariance_value - 0.41791; equivariance_jacobian - 0.53082; kp_distance - 1.12990; disc_gan - 0.26507 +00000056) perceptual - 89.87231; gen_gan - 0.59505; feature_matching - 4.42438; equivariance_value - 0.39224; equivariance_jacobian - 0.52268; kp_distance - 1.02149; disc_gan - 0.26552 +00000056) perceptual - 89.81367; gen_gan - 0.59678; feature_matching - 4.42255; equivariance_value - 0.39530; equivariance_jacobian - 0.52427; kp_distance - 1.02471; disc_gan - 0.26558 +00000057) perceptual - 90.15152; gen_gan - 0.59582; feature_matching - 4.42108; equivariance_value - 0.34373; equivariance_jacobian - 0.51007; kp_distance - 1.06994; disc_gan - 0.26431 +00000057) perceptual - 89.82108; gen_gan - 0.59664; feature_matching - 4.40804; equivariance_value - 0.34246; equivariance_jacobian - 0.50886; kp_distance - 1.06721; disc_gan - 0.26546 +00000058) perceptual - 89.49376; gen_gan - 0.59328; feature_matching - 4.39014; equivariance_value - 0.35063; equivariance_jacobian - 0.51137; kp_distance - 1.02702; disc_gan - 0.26667 +00000058) perceptual - 89.43099; gen_gan - 0.59705; feature_matching - 4.39448; equivariance_value - 0.35537; equivariance_jacobian - 0.51386; kp_distance - 1.02484; disc_gan - 0.26569 +00000059) perceptual - 89.23566; gen_gan - 0.59436; feature_matching - 4.38518; equivariance_value - 0.32428; equivariance_jacobian - 0.51120; kp_distance - 1.07953; disc_gan - 0.26839 +00000059) perceptual - 89.27039; gen_gan - 0.59557; feature_matching - 4.38487; equivariance_value - 0.32471; equivariance_jacobian - 0.51128; kp_distance - 1.08504; disc_gan - 0.26474 +00000059) perceptual - 89.19135; gen_gan - 0.59578; feature_matching - 4.39493; equivariance_value - 0.29660; equivariance_jacobian - 0.49977; kp_distance - 1.12380; disc_gan - 0.26399 +00000059) perceptual - 88.95750; gen_gan - 0.59832; feature_matching - 4.39488; equivariance_value - 0.29853; equivariance_jacobian - 0.50241; kp_distance - 1.12688; disc_gan - 0.26401 +00000060) perceptual - 89.25026; gen_gan - 0.59708; feature_matching - 4.38317; equivariance_value - 0.30292; equivariance_jacobian - 0.50174; kp_distance - 1.15515; disc_gan - 0.26398 +00000060) perceptual - 88.90533; gen_gan - 0.59609; feature_matching - 4.37219; equivariance_value - 0.30293; equivariance_jacobian - 0.50213; kp_distance - 1.15545; disc_gan - 0.26620 +00000061) perceptual - 88.54028; gen_gan - 0.59817; feature_matching - 4.35900; equivariance_value - 0.29203; equivariance_jacobian - 0.49847; kp_distance - 1.15130; disc_gan - 0.26421 +00000061) perceptual - 88.63749; gen_gan - 0.59771; feature_matching - 4.36041; equivariance_value - 0.29516; equivariance_jacobian - 0.49774; kp_distance - 1.15290; disc_gan - 0.26500 +00000062) perceptual - 88.45174; gen_gan - 0.59634; feature_matching - 4.35995; equivariance_value - 0.28948; equivariance_jacobian - 0.49584; kp_distance - 1.16771; disc_gan - 0.26521 +00000062) perceptual - 88.62737; gen_gan - 0.59775; feature_matching - 4.36280; equivariance_value - 0.29179; equivariance_jacobian - 0.49480; kp_distance - 1.16730; disc_gan - 0.26272 +00000063) perceptual - 88.68826; gen_gan - 0.60072; feature_matching - 4.35134; equivariance_value - 0.30267; equivariance_jacobian - 0.49656; kp_distance - 1.06902; disc_gan - 0.26498 +00000063) perceptual - 88.36523; gen_gan - 0.59522; feature_matching - 4.34724; equivariance_value - 0.29926; equivariance_jacobian - 0.49610; kp_distance - 1.07171; disc_gan - 0.26428 +00000064) perceptual - 92.27117; gen_gan - 0.61866; feature_matching - 4.47355; equivariance_value - 0.29734; equivariance_jacobian - 0.52353; kp_distance - 1.08870; disc_gan - 0.25225 +00000064) perceptual - 92.66782; gen_gan - 0.61641; feature_matching - 4.48412; equivariance_value - 0.29745; equivariance_jacobian - 0.52390; kp_distance - 1.09084; disc_gan - 0.25073 +00000065) perceptual - 89.07091; gen_gan - 0.60378; feature_matching - 4.36321; equivariance_value - 0.29246; equivariance_jacobian - 0.50764; kp_distance - 1.08668; disc_gan - 0.26106 +00000065) perceptual - 88.67466; gen_gan - 0.59734; feature_matching - 4.34747; equivariance_value - 0.29221; equivariance_jacobian - 0.50660; kp_distance - 1.08943; disc_gan - 0.26263 +00000066) perceptual - 87.56258; gen_gan - 0.59656; feature_matching - 4.31708; equivariance_value - 0.28244; equivariance_jacobian - 0.49074; kp_distance - 1.07066; disc_gan - 0.26394 +00000066) perceptual - 88.11166; gen_gan - 0.59946; feature_matching - 4.33540; equivariance_value - 0.28474; equivariance_jacobian - 0.49155; kp_distance - 1.07476; disc_gan - 0.26350 +00000067) perceptual - 87.34054; gen_gan - 0.59699; feature_matching - 4.31706; equivariance_value - 0.28101; equivariance_jacobian - 0.48724; kp_distance - 1.03407; disc_gan - 0.26212 +00000067) perceptual - 88.04696; gen_gan - 0.60142; feature_matching - 4.33319; equivariance_value - 0.28650; equivariance_jacobian - 0.49140; kp_distance - 1.03561; disc_gan - 0.26342 +00000068) perceptual - 87.93095; gen_gan - 0.59586; feature_matching - 4.32917; equivariance_value - 0.28511; equivariance_jacobian - 0.49099; kp_distance - 1.12130; disc_gan - 0.26441 +00000068) perceptual - 88.16653; gen_gan - 0.60104; feature_matching - 4.33028; equivariance_value - 0.27898; equivariance_jacobian - 0.48779; kp_distance - 1.12454; disc_gan - 0.26366 +00000069) perceptual - 88.96216; gen_gan - 0.60623; feature_matching - 4.38144; equivariance_value - 0.32389; equivariance_jacobian - 0.52490; kp_distance - 1.50271; disc_gan - 0.25866 +00000069) perceptual - 89.06051; gen_gan - 0.60432; feature_matching - 4.37801; equivariance_value - 0.32530; equivariance_jacobian - 0.52449; kp_distance - 1.50034; disc_gan - 0.25940 +00000070) perceptual - 88.73257; gen_gan - 0.60366; feature_matching - 4.35908; equivariance_value - 0.31364; equivariance_jacobian - 0.51221; kp_distance - 1.50874; disc_gan - 0.25955 +00000070) perceptual - 88.48067; gen_gan - 0.60233; feature_matching - 4.34810; equivariance_value - 0.31118; equivariance_jacobian - 0.51154; kp_distance - 1.51801; disc_gan - 0.26128 +00000071) perceptual - 88.13477; gen_gan - 0.60319; feature_matching - 4.34505; equivariance_value - 0.30886; equivariance_jacobian - 0.50951; kp_distance - 1.59730; disc_gan - 0.26156 +00000071) perceptual - 88.40641; gen_gan - 0.60068; feature_matching - 4.34843; equivariance_value - 0.30854; equivariance_jacobian - 0.51010; kp_distance - 1.59847; disc_gan - 0.26109 +00000072) perceptual - 88.00974; gen_gan - 0.60215; feature_matching - 4.34253; equivariance_value - 0.31222; equivariance_jacobian - 0.50918; kp_distance - 1.58909; disc_gan - 0.25981 +00000072) perceptual - 88.47176; gen_gan - 0.60183; feature_matching - 4.34614; equivariance_value - 0.31240; equivariance_jacobian - 0.50942; kp_distance - 1.58959; disc_gan - 0.26108 +00000073) perceptual - 90.50521; gen_gan - 0.61963; feature_matching - 4.41160; equivariance_value - 0.30656; equivariance_jacobian - 0.51543; kp_distance - 1.53384; disc_gan - 0.25216 +00000073) perceptual - 90.02500; gen_gan - 0.61076; feature_matching - 4.39729; equivariance_value - 0.30649; equivariance_jacobian - 0.51592; kp_distance - 1.53513; disc_gan - 0.25347 +00000074) perceptual - 87.89243; gen_gan - 0.60585; feature_matching - 4.33107; equivariance_value - 0.28704; equivariance_jacobian - 0.49510; kp_distance - 1.53951; disc_gan - 0.26040 +00000074) perceptual - 87.86741; gen_gan - 0.60395; feature_matching - 4.33656; equivariance_value - 0.28729; equivariance_jacobian - 0.49270; kp_distance - 1.54228; disc_gan - 0.25811 +00000075) perceptual - 87.87514; gen_gan - 0.60380; feature_matching - 4.33461; equivariance_value - 0.29214; equivariance_jacobian - 0.49744; kp_distance - 1.48570; disc_gan - 0.25783 +00000075) perceptual - 87.89429; gen_gan - 0.60702; feature_matching - 4.33880; equivariance_value - 0.29252; equivariance_jacobian - 0.49614; kp_distance - 1.49653; disc_gan - 0.25983 +00000076) perceptual - 87.62878; gen_gan - 0.60360; feature_matching - 4.32482; equivariance_value - 0.28701; equivariance_jacobian - 0.49347; kp_distance - 1.44981; disc_gan - 0.25944 +00000076) perceptual - 87.82854; gen_gan - 0.60612; feature_matching - 4.32996; equivariance_value - 0.28697; equivariance_jacobian - 0.49281; kp_distance - 1.44918; disc_gan - 0.25905 +00000077) perceptual - 87.69409; gen_gan - 0.60423; feature_matching - 4.32535; equivariance_value - 0.27658; equivariance_jacobian - 0.48699; kp_distance - 1.43075; disc_gan - 0.25708 +00000077) perceptual - 87.92303; gen_gan - 0.60742; feature_matching - 4.32467; equivariance_value - 0.27502; equivariance_jacobian - 0.48642; kp_distance - 1.42931; disc_gan - 0.25899 +00000078) perceptual - 87.87139; gen_gan - 0.60627; feature_matching - 4.33090; equivariance_value - 0.28419; equivariance_jacobian - 0.49113; kp_distance - 1.44259; disc_gan - 0.25686 +00000078) perceptual - 87.31561; gen_gan - 0.60765; feature_matching - 4.31903; equivariance_value - 0.28455; equivariance_jacobian - 0.48882; kp_distance - 1.44119; disc_gan - 0.25912 +00000079) perceptual - 87.45123; gen_gan - 0.60486; feature_matching - 4.32773; equivariance_value - 0.30094; equivariance_jacobian - 0.49626; kp_distance - 1.39111; disc_gan - 0.25841 +00000079) perceptual - 87.59358; gen_gan - 0.60907; feature_matching - 4.33198; equivariance_value - 0.29635; equivariance_jacobian - 0.49401; kp_distance - 1.38250; disc_gan - 0.25740 +00000080) perceptual - 87.49446; gen_gan - 0.60697; feature_matching - 4.32879; equivariance_value - 0.28585; equivariance_jacobian - 0.48787; kp_distance - 1.38994; disc_gan - 0.25824 +00000080) perceptual - 87.42281; gen_gan - 0.60793; feature_matching - 4.32271; equivariance_value - 0.28549; equivariance_jacobian - 0.48667; kp_distance - 1.39512; disc_gan - 0.25562 +00000081) perceptual - 86.94902; gen_gan - 0.60777; feature_matching - 4.30592; equivariance_value - 0.29467; equivariance_jacobian - 0.49102; kp_distance - 1.47587; disc_gan - 0.25833 +00000081) perceptual - 87.34987; gen_gan - 0.60684; feature_matching - 4.32645; equivariance_value - 0.29805; equivariance_jacobian - 0.49407; kp_distance - 1.47245; disc_gan - 0.25753 +00000082) perceptual - 87.02887; gen_gan - 0.54942; feature_matching - 3.76661; equivariance_value - 0.29745; equivariance_jacobian - 0.49000; kp_distance - 1.48364; disc_gan - 0.32312 +00000082) perceptual - 87.05081; gen_gan - 0.55328; feature_matching - 3.77170; equivariance_value - 0.30181; equivariance_jacobian - 0.49219; kp_distance - 1.48744; disc_gan - 0.32244 +00000083) perceptual - 86.72760; gen_gan - 0.36979; feature_matching - 1.93584; equivariance_value - 0.28651; equivariance_jacobian - 0.48291; kp_distance - 1.45374; disc_gan - 0.41858 +00000083) perceptual - 86.57049; gen_gan - 0.36819; feature_matching - 1.93278; equivariance_value - 0.28728; equivariance_jacobian - 0.48617; kp_distance - 1.45296; disc_gan - 0.41853 +00000084) perceptual - 86.56872; gen_gan - 0.45276; feature_matching - 3.02919; equivariance_value - 0.28963; equivariance_jacobian - 0.48931; kp_distance - 1.40775; disc_gan - 0.36528 +00000084) perceptual - 86.44317; gen_gan - 0.45293; feature_matching - 3.02762; equivariance_value - 0.28643; equivariance_jacobian - 0.48662; kp_distance - 1.41396; disc_gan - 0.36533 +00000085) perceptual - 86.63584; gen_gan - 0.61228; feature_matching - 4.29676; equivariance_value - 0.28275; equivariance_jacobian - 0.48678; kp_distance - 1.48190; disc_gan - 0.25153 +00000085) perceptual - 86.37499; gen_gan - 0.61073; feature_matching - 4.29238; equivariance_value - 0.28669; equivariance_jacobian - 0.48953; kp_distance - 1.48380; disc_gan - 0.25291 +00000086) perceptual - 86.49418; gen_gan - 0.61670; feature_matching - 4.36021; equivariance_value - 0.28514; equivariance_jacobian - 0.47873; kp_distance - 1.42211; disc_gan - 0.25074 +00000086) perceptual - 86.51266; gen_gan - 0.61287; feature_matching - 4.35693; equivariance_value - 0.28888; equivariance_jacobian - 0.48292; kp_distance - 1.42113; disc_gan - 0.24991 +00000087) perceptual - 86.69630; gen_gan - 0.61447; feature_matching - 4.36549; equivariance_value - 0.29687; equivariance_jacobian - 0.49190; kp_distance - 1.44335; disc_gan - 0.25195 +00000087) perceptual - 86.05881; gen_gan - 0.61363; feature_matching - 4.35231; equivariance_value - 0.29646; equivariance_jacobian - 0.49148; kp_distance - 1.44912; disc_gan - 0.25044 +00000088) perceptual - 86.91420; gen_gan - 0.61757; feature_matching - 4.36944; equivariance_value - 0.28022; equivariance_jacobian - 0.48290; kp_distance - 1.44735; disc_gan - 0.24971 +00000088) perceptual - 86.54469; gen_gan - 0.61298; feature_matching - 4.35392; equivariance_value - 0.28267; equivariance_jacobian - 0.48455; kp_distance - 1.44978; disc_gan - 0.25251 +00000089) perceptual - 86.36427; gen_gan - 0.61359; feature_matching - 4.34767; equivariance_value - 0.27847; equivariance_jacobian - 0.47943; kp_distance - 1.40905; disc_gan - 0.25253 +00000089) perceptual - 86.28061; gen_gan - 0.61322; feature_matching - 4.34562; equivariance_value - 0.27593; equivariance_jacobian - 0.47849; kp_distance - 1.40873; disc_gan - 0.25230 +00000090) perceptual - 86.23785; gen_gan - 0.61355; feature_matching - 4.34044; equivariance_value - 0.27519; equivariance_jacobian - 0.47925; kp_distance - 1.30831; disc_gan - 0.25286 +00000090) perceptual - 86.44689; gen_gan - 0.61144; feature_matching - 4.34587; equivariance_value - 0.27810; equivariance_jacobian - 0.48310; kp_distance - 1.31957; disc_gan - 0.25511 +00000091) perceptual - 86.07992; gen_gan - 0.61318; feature_matching - 4.33516; equivariance_value - 0.27744; equivariance_jacobian - 0.47545; kp_distance - 1.29430; disc_gan - 0.25171 +00000091) perceptual - 86.08162; gen_gan - 0.61425; feature_matching - 4.33865; equivariance_value - 0.27627; equivariance_jacobian - 0.47691; kp_distance - 1.30310; disc_gan - 0.25430 +00000092) perceptual - 86.21268; gen_gan - 0.61229; feature_matching - 4.33321; equivariance_value - 0.27669; equivariance_jacobian - 0.47242; kp_distance - 1.42561; disc_gan - 0.25413 +00000092) perceptual - 86.25097; gen_gan - 0.61416; feature_matching - 4.33423; equivariance_value - 0.27940; equivariance_jacobian - 0.47457; kp_distance - 1.42299; disc_gan - 0.25287 +00000093) perceptual - 86.29439; gen_gan - 0.61606; feature_matching - 4.33123; equivariance_value - 0.26322; equivariance_jacobian - 0.46644; kp_distance - 1.44833; disc_gan - 0.25133 +00000093) perceptual - 85.91977; gen_gan - 0.61094; feature_matching - 4.32189; equivariance_value - 0.26530; equivariance_jacobian - 0.46900; kp_distance - 1.44948; disc_gan - 0.25544 +00000094) perceptual - 85.64301; gen_gan - 0.61006; feature_matching - 4.31988; equivariance_value - 0.27229; equivariance_jacobian - 0.47438; kp_distance - 1.39900; disc_gan - 0.25382 +00000094) perceptual - 85.92591; gen_gan - 0.61375; feature_matching - 4.32695; equivariance_value - 0.27246; equivariance_jacobian - 0.47463; kp_distance - 1.40611; disc_gan - 0.25466 +00000095) perceptual - 85.75844; gen_gan - 0.61421; feature_matching - 4.31656; equivariance_value - 0.27099; equivariance_jacobian - 0.47310; kp_distance - 1.36908; disc_gan - 0.25288 +00000095) perceptual - 85.37385; gen_gan - 0.61156; feature_matching - 4.30979; equivariance_value - 0.27123; equivariance_jacobian - 0.47328; kp_distance - 1.37290; disc_gan - 0.25501 +00000096) perceptual - 85.81721; gen_gan - 0.61534; feature_matching - 4.31477; equivariance_value - 0.26820; equivariance_jacobian - 0.47135; kp_distance - 1.46052; disc_gan - 0.25365 +00000096) perceptual - 85.53631; gen_gan - 0.60798; feature_matching - 4.29849; equivariance_value - 0.26625; equivariance_jacobian - 0.47103; kp_distance - 1.45885; disc_gan - 0.25621 +00000097) perceptual - 85.43871; gen_gan - 0.61166; feature_matching - 4.31446; equivariance_value - 0.26189; equivariance_jacobian - 0.46710; kp_distance - 1.48924; disc_gan - 0.25441 +00000097) perceptual - 85.41296; gen_gan - 0.61304; feature_matching - 4.31807; equivariance_value - 0.26351; equivariance_jacobian - 0.46969; kp_distance - 1.48078; disc_gan - 0.25264 +00000098) perceptual - 85.52920; gen_gan - 0.61407; feature_matching - 4.32209; equivariance_value - 0.27119; equivariance_jacobian - 0.47207; kp_distance - 1.34595; disc_gan - 0.25305 +00000098) perceptual - 85.38803; gen_gan - 0.61444; feature_matching - 4.31696; equivariance_value - 0.26988; equivariance_jacobian - 0.47138; kp_distance - 1.34424; disc_gan - 0.25206 +00000099) perceptual - 85.35651; gen_gan - 0.61295; feature_matching - 4.31785; equivariance_value - 0.26885; equivariance_jacobian - 0.46761; kp_distance - 1.36897; disc_gan - 0.25344 +00000099) perceptual - 85.11180; gen_gan - 0.61030; feature_matching - 4.30742; equivariance_value - 0.26650; equivariance_jacobian - 0.46602; kp_distance - 1.37201; disc_gan - 0.25420 +00000100) perceptual - 85.24734; gen_gan - 0.61188; feature_matching - 4.31056; equivariance_value - 0.25727; equivariance_jacobian - 0.46335; kp_distance - 1.46406; disc_gan - 0.25431 +00000100) perceptual - 85.11356; gen_gan - 0.61341; feature_matching - 4.31208; equivariance_value - 0.25699; equivariance_jacobian - 0.46694; kp_distance - 1.46096; disc_gan - 0.25445 +00000101) perceptual - 85.58052; gen_gan - 0.61804; feature_matching - 4.32002; equivariance_value - 0.26111; equivariance_jacobian - 0.46845; kp_distance - 1.51218; disc_gan - 0.25065 +00000101) perceptual - 85.39268; gen_gan - 0.61729; feature_matching - 4.32045; equivariance_value - 0.26115; equivariance_jacobian - 0.46777; kp_distance - 1.49942; disc_gan - 0.25016 +00000102) perceptual - 84.86192; gen_gan - 0.61314; feature_matching - 4.30597; equivariance_value - 0.25136; equivariance_jacobian - 0.46058; kp_distance - 1.42194; disc_gan - 0.25161 +00000102) perceptual - 85.32603; gen_gan - 0.61771; feature_matching - 4.31586; equivariance_value - 0.25502; equivariance_jacobian - 0.46042; kp_distance - 1.42594; disc_gan - 0.25277 +00000103) perceptual - 85.05447; gen_gan - 0.61516; feature_matching - 4.31036; equivariance_value - 0.25073; equivariance_jacobian - 0.46061; kp_distance - 1.43999; disc_gan - 0.25373 +00000103) perceptual - 85.14986; gen_gan - 0.61453; feature_matching - 4.32008; equivariance_value - 0.25116; equivariance_jacobian - 0.46124; kp_distance - 1.44135; disc_gan - 0.25171 +00000104) perceptual - 85.23224; gen_gan - 0.61515; feature_matching - 4.30934; equivariance_value - 0.25230; equivariance_jacobian - 0.46092; kp_distance - 1.37601; disc_gan - 0.25117 +00000104) perceptual - 84.36573; gen_gan - 0.61540; feature_matching - 4.29491; equivariance_value - 0.25582; equivariance_jacobian - 0.46489; kp_distance - 1.38622; disc_gan - 0.25321 +00000105) perceptual - 85.19872; gen_gan - 0.61722; feature_matching - 4.32083; equivariance_value - 0.26526; equivariance_jacobian - 0.46379; kp_distance - 1.38260; disc_gan - 0.24985 +00000105) perceptual - 85.11896; gen_gan - 0.61726; feature_matching - 4.31517; equivariance_value - 0.26345; equivariance_jacobian - 0.46179; kp_distance - 1.37874; disc_gan - 0.25111 +00000106) perceptual - 85.04389; gen_gan - 0.61813; feature_matching - 4.31918; equivariance_value - 0.27955; equivariance_jacobian - 0.46435; kp_distance - 1.38499; disc_gan - 0.25214 +00000106) perceptual - 84.88550; gen_gan - 0.61278; feature_matching - 4.31568; equivariance_value - 0.28227; equivariance_jacobian - 0.46552; kp_distance - 1.38796; disc_gan - 0.25237 +00000107) perceptual - 85.49167; gen_gan - 0.62356; feature_matching - 4.33439; equivariance_value - 0.25568; equivariance_jacobian - 0.46244; kp_distance - 1.43568; disc_gan - 0.24576 +00000107) perceptual - 85.56693; gen_gan - 0.62299; feature_matching - 4.33603; equivariance_value - 0.25842; equivariance_jacobian - 0.46373; kp_distance - 1.43241; disc_gan - 0.24900 +00000108) perceptual - 84.84615; gen_gan - 0.62141; feature_matching - 4.31602; equivariance_value - 0.26156; equivariance_jacobian - 0.46346; kp_distance - 1.37606; disc_gan - 0.24924 +00000108) perceptual - 84.96363; gen_gan - 0.61579; feature_matching - 4.31381; equivariance_value - 0.26299; equivariance_jacobian - 0.46516; kp_distance - 1.36222; disc_gan - 0.25135 +00000109) perceptual - 84.86633; gen_gan - 0.62029; feature_matching - 4.31429; equivariance_value - 0.26862; equivariance_jacobian - 0.46372; kp_distance - 1.38246; disc_gan - 0.24967 +00000109) perceptual - 85.09254; gen_gan - 0.62044; feature_matching - 4.31736; equivariance_value - 0.26682; equivariance_jacobian - 0.46256; kp_distance - 1.38215; disc_gan - 0.24943 +00000110) perceptual - 84.26071; gen_gan - 0.61654; feature_matching - 4.30899; equivariance_value - 0.27118; equivariance_jacobian - 0.46381; kp_distance - 1.31120; disc_gan - 0.25036 +00000110) perceptual - 84.46466; gen_gan - 0.61909; feature_matching - 4.32033; equivariance_value - 0.27410; equivariance_jacobian - 0.46486; kp_distance - 1.31271; disc_gan - 0.25007 +00000111) perceptual - 84.60528; gen_gan - 0.61701; feature_matching - 4.31917; equivariance_value - 0.27507; equivariance_jacobian - 0.46415; kp_distance - 1.37203; disc_gan - 0.25067 +00000111) perceptual - 84.45509; gen_gan - 0.61959; feature_matching - 4.31653; equivariance_value - 0.27426; equivariance_jacobian - 0.46223; kp_distance - 1.36953; disc_gan - 0.24870 +00000112) perceptual - 84.21947; gen_gan - 0.61835; feature_matching - 4.31451; equivariance_value - 0.26426; equivariance_jacobian - 0.46353; kp_distance - 1.40931; disc_gan - 0.25185 +00000112) perceptual - 84.39447; gen_gan - 0.61896; feature_matching - 4.31939; equivariance_value - 0.26497; equivariance_jacobian - 0.46401; kp_distance - 1.39691; disc_gan - 0.24872 +00000113) perceptual - 83.93900; gen_gan - 0.61903; feature_matching - 4.30862; equivariance_value - 0.25454; equivariance_jacobian - 0.45726; kp_distance - 1.43691; disc_gan - 0.25083 +00000113) perceptual - 84.38512; gen_gan - 0.61942; feature_matching - 4.32666; equivariance_value - 0.25599; equivariance_jacobian - 0.45748; kp_distance - 1.43710; disc_gan - 0.24841 +00000114) perceptual - 85.55844; gen_gan - 0.40410; feature_matching - 2.40782; equivariance_value - 0.24343; equivariance_jacobian - 0.45266; kp_distance - 1.45910; disc_gan - 0.45132 +00000114) perceptual - 85.94467; gen_gan - 0.39975; feature_matching - 2.41524; equivariance_value - 0.24277; equivariance_jacobian - 0.45224; kp_distance - 1.46223; disc_gan - 0.45027 +00000115) perceptual - 84.19831; gen_gan - 0.52292; feature_matching - 3.58240; equivariance_value - 0.24599; equivariance_jacobian - 0.45128; kp_distance - 1.35002; disc_gan - 0.33259 +00000115) perceptual - 84.47552; gen_gan - 0.51968; feature_matching - 3.58498; equivariance_value - 0.24649; equivariance_jacobian - 0.45491; kp_distance - 1.34567; disc_gan - 0.33036 +00000116) perceptual - 83.81023; gen_gan - 0.50111; feature_matching - 3.61554; equivariance_value - 0.24719; equivariance_jacobian - 0.45671; kp_distance - 1.38305; disc_gan - 0.33080 +00000116) perceptual - 84.20973; gen_gan - 0.49752; feature_matching - 3.62033; equivariance_value - 0.24934; equivariance_jacobian - 0.45800; kp_distance - 1.38053; disc_gan - 0.33316 +00000117) perceptual - 84.31218; gen_gan - 0.62084; feature_matching - 4.32874; equivariance_value - 0.24865; equivariance_jacobian - 0.45694; kp_distance - 1.41580; disc_gan - 0.24401 +00000117) perceptual - 84.11451; gen_gan - 0.61959; feature_matching - 4.33140; equivariance_value - 0.24529; equivariance_jacobian - 0.45397; kp_distance - 1.41306; disc_gan - 0.24476 +00000118) perceptual - 84.10136; gen_gan - 0.62309; feature_matching - 4.35336; equivariance_value - 0.23583; equivariance_jacobian - 0.44836; kp_distance - 1.49614; disc_gan - 0.24423 +00000118) perceptual - 83.81242; gen_gan - 0.62204; feature_matching - 4.34563; equivariance_value - 0.23654; equivariance_jacobian - 0.44794; kp_distance - 1.48992; disc_gan - 0.24431 +00000119) perceptual - 84.16438; gen_gan - 0.62098; feature_matching - 4.35163; equivariance_value - 0.25193; equivariance_jacobian - 0.45908; kp_distance - 1.35727; disc_gan - 0.24531 +00000119) perceptual - 84.32769; gen_gan - 0.62319; feature_matching - 4.34953; equivariance_value - 0.25085; equivariance_jacobian - 0.45779; kp_distance - 1.35971; disc_gan - 0.24392 +00000120) perceptual - 84.27691; gen_gan - 0.62343; feature_matching - 4.35035; equivariance_value - 0.24653; equivariance_jacobian - 0.45806; kp_distance - 1.38269; disc_gan - 0.24475 +00000120) perceptual - 83.89056; gen_gan - 0.62154; feature_matching - 4.34101; equivariance_value - 0.24715; equivariance_jacobian - 0.45643; kp_distance - 1.38060; disc_gan - 0.24626 +00000121) perceptual - 84.02463; gen_gan - 0.62607; feature_matching - 4.34286; equivariance_value - 0.24848; equivariance_jacobian - 0.45620; kp_distance - 1.36291; disc_gan - 0.24528 +00000121) perceptual - 83.79510; gen_gan - 0.62073; feature_matching - 4.33110; equivariance_value - 0.24763; equivariance_jacobian - 0.45529; kp_distance - 1.36304; disc_gan - 0.24609 +00000122) perceptual - 83.81802; gen_gan - 0.62448; feature_matching - 4.33888; equivariance_value - 0.25537; equivariance_jacobian - 0.45700; kp_distance - 1.37227; disc_gan - 0.24717 +00000122) perceptual - 84.17158; gen_gan - 0.62068; feature_matching - 4.33903; equivariance_value - 0.25427; equivariance_jacobian - 0.45741; kp_distance - 1.37563; disc_gan - 0.24641 +00000123) perceptual - 83.95594; gen_gan - 0.62141; feature_matching - 4.33060; equivariance_value - 0.24187; equivariance_jacobian - 0.44999; kp_distance - 1.34842; disc_gan - 0.24744 +00000123) perceptual - 83.73641; gen_gan - 0.62318; feature_matching - 4.32977; equivariance_value - 0.24313; equivariance_jacobian - 0.45016; kp_distance - 1.34816; disc_gan - 0.24582 +00000124) perceptual - 83.47839; gen_gan - 0.62256; feature_matching - 4.32712; equivariance_value - 0.24259; equivariance_jacobian - 0.45069; kp_distance - 1.33878; disc_gan - 0.24718 +00000124) perceptual - 83.83955; gen_gan - 0.62299; feature_matching - 4.33567; equivariance_value - 0.24586; equivariance_jacobian - 0.45301; kp_distance - 1.34222; disc_gan - 0.24399 +00000125) perceptual - 83.73132; gen_gan - 0.62203; feature_matching - 4.32980; equivariance_value - 0.25897; equivariance_jacobian - 0.46130; kp_distance - 1.29039; disc_gan - 0.24660 +00000125) perceptual - 83.64597; gen_gan - 0.62483; feature_matching - 4.32930; equivariance_value - 0.25849; equivariance_jacobian - 0.45865; kp_distance - 1.28982; disc_gan - 0.24680 +00000126) perceptual - 84.12521; gen_gan - 0.62630; feature_matching - 4.33759; equivariance_value - 0.25367; equivariance_jacobian - 0.45634; kp_distance - 1.28239; disc_gan - 0.24299 +00000126) perceptual - 84.13804; gen_gan - 0.62561; feature_matching - 4.34403; equivariance_value - 0.25368; equivariance_jacobian - 0.45442; kp_distance - 1.27988; disc_gan - 0.24422 +00000127) perceptual - 83.66586; gen_gan - 0.62637; feature_matching - 4.33356; equivariance_value - 0.24492; equivariance_jacobian - 0.44922; kp_distance - 1.36237; disc_gan - 0.24474 +00000127) perceptual - 83.03374; gen_gan - 0.62426; feature_matching - 4.32519; equivariance_value - 0.24333; equivariance_jacobian - 0.44802; kp_distance - 1.35814; disc_gan - 0.24591 +00000128) perceptual - 83.63045; gen_gan - 0.62810; feature_matching - 4.32828; equivariance_value - 0.24438; equivariance_jacobian - 0.45283; kp_distance - 1.34153; disc_gan - 0.24405 +00000128) perceptual - 83.39236; gen_gan - 0.62253; feature_matching - 4.32376; equivariance_value - 0.24120; equivariance_jacobian - 0.45055; kp_distance - 1.34424; disc_gan - 0.24519 +00000129) perceptual - 83.39411; gen_gan - 0.62742; feature_matching - 4.33091; equivariance_value - 0.24300; equivariance_jacobian - 0.45119; kp_distance - 1.39926; disc_gan - 0.24543 +00000129) perceptual - 83.58357; gen_gan - 0.62364; feature_matching - 4.33074; equivariance_value - 0.24419; equivariance_jacobian - 0.45151; kp_distance - 1.39691; disc_gan - 0.24439 +00000130) perceptual - 83.51948; gen_gan - 0.62843; feature_matching - 4.32914; equivariance_value - 0.24521; equivariance_jacobian - 0.45170; kp_distance - 1.36650; disc_gan - 0.24363 +00000130) perceptual - 83.40286; gen_gan - 0.62374; feature_matching - 4.32622; equivariance_value - 0.24435; equivariance_jacobian - 0.44943; kp_distance - 1.37491; disc_gan - 0.24508 +00000131) perceptual - 83.56884; gen_gan - 0.62889; feature_matching - 4.33654; equivariance_value - 0.25188; equivariance_jacobian - 0.45501; kp_distance - 1.42644; disc_gan - 0.24274 +00000131) perceptual - 83.70186; gen_gan - 0.62472; feature_matching - 4.33823; equivariance_value - 0.25028; equivariance_jacobian - 0.45335; kp_distance - 1.43067; disc_gan - 0.24475 +00000132) perceptual - 83.43256; gen_gan - 0.62755; feature_matching - 4.33632; equivariance_value - 0.25212; equivariance_jacobian - 0.44991; kp_distance - 1.36212; disc_gan - 0.24348 +00000132) perceptual - 82.88958; gen_gan - 0.62549; feature_matching - 4.31882; equivariance_value - 0.24870; equivariance_jacobian - 0.44765; kp_distance - 1.36460; disc_gan - 0.24463 +00000133) perceptual - 83.08562; gen_gan - 0.62978; feature_matching - 4.33589; equivariance_value - 0.25300; equivariance_jacobian - 0.45165; kp_distance - 1.28828; disc_gan - 0.24386 +00000133) perceptual - 83.27054; gen_gan - 0.62731; feature_matching - 4.34177; equivariance_value - 0.25181; equivariance_jacobian - 0.45111; kp_distance - 1.28927; disc_gan - 0.24242 +00000134) perceptual - 84.73379; gen_gan - 0.60306; feature_matching - 4.13970; equivariance_value - 0.24690; equivariance_jacobian - 0.45525; kp_distance - 1.36518; disc_gan - 0.27822 +00000134) perceptual - 85.04108; gen_gan - 0.60340; feature_matching - 4.15022; equivariance_value - 0.24704; equivariance_jacobian - 0.45411; kp_distance - 1.36639; disc_gan - 0.27862 +00000135) perceptual - 84.60548; gen_gan - 0.47451; feature_matching - 3.23571; equivariance_value - 0.26926; equivariance_jacobian - 0.45543; kp_distance - 1.67771; disc_gan - 0.35414 +00000135) perceptual - 84.71645; gen_gan - 0.47374; feature_matching - 3.24176; equivariance_value - 0.27028; equivariance_jacobian - 0.45456; kp_distance - 1.68181; disc_gan - 0.35404 +00000136) perceptual - 83.62778; gen_gan - 0.62877; feature_matching - 4.34444; equivariance_value - 0.25600; equivariance_jacobian - 0.44658; kp_distance - 1.48392; disc_gan - 0.24026 +00000136) perceptual - 83.10474; gen_gan - 0.62565; feature_matching - 4.33185; equivariance_value - 0.25797; equivariance_jacobian - 0.44964; kp_distance - 1.47381; disc_gan - 0.24290 +00000137) perceptual - 83.54678; gen_gan - 0.62828; feature_matching - 4.34906; equivariance_value - 0.23904; equivariance_jacobian - 0.44362; kp_distance - 1.35320; disc_gan - 0.24248 +00000137) perceptual - 82.83389; gen_gan - 0.62442; feature_matching - 4.32704; equivariance_value - 0.24202; equivariance_jacobian - 0.44720; kp_distance - 1.34019; disc_gan - 0.24226 +00000138) perceptual - 82.73990; gen_gan - 0.62580; feature_matching - 4.32687; equivariance_value - 0.24020; equivariance_jacobian - 0.44759; kp_distance - 1.32238; disc_gan - 0.24261 +00000138) perceptual - 83.61234; gen_gan - 0.62938; feature_matching - 4.35242; equivariance_value - 0.23914; equivariance_jacobian - 0.44693; kp_distance - 1.32273; disc_gan - 0.24018 +00000139) perceptual - 82.96519; gen_gan - 0.62984; feature_matching - 4.33826; equivariance_value - 0.24817; equivariance_jacobian - 0.44976; kp_distance - 1.30322; disc_gan - 0.24215 +00000139) perceptual - 83.32954; gen_gan - 0.62731; feature_matching - 4.33370; equivariance_value - 0.24735; equivariance_jacobian - 0.44690; kp_distance - 1.30364; disc_gan - 0.24289 +00000140) perceptual - 83.37106; gen_gan - 0.63102; feature_matching - 4.34450; equivariance_value - 0.24607; equivariance_jacobian - 0.44639; kp_distance - 1.29327; disc_gan - 0.24106 +00000140) perceptual - 82.86888; gen_gan - 0.62858; feature_matching - 4.33259; equivariance_value - 0.24614; equivariance_jacobian - 0.44872; kp_distance - 1.29303; disc_gan - 0.24053 +00000141) perceptual - 82.82468; gen_gan - 0.62744; feature_matching - 4.33414; equivariance_value - 0.24501; equivariance_jacobian - 0.44485; kp_distance - 1.29481; disc_gan - 0.24064 +00000141) perceptual - 82.47768; gen_gan - 0.63149; feature_matching - 4.33240; equivariance_value - 0.24809; equivariance_jacobian - 0.44764; kp_distance - 1.29360; disc_gan - 0.24197 +00000142) perceptual - 82.96964; gen_gan - 0.63104; feature_matching - 4.34226; equivariance_value - 0.23924; equivariance_jacobian - 0.44194; kp_distance - 1.32422; disc_gan - 0.23990 +00000142) perceptual - 82.67072; gen_gan - 0.63052; feature_matching - 4.33474; equivariance_value - 0.24119; equivariance_jacobian - 0.44457; kp_distance - 1.32179; disc_gan - 0.24094 +00000143) perceptual - 82.94820; gen_gan - 0.62861; feature_matching - 4.34687; equivariance_value - 0.24417; equivariance_jacobian - 0.44621; kp_distance - 1.33706; disc_gan - 0.24280 +00000143) perceptual - 82.70087; gen_gan - 0.63158; feature_matching - 4.34786; equivariance_value - 0.24576; equivariance_jacobian - 0.44711; kp_distance - 1.34125; disc_gan - 0.24023 +00000144) perceptual - 83.07845; gen_gan - 0.63257; feature_matching - 4.35103; equivariance_value - 0.24001; equivariance_jacobian - 0.44241; kp_distance - 1.36043; disc_gan - 0.23945 +00000144) perceptual - 82.57101; gen_gan - 0.63197; feature_matching - 4.34100; equivariance_value - 0.23938; equivariance_jacobian - 0.44399; kp_distance - 1.35570; disc_gan - 0.23965 +00000145) perceptual - 82.55820; gen_gan - 0.63007; feature_matching - 4.34446; equivariance_value - 0.23719; equivariance_jacobian - 0.44195; kp_distance - 1.32594; disc_gan - 0.24085 +00000145) perceptual - 82.51115; gen_gan - 0.63013; feature_matching - 4.34175; equivariance_value - 0.23744; equivariance_jacobian - 0.44235; kp_distance - 1.32960; disc_gan - 0.24184 +00000146) perceptual - 82.77505; gen_gan - 0.63361; feature_matching - 4.34580; equivariance_value - 0.23806; equivariance_jacobian - 0.44327; kp_distance - 1.33645; disc_gan - 0.23983 +00000146) perceptual - 82.48419; gen_gan - 0.62900; feature_matching - 4.33884; equivariance_value - 0.23656; equivariance_jacobian - 0.44145; kp_distance - 1.33526; disc_gan - 0.24151 +00000147) perceptual - 82.80801; gen_gan - 0.63641; feature_matching - 4.35481; equivariance_value - 0.23883; equivariance_jacobian - 0.44454; kp_distance - 1.38699; disc_gan - 0.23787 +00000147) perceptual - 82.74374; gen_gan - 0.63386; feature_matching - 4.35371; equivariance_value - 0.23852; equivariance_jacobian - 0.44534; kp_distance - 1.38131; disc_gan - 0.23799 +00000148) perceptual - 81.88258; gen_gan - 0.63278; feature_matching - 4.33158; equivariance_value - 0.23090; equivariance_jacobian - 0.43707; kp_distance - 1.37725; disc_gan - 0.23916 +00000148) perceptual - 82.38937; gen_gan - 0.63354; feature_matching - 4.34163; equivariance_value - 0.23303; equivariance_jacobian - 0.43901; kp_distance - 1.37095; disc_gan - 0.23882 +00000149) perceptual - 82.55762; gen_gan - 0.63534; feature_matching - 4.35239; equivariance_value - 0.23718; equivariance_jacobian - 0.44214; kp_distance - 1.33969; disc_gan - 0.23830 +00000149) perceptual - 82.40334; gen_gan - 0.63374; feature_matching - 4.35617; equivariance_value - 0.23679; equivariance_jacobian - 0.44251; kp_distance - 1.33877; disc_gan - 0.23837 +00000150) perceptual - 82.51154; gen_gan - 0.63633; feature_matching - 4.35801; equivariance_value - 0.23751; equivariance_jacobian - 0.43993; kp_distance - 1.35039; disc_gan - 0.23791 +00000150) perceptual - 82.48035; gen_gan - 0.63507; feature_matching - 4.35335; equivariance_value - 0.23845; equivariance_jacobian - 0.43908; kp_distance - 1.34237; disc_gan - 0.23807 +00000151) perceptual - 82.11119; gen_gan - 0.63273; feature_matching - 4.35047; equivariance_value - 0.23729; equivariance_jacobian - 0.43968; kp_distance - 1.31193; disc_gan - 0.23925 +00000151) perceptual - 81.94282; gen_gan - 0.63511; feature_matching - 4.35420; equivariance_value - 0.23907; equivariance_jacobian - 0.43902; kp_distance - 1.30827; disc_gan - 0.23775 +00000152) perceptual - 82.14086; gen_gan - 0.63653; feature_matching - 4.34481; equivariance_value - 0.23895; equivariance_jacobian - 0.43879; kp_distance - 1.38685; disc_gan - 0.23706 +00000152) perceptual - 82.75671; gen_gan - 0.63662; feature_matching - 4.36442; equivariance_value - 0.23833; equivariance_jacobian - 0.43994; kp_distance - 1.39000; disc_gan - 0.23704 +00000153) perceptual - 82.87767; gen_gan - 0.63887; feature_matching - 4.36554; equivariance_value - 0.23410; equivariance_jacobian - 0.43679; kp_distance - 1.38523; disc_gan - 0.23701 +00000153) perceptual - 82.40894; gen_gan - 0.63691; feature_matching - 4.35506; equivariance_value - 0.23433; equivariance_jacobian - 0.43617; kp_distance - 1.38940; disc_gan - 0.23706 +00000154) perceptual - 82.27338; gen_gan - 0.63740; feature_matching - 4.36590; equivariance_value - 0.23625; equivariance_jacobian - 0.43936; kp_distance - 1.32569; disc_gan - 0.23632 +00000154) perceptual - 81.86414; gen_gan - 0.63573; feature_matching - 4.35043; equivariance_value - 0.23671; equivariance_jacobian - 0.43717; kp_distance - 1.32344; disc_gan - 0.23831 +00000155) perceptual - 82.55434; gen_gan - 0.63742; feature_matching - 4.37110; equivariance_value - 0.23242; equivariance_jacobian - 0.43868; kp_distance - 1.32961; disc_gan - 0.23457 +00000155) perceptual - 82.56173; gen_gan - 0.64028; feature_matching - 4.37575; equivariance_value - 0.23407; equivariance_jacobian - 0.44133; kp_distance - 1.32580; disc_gan - 0.23704 +00000156) perceptual - 82.01722; gen_gan - 0.63597; feature_matching - 4.36607; equivariance_value - 0.23760; equivariance_jacobian - 0.44019; kp_distance - 1.26114; disc_gan - 0.23677 +00000156) perceptual - 82.57983; gen_gan - 0.63986; feature_matching - 4.38196; equivariance_value - 0.23557; equivariance_jacobian - 0.43684; kp_distance - 1.26028; disc_gan - 0.23477 +00000157) perceptual - 82.23787; gen_gan - 0.64253; feature_matching - 4.37334; equivariance_value - 0.23128; equivariance_jacobian - 0.43466; kp_distance - 1.33539; disc_gan - 0.23805 +00000157) perceptual - 82.07127; gen_gan - 0.63625; feature_matching - 4.36852; equivariance_value - 0.22861; equivariance_jacobian - 0.43205; kp_distance - 1.33534; disc_gan - 0.23538 +00000158) perceptual - 81.75941; gen_gan - 0.63570; feature_matching - 4.36378; equivariance_value - 0.23342; equivariance_jacobian - 0.43329; kp_distance - 1.30372; disc_gan - 0.23586 +00000158) perceptual - 82.03445; gen_gan - 0.64011; feature_matching - 4.37262; equivariance_value - 0.23209; equivariance_jacobian - 0.43193; kp_distance - 1.31092; disc_gan - 0.23552 +00000159) perceptual - 85.29530; gen_gan - 0.54146; feature_matching - 3.65920; equivariance_value - 0.23436; equivariance_jacobian - 0.44246; kp_distance - 1.32898; disc_gan - 0.33693 +00000159) perceptual - 85.09135; gen_gan - 0.54133; feature_matching - 3.65508; equivariance_value - 0.23370; equivariance_jacobian - 0.44159; kp_distance - 1.32373; disc_gan - 0.33897 +00000160) perceptual - 82.37489; gen_gan - 0.37965; feature_matching - 2.60655; equivariance_value - 0.23095; equivariance_jacobian - 0.43248; kp_distance - 1.34592; disc_gan - 0.41939 +00000160) perceptual - 82.54387; gen_gan - 0.38109; feature_matching - 2.61144; equivariance_value - 0.23219; equivariance_jacobian - 0.43354; kp_distance - 1.34404; disc_gan - 0.41764 +00000161) perceptual - 82.27683; gen_gan - 0.58626; feature_matching - 4.08282; equivariance_value - 0.23201; equivariance_jacobian - 0.43205; kp_distance - 1.30904; disc_gan - 0.28028 +00000161) perceptual - 82.31171; gen_gan - 0.58533; feature_matching - 4.08668; equivariance_value - 0.23014; equivariance_jacobian - 0.43108; kp_distance - 1.31577; disc_gan - 0.28307 +00000162) perceptual - 82.09248; gen_gan - 0.64221; feature_matching - 4.39375; equivariance_value - 0.23272; equivariance_jacobian - 0.43572; kp_distance - 1.34009; disc_gan - 0.23307 +00000162) perceptual - 81.56544; gen_gan - 0.63937; feature_matching - 4.37684; equivariance_value - 0.23278; equivariance_jacobian - 0.43355; kp_distance - 1.33843; disc_gan - 0.23456 +00000163) perceptual - 82.38898; gen_gan - 0.64496; feature_matching - 4.42569; equivariance_value - 0.23030; equivariance_jacobian - 0.43389; kp_distance - 1.31915; disc_gan - 0.23015 +00000163) perceptual - 82.58600; gen_gan - 0.64775; feature_matching - 4.42762; equivariance_value - 0.23129; equivariance_jacobian - 0.43619; kp_distance - 1.31959; disc_gan - 0.23012 +00000164) perceptual - 82.28675; gen_gan - 0.64501; feature_matching - 4.41820; equivariance_value - 0.23734; equivariance_jacobian - 0.43745; kp_distance - 1.28805; disc_gan - 0.23106 +00000164) perceptual - 81.69701; gen_gan - 0.64410; feature_matching - 4.40037; equivariance_value - 0.23597; equivariance_jacobian - 0.43413; kp_distance - 1.29014; disc_gan - 0.22998 +00000165) perceptual - 81.91066; gen_gan - 0.64272; feature_matching - 4.41235; equivariance_value - 0.23563; equivariance_jacobian - 0.43389; kp_distance - 1.27456; disc_gan - 0.23162 +00000165) perceptual - 82.32365; gen_gan - 0.64911; feature_matching - 4.42865; equivariance_value - 0.23581; equivariance_jacobian - 0.43557; kp_distance - 1.27400; disc_gan - 0.23053 +00000166) perceptual - 82.00014; gen_gan - 0.64452; feature_matching - 4.42008; equivariance_value - 0.23709; equivariance_jacobian - 0.43796; kp_distance - 1.27734; disc_gan - 0.23153 +00000166) perceptual - 82.11068; gen_gan - 0.64403; feature_matching - 4.41403; equivariance_value - 0.23782; equivariance_jacobian - 0.44016; kp_distance - 1.27867; disc_gan - 0.23162 +00000167) perceptual - 82.07749; gen_gan - 0.64520; feature_matching - 4.42368; equivariance_value - 0.23640; equivariance_jacobian - 0.43355; kp_distance - 1.33564; disc_gan - 0.23250 +00000167) perceptual - 81.84787; gen_gan - 0.64380; feature_matching - 4.41592; equivariance_value - 0.23674; equivariance_jacobian - 0.43548; kp_distance - 1.33645; disc_gan - 0.23208 +00000168) perceptual - 81.95975; gen_gan - 0.64458; feature_matching - 4.42333; equivariance_value - 0.23677; equivariance_jacobian - 0.43807; kp_distance - 1.30879; disc_gan - 0.23268 +00000168) perceptual - 81.92509; gen_gan - 0.64304; feature_matching - 4.41586; equivariance_value - 0.23798; equivariance_jacobian - 0.43909; kp_distance - 1.31084; disc_gan - 0.23066 +00000169) perceptual - 81.82288; gen_gan - 0.64478; feature_matching - 4.42307; equivariance_value - 0.23675; equivariance_jacobian - 0.43480; kp_distance - 1.33244; disc_gan - 0.23174 +00000169) perceptual - 81.72290; gen_gan - 0.64242; feature_matching - 4.42111; equivariance_value - 0.23692; equivariance_jacobian - 0.43517; kp_distance - 1.32657; disc_gan - 0.23216 +00000170) perceptual - 82.36397; gen_gan - 0.66054; feature_matching - 4.37113; equivariance_value - 0.23688; equivariance_jacobian - 0.43772; kp_distance - 1.35766; disc_gan - 0.29496 +00000170) perceptual - 82.58678; gen_gan - 0.66280; feature_matching - 4.38343; equivariance_value - 0.23528; equivariance_jacobian - 0.43761; kp_distance - 1.35762; disc_gan - 0.29007 +00000171) perceptual - 81.81454; gen_gan - 0.33983; feature_matching - 1.96089; equivariance_value - 0.23125; equivariance_jacobian - 0.43244; kp_distance - 1.33197; disc_gan - 0.44352 +00000171) perceptual - 81.76997; gen_gan - 0.34087; feature_matching - 1.96081; equivariance_value - 0.22990; equivariance_jacobian - 0.43077; kp_distance - 1.32496; disc_gan - 0.44279 +00000172) perceptual - 81.24411; gen_gan - 0.39692; feature_matching - 2.50984; equivariance_value - 0.22880; equivariance_jacobian - 0.42860; kp_distance - 1.35263; disc_gan - 0.39972 +00000172) perceptual - 81.21879; gen_gan - 0.39758; feature_matching - 2.50955; equivariance_value - 0.22792; equivariance_jacobian - 0.42767; kp_distance - 1.35577; disc_gan - 0.39948 +00000173) perceptual - 81.57458; gen_gan - 0.53635; feature_matching - 3.79535; equivariance_value - 0.23542; equivariance_jacobian - 0.43548; kp_distance - 1.34550; disc_gan - 0.31235 +00000173) perceptual - 81.54153; gen_gan - 0.53802; feature_matching - 3.79912; equivariance_value - 0.23688; equivariance_jacobian - 0.43692; kp_distance - 1.34489; disc_gan - 0.31222 +00000174) perceptual - 81.52794; gen_gan - 0.64304; feature_matching - 4.39070; equivariance_value - 0.23327; equivariance_jacobian - 0.43461; kp_distance - 1.29745; disc_gan - 0.22868 +00000174) perceptual - 81.63789; gen_gan - 0.64359; feature_matching - 4.40044; equivariance_value - 0.23505; equivariance_jacobian - 0.43492; kp_distance - 1.30191; disc_gan - 0.22937 +00000175) perceptual - 81.66991; gen_gan - 0.64763; feature_matching - 4.42712; equivariance_value - 0.23189; equivariance_jacobian - 0.43176; kp_distance - 1.34776; disc_gan - 0.22820 +00000175) perceptual - 81.54354; gen_gan - 0.64638; feature_matching - 4.41726; equivariance_value - 0.23372; equivariance_jacobian - 0.43477; kp_distance - 1.35144; disc_gan - 0.22775 +00000176) perceptual - 81.62862; gen_gan - 0.64885; feature_matching - 4.42872; equivariance_value - 0.22953; equivariance_jacobian - 0.43002; kp_distance - 1.36092; disc_gan - 0.22786 +00000176) perceptual - 81.10256; gen_gan - 0.64319; feature_matching - 4.41995; equivariance_value - 0.23150; equivariance_jacobian - 0.43171; kp_distance - 1.35861; disc_gan - 0.23162 +00000177) perceptual - 81.54432; gen_gan - 0.64300; feature_matching - 4.42107; equivariance_value - 0.23738; equivariance_jacobian - 0.43423; kp_distance - 1.25516; disc_gan - 0.22881 +00000177) perceptual - 81.59668; gen_gan - 0.64737; feature_matching - 4.42776; equivariance_value - 0.23555; equivariance_jacobian - 0.43419; kp_distance - 1.26044; disc_gan - 0.23155 +00000178) perceptual - 81.09419; gen_gan - 0.64234; feature_matching - 4.41623; equivariance_value - 0.23356; equivariance_jacobian - 0.42955; kp_distance - 1.29955; disc_gan - 0.23291 +00000178) perceptual - 81.62925; gen_gan - 0.64582; feature_matching - 4.42791; equivariance_value - 0.23257; equivariance_jacobian - 0.43086; kp_distance - 1.29815; disc_gan - 0.23065 +00000179) perceptual - 80.91306; gen_gan - 0.64267; feature_matching - 4.40449; equivariance_value - 0.23356; equivariance_jacobian - 0.43066; kp_distance - 1.29644; disc_gan - 0.23231 +00000179) perceptual - 81.29290; gen_gan - 0.64594; feature_matching - 4.41327; equivariance_value - 0.23251; equivariance_jacobian - 0.43004; kp_distance - 1.29952; disc_gan - 0.23042 +00000180) perceptual - 81.24978; gen_gan - 0.64522; feature_matching - 4.40216; equivariance_value - 0.22697; equivariance_jacobian - 0.42779; kp_distance - 1.32008; disc_gan - 0.23234 +00000180) perceptual - 81.45173; gen_gan - 0.64162; feature_matching - 4.41019; equivariance_value - 0.22656; equivariance_jacobian - 0.42917; kp_distance - 1.33721; disc_gan - 0.23187 +00000181) perceptual - 81.45049; gen_gan - 0.64202; feature_matching - 4.40742; equivariance_value - 0.22744; equivariance_jacobian - 0.42833; kp_distance - 1.37405; disc_gan - 0.23276 +00000181) perceptual - 81.27308; gen_gan - 0.64660; feature_matching - 4.40506; equivariance_value - 0.22599; equivariance_jacobian - 0.42787; kp_distance - 1.37173; disc_gan - 0.23290 +00000182) perceptual - 81.35469; gen_gan - 0.40002; feature_matching - 2.34405; equivariance_value - 0.23080; equivariance_jacobian - 0.42982; kp_distance - 1.32309; disc_gan - 0.45751 +00000182) perceptual - 81.50148; gen_gan - 0.40002; feature_matching - 2.34505; equivariance_value - 0.23059; equivariance_jacobian - 0.43084; kp_distance - 1.32902; disc_gan - 0.45715 +00000183) perceptual - 80.79039; gen_gan - 0.36742; feature_matching - 2.08555; equivariance_value - 0.22988; equivariance_jacobian - 0.42888; kp_distance - 1.31275; disc_gan - 0.42077 +00000183) perceptual - 80.34821; gen_gan - 0.36720; feature_matching - 2.07410; equivariance_value - 0.22883; equivariance_jacobian - 0.42785; kp_distance - 1.31769; disc_gan - 0.42066 +00000184) perceptual - 81.03865; gen_gan - 0.49163; feature_matching - 3.40008; equivariance_value - 0.22831; equivariance_jacobian - 0.42795; kp_distance - 1.28680; disc_gan - 0.34697 +00000184) perceptual - 80.85596; gen_gan - 0.48994; feature_matching - 3.39322; equivariance_value - 0.22888; equivariance_jacobian - 0.43087; kp_distance - 1.28643; disc_gan - 0.34762 +00000185) perceptual - 81.06506; gen_gan - 0.65046; feature_matching - 4.38346; equivariance_value - 0.22630; equivariance_jacobian - 0.42598; kp_distance - 1.29761; disc_gan - 0.23071 +00000185) perceptual - 81.29269; gen_gan - 0.64511; feature_matching - 4.38735; equivariance_value - 0.22658; equivariance_jacobian - 0.42896; kp_distance - 1.30243; disc_gan - 0.23102 +00000186) perceptual - 80.71030; gen_gan - 0.65044; feature_matching - 4.43008; equivariance_value - 0.22795; equivariance_jacobian - 0.42815; kp_distance - 1.27278; disc_gan - 0.22544 +00000186) perceptual - 80.88593; gen_gan - 0.64965; feature_matching - 4.43545; equivariance_value - 0.23122; equivariance_jacobian - 0.43158; kp_distance - 1.27069; disc_gan - 0.22751 +00000187) perceptual - 80.66090; gen_gan - 0.65231; feature_matching - 4.44939; equivariance_value - 0.22871; equivariance_jacobian - 0.42825; kp_distance - 1.26328; disc_gan - 0.22419 +00000187) perceptual - 80.72761; gen_gan - 0.64920; feature_matching - 4.44464; equivariance_value - 0.23092; equivariance_jacobian - 0.43157; kp_distance - 1.26003; disc_gan - 0.22635 +00000188) perceptual - 81.69978; gen_gan - 0.65727; feature_matching - 4.47136; equivariance_value - 0.24255; equivariance_jacobian - 0.43713; kp_distance - 1.23610; disc_gan - 0.22337 +00000188) perceptual - 82.05112; gen_gan - 0.66010; feature_matching - 4.46870; equivariance_value - 0.24316; equivariance_jacobian - 0.43708; kp_distance - 1.24591; disc_gan - 0.22487 +00000189) perceptual - 80.95963; gen_gan - 0.65222; feature_matching - 4.45334; equivariance_value - 0.22814; equivariance_jacobian - 0.42895; kp_distance - 1.26378; disc_gan - 0.22716 +00000189) perceptual - 80.90575; gen_gan - 0.65067; feature_matching - 4.44985; equivariance_value - 0.22759; equivariance_jacobian - 0.42822; kp_distance - 1.25528; disc_gan - 0.22572 +00000190) perceptual - 81.12254; gen_gan - 0.64660; feature_matching - 4.44644; equivariance_value - 0.22704; equivariance_jacobian - 0.42847; kp_distance - 1.23745; disc_gan - 0.22865 +00000190) perceptual - 80.43584; gen_gan - 0.64991; feature_matching - 4.43857; equivariance_value - 0.22644; equivariance_jacobian - 0.42815; kp_distance - 1.22962; disc_gan - 0.22948 +00000191) perceptual - 80.71407; gen_gan - 0.65002; feature_matching - 4.44310; equivariance_value - 0.22988; equivariance_jacobian - 0.42806; kp_distance - 1.27720; disc_gan - 0.22858 +00000191) perceptual - 81.00718; gen_gan - 0.65030; feature_matching - 4.44469; equivariance_value - 0.22863; equivariance_jacobian - 0.42757; kp_distance - 1.26664; disc_gan - 0.22673 +00000192) perceptual - 80.85573; gen_gan - 0.65053; feature_matching - 4.45073; equivariance_value - 0.22784; equivariance_jacobian - 0.42855; kp_distance - 1.26273; disc_gan - 0.22927 +00000192) perceptual - 81.09417; gen_gan - 0.64779; feature_matching - 4.45083; equivariance_value - 0.22452; equivariance_jacobian - 0.42796; kp_distance - 1.25685; disc_gan - 0.22849 +00000193) perceptual - 80.74527; gen_gan - 0.64829; feature_matching - 4.44726; equivariance_value - 0.22616; equivariance_jacobian - 0.42675; kp_distance - 1.24512; disc_gan - 0.22934 +00000193) perceptual - 80.89095; gen_gan - 0.64722; feature_matching - 4.44934; equivariance_value - 0.22698; equivariance_jacobian - 0.42622; kp_distance - 1.25075; disc_gan - 0.22832 +00000194) perceptual - 80.60120; gen_gan - 0.64671; feature_matching - 4.43268; equivariance_value - 0.22338; equivariance_jacobian - 0.42548; kp_distance - 1.28544; disc_gan - 0.22945 +00000194) perceptual - 80.77805; gen_gan - 0.64775; feature_matching - 4.44355; equivariance_value - 0.22260; equivariance_jacobian - 0.42581; kp_distance - 1.27604; disc_gan - 0.23060 +00000195) perceptual - 80.44448; gen_gan - 0.64913; feature_matching - 4.44092; equivariance_value - 0.22432; equivariance_jacobian - 0.42538; kp_distance - 1.29468; disc_gan - 0.22921 +00000195) perceptual - 80.81014; gen_gan - 0.65169; feature_matching - 4.44732; equivariance_value - 0.22198; equivariance_jacobian - 0.42252; kp_distance - 1.29295; disc_gan - 0.22678 +00000196) perceptual - 80.51379; gen_gan - 0.64823; feature_matching - 4.43770; equivariance_value - 0.21999; equivariance_jacobian - 0.42329; kp_distance - 1.31483; disc_gan - 0.22942 +00000196) perceptual - 80.33723; gen_gan - 0.64831; feature_matching - 4.43723; equivariance_value - 0.22132; equivariance_jacobian - 0.42485; kp_distance - 1.31313; disc_gan - 0.23004 +00000197) perceptual - 81.01070; gen_gan - 0.65012; feature_matching - 4.46231; equivariance_value - 0.22105; equivariance_jacobian - 0.42239; kp_distance - 1.30708; disc_gan - 0.22911 +00000197) perceptual - 80.58325; gen_gan - 0.64861; feature_matching - 4.44771; equivariance_value - 0.21911; equivariance_jacobian - 0.42113; kp_distance - 1.29965; disc_gan - 0.22770 +00000198) perceptual - 80.88320; gen_gan - 0.65089; feature_matching - 4.44756; equivariance_value - 0.21860; equivariance_jacobian - 0.42396; kp_distance - 1.28035; disc_gan - 0.22979 +00000198) perceptual - 80.62606; gen_gan - 0.64708; feature_matching - 4.44451; equivariance_value - 0.21805; equivariance_jacobian - 0.42244; kp_distance - 1.27399; disc_gan - 0.22749 +00000199) perceptual - 80.38562; gen_gan - 0.64912; feature_matching - 4.44704; equivariance_value - 0.21288; equivariance_jacobian - 0.41813; kp_distance - 1.36038; disc_gan - 0.22926 +00000199) perceptual - 80.42622; gen_gan - 0.64591; feature_matching - 4.44447; equivariance_value - 0.21297; equivariance_jacobian - 0.41850; kp_distance - 1.35569; disc_gan - 0.23044 +00000200) perceptual - 80.58389; gen_gan - 0.64959; feature_matching - 4.45437; equivariance_value - 0.22394; equivariance_jacobian - 0.42348; kp_distance - 1.30448; disc_gan - 0.22880 +00000200) perceptual - 80.47224; gen_gan - 0.65082; feature_matching - 4.44855; equivariance_value - 0.22178; equivariance_jacobian - 0.42191; kp_distance - 1.30271; disc_gan - 0.22752 +00000201) perceptual - 80.41278; gen_gan - 0.65240; feature_matching - 4.44067; equivariance_value - 0.22982; equivariance_jacobian - 0.42643; kp_distance - 1.22511; disc_gan - 0.22807 +00000201) perceptual - 80.76192; gen_gan - 0.64791; feature_matching - 4.44660; equivariance_value - 0.22624; equivariance_jacobian - 0.42254; kp_distance - 1.22576; disc_gan - 0.22783 +00000202) perceptual - 80.15063; gen_gan - 0.64793; feature_matching - 4.43155; equivariance_value - 0.22756; equivariance_jacobian - 0.42064; kp_distance - 1.23421; disc_gan - 0.23047 +00000202) perceptual - 80.45271; gen_gan - 0.64796; feature_matching - 4.44117; equivariance_value - 0.22778; equivariance_jacobian - 0.42319; kp_distance - 1.23930; disc_gan - 0.23048 +00000203) perceptual - 80.29340; gen_gan - 0.65196; feature_matching - 4.45610; equivariance_value - 0.22572; equivariance_jacobian - 0.42389; kp_distance - 1.24912; disc_gan - 0.22646 +00000203) perceptual - 80.41849; gen_gan - 0.64955; feature_matching - 4.45477; equivariance_value - 0.22460; equivariance_jacobian - 0.42385; kp_distance - 1.25130; disc_gan - 0.22865 +00000204) perceptual - 81.87057; gen_gan - 0.65802; feature_matching - 4.48531; equivariance_value - 0.22922; equivariance_jacobian - 0.43100; kp_distance - 1.24148; disc_gan - 0.22462 +00000204) perceptual - 81.89568; gen_gan - 0.65941; feature_matching - 4.48548; equivariance_value - 0.23260; equivariance_jacobian - 0.43422; kp_distance - 1.24848; disc_gan - 0.22833 +00000205) perceptual - 80.38376; gen_gan - 0.65461; feature_matching - 4.45119; equivariance_value - 0.22121; equivariance_jacobian - 0.42178; kp_distance - 1.27654; disc_gan - 0.22349 +00000205) perceptual - 80.21010; gen_gan - 0.64716; feature_matching - 4.45038; equivariance_value - 0.22027; equivariance_jacobian - 0.42123; kp_distance - 1.28275; disc_gan - 0.22461 +00000206) perceptual - 80.45737; gen_gan - 0.65197; feature_matching - 4.46184; equivariance_value - 0.21801; equivariance_jacobian - 0.41749; kp_distance - 1.32894; disc_gan - 0.22785 +00000206) perceptual - 80.67097; gen_gan - 0.65208; feature_matching - 4.46782; equivariance_value - 0.21818; equivariance_jacobian - 0.41953; kp_distance - 1.32032; disc_gan - 0.22772 +00000207) perceptual - 80.28513; gen_gan - 0.64960; feature_matching - 4.45340; equivariance_value - 0.21843; equivariance_jacobian - 0.42022; kp_distance - 1.33062; disc_gan - 0.22752 +00000207) perceptual - 80.44289; gen_gan - 0.65208; feature_matching - 4.45841; equivariance_value - 0.21859; equivariance_jacobian - 0.41998; kp_distance - 1.32511; disc_gan - 0.22712 +00000208) perceptual - 80.36323; gen_gan - 0.65272; feature_matching - 4.45590; equivariance_value - 0.22488; equivariance_jacobian - 0.42209; kp_distance - 1.24546; disc_gan - 0.22646 +00000208) perceptual - 80.37901; gen_gan - 0.65401; feature_matching - 4.45735; equivariance_value - 0.22189; equivariance_jacobian - 0.41962; kp_distance - 1.24935; disc_gan - 0.22636 +00000209) perceptual - 80.04462; gen_gan - 0.65076; feature_matching - 4.44983; equivariance_value - 0.22200; equivariance_jacobian - 0.42149; kp_distance - 1.27075; disc_gan - 0.22810 +00000209) perceptual - 80.33940; gen_gan - 0.65238; feature_matching - 4.45395; equivariance_value - 0.21939; equivariance_jacobian - 0.41902; kp_distance - 1.26794; disc_gan - 0.22774 +00000210) perceptual - 80.44386; gen_gan - 0.65539; feature_matching - 4.46841; equivariance_value - 0.21593; equivariance_jacobian - 0.41374; kp_distance - 1.32794; disc_gan - 0.22640 +00000210) perceptual - 80.51835; gen_gan - 0.65048; feature_matching - 4.47229; equivariance_value - 0.21764; equivariance_jacobian - 0.41554; kp_distance - 1.32341; disc_gan - 0.22626 +00000211) perceptual - 80.51437; gen_gan - 0.65286; feature_matching - 4.47136; equivariance_value - 0.22594; equivariance_jacobian - 0.42130; kp_distance - 1.28009; disc_gan - 0.22627 +00000211) perceptual - 80.67668; gen_gan - 0.65357; feature_matching - 4.47039; equivariance_value - 0.22579; equivariance_jacobian - 0.42030; kp_distance - 1.27949; disc_gan - 0.22559 +00000212) perceptual - 80.23518; gen_gan - 0.65220; feature_matching - 4.46017; equivariance_value - 0.22238; equivariance_jacobian - 0.41907; kp_distance - 1.28475; disc_gan - 0.22744 +00000212) perceptual - 80.19579; gen_gan - 0.65222; feature_matching - 4.46372; equivariance_value - 0.22031; equivariance_jacobian - 0.41624; kp_distance - 1.27849; disc_gan - 0.22733 +00000213) perceptual - 79.99590; gen_gan - 0.65426; feature_matching - 4.46822; equivariance_value - 0.22058; equivariance_jacobian - 0.41567; kp_distance - 1.27315; disc_gan - 0.22554 +00000213) perceptual - 80.32001; gen_gan - 0.65407; feature_matching - 4.47827; equivariance_value - 0.21942; equivariance_jacobian - 0.41444; kp_distance - 1.27753; disc_gan - 0.22502 +00000214) perceptual - 80.20168; gen_gan - 0.65349; feature_matching - 4.47175; equivariance_value - 0.22013; equivariance_jacobian - 0.41497; kp_distance - 1.31775; disc_gan - 0.22756 +00000214) perceptual - 80.06352; gen_gan - 0.65292; feature_matching - 4.46598; equivariance_value - 0.21935; equivariance_jacobian - 0.41450; kp_distance - 1.31369; disc_gan - 0.22690 +00000215) perceptual - 79.73295; gen_gan - 0.65310; feature_matching - 4.46633; equivariance_value - 0.21333; equivariance_jacobian - 0.41263; kp_distance - 1.33624; disc_gan - 0.22763 +00000215) perceptual - 80.07711; gen_gan - 0.65248; feature_matching - 4.46980; equivariance_value - 0.21549; equivariance_jacobian - 0.41439; kp_distance - 1.33468; disc_gan - 0.22621 +00000216) perceptual - 80.00665; gen_gan - 0.65331; feature_matching - 4.47270; equivariance_value - 0.21409; equivariance_jacobian - 0.41759; kp_distance - 1.38435; disc_gan - 0.22748 +00000216) perceptual - 80.00351; gen_gan - 0.65182; feature_matching - 4.47262; equivariance_value - 0.21167; equivariance_jacobian - 0.41427; kp_distance - 1.38760; disc_gan - 0.22559 +00000217) perceptual - 79.54707; gen_gan - 0.64971; feature_matching - 4.46407; equivariance_value - 0.21404; equivariance_jacobian - 0.41561; kp_distance - 1.30191; disc_gan - 0.22852 +00000217) perceptual - 79.95016; gen_gan - 0.65319; feature_matching - 4.47327; equivariance_value - 0.21388; equivariance_jacobian - 0.41406; kp_distance - 1.30278; disc_gan - 0.22709 +00000218) perceptual - 80.00089; gen_gan - 0.65393; feature_matching - 4.47841; equivariance_value - 0.21059; equivariance_jacobian - 0.41284; kp_distance - 1.33987; disc_gan - 0.22647 +00000218) perceptual - 79.88322; gen_gan - 0.65430; feature_matching - 4.47814; equivariance_value - 0.21125; equivariance_jacobian - 0.41317; kp_distance - 1.33772; disc_gan - 0.22481 +00000219) perceptual - 80.01251; gen_gan - 0.65493; feature_matching - 4.47659; equivariance_value - 0.21234; equivariance_jacobian - 0.41544; kp_distance - 1.30328; disc_gan - 0.22449 +00000219) perceptual - 79.99000; gen_gan - 0.65649; feature_matching - 4.47531; equivariance_value - 0.21471; equivariance_jacobian - 0.41821; kp_distance - 1.29910; disc_gan - 0.22492 +00000220) perceptual - 79.75902; gen_gan - 0.65626; feature_matching - 4.47925; equivariance_value - 0.21591; equivariance_jacobian - 0.41468; kp_distance - 1.27515; disc_gan - 0.22556 +00000220) perceptual - 79.91941; gen_gan - 0.65636; feature_matching - 4.48368; equivariance_value - 0.21656; equivariance_jacobian - 0.41656; kp_distance - 1.27357; disc_gan - 0.22417 +00000221) perceptual - 79.65942; gen_gan - 0.65512; feature_matching - 4.47642; equivariance_value - 0.21243; equivariance_jacobian - 0.41052; kp_distance - 1.35803; disc_gan - 0.22468 +00000221) perceptual - 79.41756; gen_gan - 0.65170; feature_matching - 4.46468; equivariance_value - 0.21654; equivariance_jacobian - 0.41558; kp_distance - 1.34661; disc_gan - 0.22798 +00000222) perceptual - 79.75117; gen_gan - 0.65630; feature_matching - 4.49138; equivariance_value - 0.21373; equivariance_jacobian - 0.41367; kp_distance - 1.28224; disc_gan - 0.22473 +00000222) perceptual - 79.69367; gen_gan - 0.65633; feature_matching - 4.48575; equivariance_value - 0.21344; equivariance_jacobian - 0.41389; kp_distance - 1.27839; disc_gan - 0.22548 +00000223) perceptual - 80.04583; gen_gan - 0.55760; feature_matching - 3.50390; equivariance_value - 0.21405; equivariance_jacobian - 0.41386; kp_distance - 1.28863; disc_gan - 0.36243 +00000223) perceptual - 80.00769; gen_gan - 0.56021; feature_matching - 3.50460; equivariance_value - 0.21120; equivariance_jacobian - 0.41178; kp_distance - 1.29058; disc_gan - 0.36813 +00000224) perceptual - 80.37131; gen_gan - 0.34061; feature_matching - 1.74458; equivariance_value - 0.21858; equivariance_jacobian - 0.42083; kp_distance - 1.23025; disc_gan - 0.44544 +00000224) perceptual - 79.95718; gen_gan - 0.34133; feature_matching - 1.73630; equivariance_value - 0.21686; equivariance_jacobian - 0.41850; kp_distance - 1.22518; disc_gan - 0.44609 +00000225) perceptual - 78.95683; gen_gan - 0.44731; feature_matching - 2.82809; equivariance_value - 0.22135; equivariance_jacobian - 0.41658; kp_distance - 1.25951; disc_gan - 0.37811 +00000225) perceptual - 79.07204; gen_gan - 0.44401; feature_matching - 2.83005; equivariance_value - 0.21845; equivariance_jacobian - 0.41591; kp_distance - 1.25380; disc_gan - 0.37796 +00000226) perceptual - 79.81213; gen_gan - 0.65310; feature_matching - 4.40750; equivariance_value - 0.21770; equivariance_jacobian - 0.41635; kp_distance - 1.27704; disc_gan - 0.22329 +00000226) perceptual - 79.82803; gen_gan - 0.65339; feature_matching - 4.41707; equivariance_value - 0.21499; equivariance_jacobian - 0.41474; kp_distance - 1.27702; disc_gan - 0.22520 +00000227) perceptual - 79.60131; gen_gan - 0.66625; feature_matching - 4.49173; equivariance_value - 0.21385; equivariance_jacobian - 0.41433; kp_distance - 1.32902; disc_gan - 0.21452 +00000227) perceptual - 79.59245; gen_gan - 0.66560; feature_matching - 4.49500; equivariance_value - 0.21403; equivariance_jacobian - 0.41247; kp_distance - 1.31948; disc_gan - 0.21528 +00000228) perceptual - 79.66680; gen_gan - 0.66438; feature_matching - 4.50086; equivariance_value - 0.21874; equivariance_jacobian - 0.41572; kp_distance - 1.23503; disc_gan - 0.21615 +00000228) perceptual - 79.97805; gen_gan - 0.66592; feature_matching - 4.51289; equivariance_value - 0.21984; equivariance_jacobian - 0.41660; kp_distance - 1.23594; disc_gan - 0.21797 +00000229) perceptual - 79.48617; gen_gan - 0.66036; feature_matching - 4.51537; equivariance_value - 0.21481; equivariance_jacobian - 0.41349; kp_distance - 1.30800; disc_gan - 0.21893 +00000229) perceptual - 79.89148; gen_gan - 0.66348; feature_matching - 4.51937; equivariance_value - 0.21248; equivariance_jacobian - 0.41069; kp_distance - 1.30737; disc_gan - 0.21670 +00000230) perceptual - 80.28600; gen_gan - 0.66286; feature_matching - 4.32197; equivariance_value - 0.21521; equivariance_jacobian - 0.41347; kp_distance - 1.24785; disc_gan - 0.30083 +00000230) perceptual - 80.29099; gen_gan - 0.66263; feature_matching - 4.32658; equivariance_value - 0.21439; equivariance_jacobian - 0.41388; kp_distance - 1.24410; disc_gan - 0.30125 +00000231) perceptual - 79.49928; gen_gan - 0.31827; feature_matching - 1.57596; equivariance_value - 0.21592; equivariance_jacobian - 0.41700; kp_distance - 1.20095; disc_gan - 0.45959 +00000231) perceptual - 79.17218; gen_gan - 0.32073; feature_matching - 1.57103; equivariance_value - 0.21764; equivariance_jacobian - 0.41736; kp_distance - 1.20782; disc_gan - 0.45758 +00000232) perceptual - 79.39152; gen_gan - 0.37032; feature_matching - 1.75795; equivariance_value - 0.21569; equivariance_jacobian - 0.41345; kp_distance - 1.25776; disc_gan - 0.42171 +00000232) perceptual - 79.25650; gen_gan - 0.36806; feature_matching - 1.75673; equivariance_value - 0.21247; equivariance_jacobian - 0.41133; kp_distance - 1.25904; disc_gan - 0.42156 +00000233) perceptual - 79.08201; gen_gan - 0.40802; feature_matching - 2.42398; equivariance_value - 0.21290; equivariance_jacobian - 0.40984; kp_distance - 1.27397; disc_gan - 0.39422 +00000233) perceptual - 79.06847; gen_gan - 0.40850; feature_matching - 2.42427; equivariance_value - 0.21437; equivariance_jacobian - 0.41162; kp_distance - 1.27404; disc_gan - 0.39335 +00000234) perceptual - 79.39305; gen_gan - 0.49431; feature_matching - 3.45011; equivariance_value - 0.21839; equivariance_jacobian - 0.41382; kp_distance - 1.21864; disc_gan - 0.36052 +00000234) perceptual - 79.42426; gen_gan - 0.49454; feature_matching - 3.45166; equivariance_value - 0.21760; equivariance_jacobian - 0.41445; kp_distance - 1.21545; disc_gan - 0.36055 +00000235) perceptual - 79.15997; gen_gan - 0.60616; feature_matching - 4.16967; equivariance_value - 0.21530; equivariance_jacobian - 0.41095; kp_distance - 1.24252; disc_gan - 0.26237 +00000235) perceptual - 79.23705; gen_gan - 0.60407; feature_matching - 4.16836; equivariance_value - 0.21776; equivariance_jacobian - 0.41261; kp_distance - 1.23922; disc_gan - 0.26114 +00000236) perceptual - 79.59878; gen_gan - 0.66994; feature_matching - 4.51100; equivariance_value - 0.21453; equivariance_jacobian - 0.41153; kp_distance - 1.28374; disc_gan - 0.21245 +00000236) perceptual - 79.18345; gen_gan - 0.66906; feature_matching - 4.50499; equivariance_value - 0.21572; equivariance_jacobian - 0.41266; kp_distance - 1.28465; disc_gan - 0.21327 +00000237) perceptual - 79.61452; gen_gan - 0.67243; feature_matching - 4.53373; equivariance_value - 0.21254; equivariance_jacobian - 0.40967; kp_distance - 1.27737; disc_gan - 0.21107 +00000237) perceptual - 79.79286; gen_gan - 0.67384; feature_matching - 4.54243; equivariance_value - 0.21339; equivariance_jacobian - 0.41064; kp_distance - 1.27524; disc_gan - 0.21216 +00000238) perceptual - 79.23198; gen_gan - 0.66989; feature_matching - 4.54150; equivariance_value - 0.21429; equivariance_jacobian - 0.41123; kp_distance - 1.27742; disc_gan - 0.21336 +00000238) perceptual - 79.30555; gen_gan - 0.66959; feature_matching - 4.54404; equivariance_value - 0.21222; equivariance_jacobian - 0.40956; kp_distance - 1.27588; disc_gan - 0.21304 +00000239) perceptual - 79.53273; gen_gan - 0.67031; feature_matching - 4.55801; equivariance_value - 0.21647; equivariance_jacobian - 0.41445; kp_distance - 1.26951; disc_gan - 0.21272 +00000239) perceptual - 79.76432; gen_gan - 0.67228; feature_matching - 4.56766; equivariance_value - 0.21505; equivariance_jacobian - 0.41172; kp_distance - 1.26610; disc_gan - 0.21180 +00000240) perceptual - 79.80102; gen_gan - 0.48631; feature_matching - 3.11474; equivariance_value - 0.21514; equivariance_jacobian - 0.41403; kp_distance - 1.22533; disc_gan - 0.37923 +00000240) perceptual - 80.08457; gen_gan - 0.48648; feature_matching - 3.12941; equivariance_value - 0.21630; equivariance_jacobian - 0.41400; kp_distance - 1.22143; disc_gan - 0.37968 +00000241) perceptual - 78.91821; gen_gan - 0.41303; feature_matching - 2.88896; equivariance_value - 0.21248; equivariance_jacobian - 0.41051; kp_distance - 1.24688; disc_gan - 0.39422 +00000241) perceptual - 78.80544; gen_gan - 0.41393; feature_matching - 2.88762; equivariance_value - 0.21431; equivariance_jacobian - 0.41017; kp_distance - 1.24596; disc_gan - 0.39417 +00000242) perceptual - 79.00259; gen_gan - 0.64053; feature_matching - 4.42218; equivariance_value - 0.21766; equivariance_jacobian - 0.41267; kp_distance - 1.23441; disc_gan - 0.24741 +00000242) perceptual - 79.12131; gen_gan - 0.64117; feature_matching - 4.41861; equivariance_value - 0.21411; equivariance_jacobian - 0.40954; kp_distance - 1.22882; disc_gan - 0.24688 +00000243) perceptual - 78.99535; gen_gan - 0.66941; feature_matching - 4.57070; equivariance_value - 0.21768; equivariance_jacobian - 0.41153; kp_distance - 1.23042; disc_gan - 0.21371 +00000243) perceptual - 79.50152; gen_gan - 0.66857; feature_matching - 4.58236; equivariance_value - 0.22028; equivariance_jacobian - 0.41655; kp_distance - 1.22540; disc_gan - 0.21414 +00000244) perceptual - 79.75785; gen_gan - 0.67266; feature_matching - 4.59449; equivariance_value - 0.21248; equivariance_jacobian - 0.40903; kp_distance - 1.23849; disc_gan - 0.21220 +00000244) perceptual - 79.10767; gen_gan - 0.66992; feature_matching - 4.58498; equivariance_value - 0.21392; equivariance_jacobian - 0.41056; kp_distance - 1.23521; disc_gan - 0.21367 +00000245) perceptual - 79.24333; gen_gan - 0.67122; feature_matching - 4.58267; equivariance_value - 0.20625; equivariance_jacobian - 0.41157; kp_distance - 1.26701; disc_gan - 0.21159 +00000245) perceptual - 79.11317; gen_gan - 0.66951; feature_matching - 4.57921; equivariance_value - 0.20740; equivariance_jacobian - 0.41179; kp_distance - 1.27251; disc_gan - 0.21491 +00000246) perceptual - 79.31442; gen_gan - 0.67466; feature_matching - 4.59562; equivariance_value - 0.21069; equivariance_jacobian - 0.41120; kp_distance - 1.28390; disc_gan - 0.21362 +00000246) perceptual - 79.56089; gen_gan - 0.66842; feature_matching - 4.60429; equivariance_value - 0.21229; equivariance_jacobian - 0.41317; kp_distance - 1.28319; disc_gan - 0.21170 +00000247) perceptual - 79.18671; gen_gan - 0.66761; feature_matching - 4.59285; equivariance_value - 0.21303; equivariance_jacobian - 0.41091; kp_distance - 1.26663; disc_gan - 0.21226 +00000247) perceptual - 79.66489; gen_gan - 0.67550; feature_matching - 4.60591; equivariance_value - 0.21167; equivariance_jacobian - 0.41099; kp_distance - 1.26260; disc_gan - 0.21306 +00000248) perceptual - 79.29946; gen_gan - 0.66887; feature_matching - 4.59363; equivariance_value - 0.21236; equivariance_jacobian - 0.41045; kp_distance - 1.28425; disc_gan - 0.21343 +00000248) perceptual - 78.95148; gen_gan - 0.67069; feature_matching - 4.59246; equivariance_value - 0.21078; equivariance_jacobian - 0.41081; kp_distance - 1.28819; disc_gan - 0.21345 +00000249) perceptual - 78.96770; gen_gan - 0.67108; feature_matching - 4.59295; equivariance_value - 0.21314; equivariance_jacobian - 0.40923; kp_distance - 1.24051; disc_gan - 0.21259 +00000249) perceptual - 79.04548; gen_gan - 0.66915; feature_matching - 4.59154; equivariance_value - 0.21457; equivariance_jacobian - 0.40860; kp_distance - 1.23012; disc_gan - 0.21572 +00000250) perceptual - 79.24755; gen_gan - 0.67158; feature_matching - 4.59802; equivariance_value - 0.21033; equivariance_jacobian - 0.40767; kp_distance - 1.25414; disc_gan - 0.21389 +00000250) perceptual - 78.76142; gen_gan - 0.66876; feature_matching - 4.58563; equivariance_value - 0.21448; equivariance_jacobian - 0.41251; kp_distance - 1.25591; disc_gan - 0.21509 +00000251) perceptual - 79.14883; gen_gan - 0.67034; feature_matching - 4.60128; equivariance_value - 0.21417; equivariance_jacobian - 0.41126; kp_distance - 1.23058; disc_gan - 0.21368 +00000251) perceptual - 79.19794; gen_gan - 0.67350; feature_matching - 4.60771; equivariance_value - 0.21505; equivariance_jacobian - 0.41224; kp_distance - 1.23146; disc_gan - 0.21242 +00000252) perceptual - 79.19475; gen_gan - 0.67506; feature_matching - 4.60179; equivariance_value - 0.21545; equivariance_jacobian - 0.41179; kp_distance - 1.22640; disc_gan - 0.21238 +00000252) perceptual - 78.92213; gen_gan - 0.67134; feature_matching - 4.59563; equivariance_value - 0.21464; equivariance_jacobian - 0.40951; kp_distance - 1.22871; disc_gan - 0.21279 +00000253) perceptual - 78.97675; gen_gan - 0.67442; feature_matching - 4.60305; equivariance_value - 0.21099; equivariance_jacobian - 0.41067; kp_distance - 1.21728; disc_gan - 0.21320 +00000253) perceptual - 78.74689; gen_gan - 0.67091; feature_matching - 4.59774; equivariance_value - 0.21270; equivariance_jacobian - 0.41085; kp_distance - 1.22241; disc_gan - 0.21318 +00000254) perceptual - 78.94544; gen_gan - 0.67228; feature_matching - 4.61021; equivariance_value - 0.20965; equivariance_jacobian - 0.40822; kp_distance - 1.26272; disc_gan - 0.21164 +00000254) perceptual - 79.00478; gen_gan - 0.67535; feature_matching - 4.60618; equivariance_value - 0.21099; equivariance_jacobian - 0.40919; kp_distance - 1.26768; disc_gan - 0.21369 +00000255) perceptual - 80.53197; gen_gan - 0.42338; feature_matching - 2.87708; equivariance_value - 0.21386; equivariance_jacobian - 0.41872; kp_distance - 1.24031; disc_gan - 0.42693 +00000255) perceptual - 80.78575; gen_gan - 0.42715; feature_matching - 2.88383; equivariance_value - 0.21307; equivariance_jacobian - 0.41773; kp_distance - 1.23914; disc_gan - 0.42485 +00000256) perceptual - 78.57658; gen_gan - 0.60810; feature_matching - 4.29070; equivariance_value - 0.21263; equivariance_jacobian - 0.41019; kp_distance - 1.24238; disc_gan - 0.26450 +00000256) perceptual - 78.27248; gen_gan - 0.61067; feature_matching - 4.29153; equivariance_value - 0.21024; equivariance_jacobian - 0.40780; kp_distance - 1.23917; disc_gan - 0.26566 +00000257) perceptual - 79.12835; gen_gan - 0.67430; feature_matching - 4.63105; equivariance_value - 0.21016; equivariance_jacobian - 0.40839; kp_distance - 1.29356; disc_gan - 0.20914 +00000257) perceptual - 79.17377; gen_gan - 0.67793; feature_matching - 4.63215; equivariance_value - 0.20914; equivariance_jacobian - 0.40764; kp_distance - 1.28998; disc_gan - 0.20965 +00000258) perceptual - 78.78790; gen_gan - 0.67614; feature_matching - 4.64928; equivariance_value - 0.20997; equivariance_jacobian - 0.40708; kp_distance - 1.33099; disc_gan - 0.20992 +00000258) perceptual - 79.10487; gen_gan - 0.67865; feature_matching - 4.65993; equivariance_value - 0.20935; equivariance_jacobian - 0.40781; kp_distance - 1.33641; disc_gan - 0.20898 +00000259) perceptual - 79.25582; gen_gan - 0.67383; feature_matching - 4.65033; equivariance_value - 0.20768; equivariance_jacobian - 0.40507; kp_distance - 1.29012; disc_gan - 0.20864 +00000259) perceptual - 78.76917; gen_gan - 0.68149; feature_matching - 4.65080; equivariance_value - 0.21034; equivariance_jacobian - 0.40620; kp_distance - 1.29313; disc_gan - 0.20790 +00000260) perceptual - 78.88767; gen_gan - 0.67904; feature_matching - 4.65458; equivariance_value - 0.20964; equivariance_jacobian - 0.40630; kp_distance - 1.28950; disc_gan - 0.20914 +00000260) perceptual - 79.49952; gen_gan - 0.68044; feature_matching - 4.65995; equivariance_value - 0.20882; equivariance_jacobian - 0.40522; kp_distance - 1.29586; disc_gan - 0.20749 +00000261) perceptual - 78.79244; gen_gan - 0.67826; feature_matching - 4.64804; equivariance_value - 0.21290; equivariance_jacobian - 0.40976; kp_distance - 1.25764; disc_gan - 0.20857 +00000261) perceptual - 79.12802; gen_gan - 0.67980; feature_matching - 4.65985; equivariance_value - 0.21423; equivariance_jacobian - 0.40994; kp_distance - 1.26037; disc_gan - 0.20793 +00000262) perceptual - 78.97152; gen_gan - 0.68100; feature_matching - 4.66558; equivariance_value - 0.20951; equivariance_jacobian - 0.40638; kp_distance - 1.27682; disc_gan - 0.20554 +00000262) perceptual - 78.90509; gen_gan - 0.68000; feature_matching - 4.66186; equivariance_value - 0.21023; equivariance_jacobian - 0.40720; kp_distance - 1.28127; disc_gan - 0.20873 +00000263) perceptual - 79.14763; gen_gan - 0.67963; feature_matching - 4.66698; equivariance_value - 0.21093; equivariance_jacobian - 0.40826; kp_distance - 1.26173; disc_gan - 0.20715 +00000263) perceptual - 78.70599; gen_gan - 0.67975; feature_matching - 4.65226; equivariance_value - 0.21126; equivariance_jacobian - 0.40826; kp_distance - 1.25705; disc_gan - 0.20886 +00000264) perceptual - 78.67377; gen_gan - 0.68185; feature_matching - 4.66597; equivariance_value - 0.20704; equivariance_jacobian - 0.40484; kp_distance - 1.27586; disc_gan - 0.20669 +00000264) perceptual - 78.79232; gen_gan - 0.67882; feature_matching - 4.66595; equivariance_value - 0.20837; equivariance_jacobian - 0.40610; kp_distance - 1.27267; disc_gan - 0.20801 +00000265) perceptual - 78.81169; gen_gan - 0.68164; feature_matching - 4.66836; equivariance_value - 0.21020; equivariance_jacobian - 0.40766; kp_distance - 1.26852; disc_gan - 0.20689 +00000265) perceptual - 79.00920; gen_gan - 0.68234; feature_matching - 4.66950; equivariance_value - 0.20949; equivariance_jacobian - 0.40726; kp_distance - 1.26100; disc_gan - 0.20605 +00000266) perceptual - 78.85072; gen_gan - 0.68601; feature_matching - 4.67054; equivariance_value - 0.21123; equivariance_jacobian - 0.40802; kp_distance - 1.27873; disc_gan - 0.20480 +00000266) perceptual - 78.92916; gen_gan - 0.68614; feature_matching - 4.67390; equivariance_value - 0.20945; equivariance_jacobian - 0.40555; kp_distance - 1.28179; disc_gan - 0.20427 +00000267) perceptual - 79.06093; gen_gan - 0.68550; feature_matching - 4.68230; equivariance_value - 0.21141; equivariance_jacobian - 0.40910; kp_distance - 1.26840; disc_gan - 0.20429 +00000267) perceptual - 78.47900; gen_gan - 0.68314; feature_matching - 4.67340; equivariance_value - 0.21305; equivariance_jacobian - 0.40931; kp_distance - 1.26243; disc_gan - 0.20567 +00000268) perceptual - 78.46834; gen_gan - 0.68398; feature_matching - 4.67600; equivariance_value - 0.20994; equivariance_jacobian - 0.40442; kp_distance - 1.26477; disc_gan - 0.20499 +00000268) perceptual - 78.82841; gen_gan - 0.68800; feature_matching - 4.67240; equivariance_value - 0.20961; equivariance_jacobian - 0.40444; kp_distance - 1.26023; disc_gan - 0.20527 +00000269) perceptual - 78.73233; gen_gan - 0.68940; feature_matching - 4.68386; equivariance_value - 0.20619; equivariance_jacobian - 0.40230; kp_distance - 1.24440; disc_gan - 0.20280 +00000269) perceptual - 78.54430; gen_gan - 0.68563; feature_matching - 4.67722; equivariance_value - 0.20699; equivariance_jacobian - 0.40224; kp_distance - 1.25114; disc_gan - 0.20332 +00000270) perceptual - 78.94871; gen_gan - 0.69122; feature_matching - 4.68335; equivariance_value - 0.21146; equivariance_jacobian - 0.40605; kp_distance - 1.23723; disc_gan - 0.20180 +00000270) perceptual - 78.95713; gen_gan - 0.68713; feature_matching - 4.68583; equivariance_value - 0.21113; equivariance_jacobian - 0.40524; kp_distance - 1.24799; disc_gan - 0.20125 +00000271) perceptual - 78.47991; gen_gan - 0.68826; feature_matching - 4.69541; equivariance_value - 0.21181; equivariance_jacobian - 0.40468; kp_distance - 1.26884; disc_gan - 0.20336 +00000271) perceptual - 78.50179; gen_gan - 0.68613; feature_matching - 4.67917; equivariance_value - 0.21041; equivariance_jacobian - 0.40380; kp_distance - 1.26619; disc_gan - 0.20352 +00000272) perceptual - 78.90729; gen_gan - 0.69231; feature_matching - 4.69842; equivariance_value - 0.21088; equivariance_jacobian - 0.40695; kp_distance - 1.23668; disc_gan - 0.20025 +00000272) perceptual - 78.90969; gen_gan - 0.68948; feature_matching - 4.69483; equivariance_value - 0.20892; equivariance_jacobian - 0.40497; kp_distance - 1.23866; disc_gan - 0.19976 +00000273) perceptual - 78.81393; gen_gan - 0.69402; feature_matching - 4.70614; equivariance_value - 0.21271; equivariance_jacobian - 0.40671; kp_distance - 1.24133; disc_gan - 0.19954 +00000273) perceptual - 78.58557; gen_gan - 0.69425; feature_matching - 4.69889; equivariance_value - 0.21210; equivariance_jacobian - 0.40585; kp_distance - 1.24439; disc_gan - 0.19920 +00000274) perceptual - 78.81496; gen_gan - 0.45504; feature_matching - 2.97022; equivariance_value - 0.21181; equivariance_jacobian - 0.40581; kp_distance - 1.25334; disc_gan - 0.39395 +00000274) perceptual - 78.47169; gen_gan - 0.45312; feature_matching - 2.96688; equivariance_value - 0.21103; equivariance_jacobian - 0.40440; kp_distance - 1.24847; disc_gan - 0.39408 +00000275) perceptual - 77.99875; gen_gan - 0.38003; feature_matching - 2.43423; equivariance_value - 0.20709; equivariance_jacobian - 0.40356; kp_distance - 1.27218; disc_gan - 0.41622 +00000275) perceptual - 78.25590; gen_gan - 0.37765; feature_matching - 2.43896; equivariance_value - 0.20747; equivariance_jacobian - 0.40411; kp_distance - 1.27098; disc_gan - 0.41514 +00000276) perceptual - 78.08408; gen_gan - 0.46637; feature_matching - 3.53872; equivariance_value - 0.20798; equivariance_jacobian - 0.40234; kp_distance - 1.26484; disc_gan - 0.35296 +00000276) perceptual - 77.96973; gen_gan - 0.46960; feature_matching - 3.54269; equivariance_value - 0.20762; equivariance_jacobian - 0.40353; kp_distance - 1.26389; disc_gan - 0.35197 +00000277) perceptual - 78.64898; gen_gan - 0.55442; feature_matching - 4.11359; equivariance_value - 0.20761; equivariance_jacobian - 0.40244; kp_distance - 1.29049; disc_gan - 0.29370 +00000277) perceptual - 78.10843; gen_gan - 0.55309; feature_matching - 4.10468; equivariance_value - 0.20597; equivariance_jacobian - 0.40084; kp_distance - 1.28208; disc_gan - 0.29410 +00000278) perceptual - 78.27613; gen_gan - 0.57268; feature_matching - 4.06049; equivariance_value - 0.20703; equivariance_jacobian - 0.40250; kp_distance - 1.25727; disc_gan - 0.31629 +00000278) perceptual - 78.09345; gen_gan - 0.57037; feature_matching - 4.05222; equivariance_value - 0.20597; equivariance_jacobian - 0.40067; kp_distance - 1.25931; disc_gan - 0.31509 +00000279) perceptual - 77.74371; gen_gan - 0.42137; feature_matching - 2.88000; equivariance_value - 0.20586; equivariance_jacobian - 0.40121; kp_distance - 1.28008; disc_gan - 0.38905 +00000279) perceptual - 77.82820; gen_gan - 0.42159; feature_matching - 2.88752; equivariance_value - 0.20752; equivariance_jacobian - 0.40329; kp_distance - 1.27078; disc_gan - 0.38816 +00000280) perceptual - 78.11497; gen_gan - 0.60996; feature_matching - 4.37380; equivariance_value - 0.20766; equivariance_jacobian - 0.40277; kp_distance - 1.29045; disc_gan - 0.25933 +00000280) perceptual - 78.26438; gen_gan - 0.61347; feature_matching - 4.37504; equivariance_value - 0.20790; equivariance_jacobian - 0.40371; kp_distance - 1.28401; disc_gan - 0.25923 +00000281) perceptual - 77.93702; gen_gan - 0.64347; feature_matching - 4.50644; equivariance_value - 0.20698; equivariance_jacobian - 0.40151; kp_distance - 1.27815; disc_gan - 0.24069 +00000281) perceptual - 78.42336; gen_gan - 0.64471; feature_matching - 4.51225; equivariance_value - 0.20836; equivariance_jacobian - 0.40258; kp_distance - 1.28251; disc_gan - 0.23955 +00000282) perceptual - 78.61388; gen_gan - 0.69022; feature_matching - 4.64347; equivariance_value - 0.20459; equivariance_jacobian - 0.40011; kp_distance - 1.27765; disc_gan - 0.21927 +00000282) perceptual - 78.40486; gen_gan - 0.68802; feature_matching - 4.63870; equivariance_value - 0.20261; equivariance_jacobian - 0.39770; kp_distance - 1.27332; disc_gan - 0.21963 +00000283) perceptual - 78.38255; gen_gan - 0.69957; feature_matching - 4.66420; equivariance_value - 0.20826; equivariance_jacobian - 0.40017; kp_distance - 1.27362; disc_gan - 0.20279 +00000283) perceptual - 78.00620; gen_gan - 0.70203; feature_matching - 4.65988; equivariance_value - 0.20810; equivariance_jacobian - 0.40114; kp_distance - 1.27888; disc_gan - 0.20235 +00000284) perceptual - 77.98166; gen_gan - 0.70170; feature_matching - 4.68154; equivariance_value - 0.20498; equivariance_jacobian - 0.40037; kp_distance - 1.31484; disc_gan - 0.19724 +00000284) perceptual - 78.08459; gen_gan - 0.70273; feature_matching - 4.68263; equivariance_value - 0.20428; equivariance_jacobian - 0.39973; kp_distance - 1.31336; disc_gan - 0.19806 +00000285) perceptual - 78.37660; gen_gan - 0.70341; feature_matching - 4.69530; equivariance_value - 0.20572; equivariance_jacobian - 0.39879; kp_distance - 1.27689; disc_gan - 0.19417 +00000285) perceptual - 78.13660; gen_gan - 0.70698; feature_matching - 4.70133; equivariance_value - 0.20564; equivariance_jacobian - 0.39875; kp_distance - 1.27196; disc_gan - 0.19326 +00000286) perceptual - 78.21704; gen_gan - 0.70386; feature_matching - 4.69356; equivariance_value - 0.20870; equivariance_jacobian - 0.40281; kp_distance - 1.24881; disc_gan - 0.19472 +00000286) perceptual - 78.13670; gen_gan - 0.70289; feature_matching - 4.69870; equivariance_value - 0.20840; equivariance_jacobian - 0.40210; kp_distance - 1.25195; disc_gan - 0.19554 +00000287) perceptual - 78.26811; gen_gan - 0.70661; feature_matching - 4.71150; equivariance_value - 0.20813; equivariance_jacobian - 0.40237; kp_distance - 1.27572; disc_gan - 0.19349 +00000287) perceptual - 77.82478; gen_gan - 0.70254; feature_matching - 4.71155; equivariance_value - 0.20662; equivariance_jacobian - 0.40116; kp_distance - 1.27339; disc_gan - 0.19223 +00000288) perceptual - 77.96557; gen_gan - 0.70426; feature_matching - 4.71713; equivariance_value - 0.20516; equivariance_jacobian - 0.40139; kp_distance - 1.30419; disc_gan - 0.19193 +00000288) perceptual - 77.78852; gen_gan - 0.70599; feature_matching - 4.71573; equivariance_value - 0.20290; equivariance_jacobian - 0.39933; kp_distance - 1.29340; disc_gan - 0.19288 +00000289) perceptual - 78.12367; gen_gan - 0.70425; feature_matching - 4.71981; equivariance_value - 0.20174; equivariance_jacobian - 0.39739; kp_distance - 1.27519; disc_gan - 0.19330 +00000289) perceptual - 78.31291; gen_gan - 0.70787; feature_matching - 4.72046; equivariance_value - 0.20240; equivariance_jacobian - 0.39806; kp_distance - 1.27810; disc_gan - 0.19032 +00000290) perceptual - 78.83651; gen_gan - 0.71152; feature_matching - 4.75039; equivariance_value - 0.20301; equivariance_jacobian - 0.39838; kp_distance - 1.26029; disc_gan - 0.18826 +00000290) perceptual - 78.73452; gen_gan - 0.70901; feature_matching - 4.74607; equivariance_value - 0.20388; equivariance_jacobian - 0.39814; kp_distance - 1.25082; disc_gan - 0.18843 +00000291) perceptual - 78.33711; gen_gan - 0.70777; feature_matching - 4.74122; equivariance_value - 0.20694; equivariance_jacobian - 0.40259; kp_distance - 1.22458; disc_gan - 0.18871 +00000291) perceptual - 78.33447; gen_gan - 0.70879; feature_matching - 4.74387; equivariance_value - 0.20380; equivariance_jacobian - 0.39935; kp_distance - 1.22509; disc_gan - 0.19015 +00000292) perceptual - 78.21522; gen_gan - 0.71023; feature_matching - 4.74230; equivariance_value - 0.20777; equivariance_jacobian - 0.40329; kp_distance - 1.24377; disc_gan - 0.18822 +00000292) perceptual - 78.18375; gen_gan - 0.71075; feature_matching - 4.74468; equivariance_value - 0.20708; equivariance_jacobian - 0.40176; kp_distance - 1.25092; disc_gan - 0.18889 +00000293) perceptual - 78.04729; gen_gan - 0.70948; feature_matching - 4.76663; equivariance_value - 0.20191; equivariance_jacobian - 0.39689; kp_distance - 1.23223; disc_gan - 0.18912 +00000293) perceptual - 78.34756; gen_gan - 0.70690; feature_matching - 4.76143; equivariance_value - 0.20564; equivariance_jacobian - 0.40058; kp_distance - 1.23726; disc_gan - 0.18890 +00000294) perceptual - 78.10527; gen_gan - 0.70621; feature_matching - 4.75679; equivariance_value - 0.20793; equivariance_jacobian - 0.40001; kp_distance - 1.19381; disc_gan - 0.18664 +00000294) perceptual - 78.26965; gen_gan - 0.71476; feature_matching - 4.75697; equivariance_value - 0.20573; equivariance_jacobian - 0.39710; kp_distance - 1.19691; disc_gan - 0.18835 +00000295) perceptual - 78.07539; gen_gan - 0.71116; feature_matching - 4.75917; equivariance_value - 0.20420; equivariance_jacobian - 0.39846; kp_distance - 1.21381; disc_gan - 0.18765 +00000295) perceptual - 78.10094; gen_gan - 0.70958; feature_matching - 4.75790; equivariance_value - 0.20478; equivariance_jacobian - 0.39950; kp_distance - 1.20751; disc_gan - 0.18824 +00000296) perceptual - 78.21817; gen_gan - 0.71235; feature_matching - 4.76873; equivariance_value - 0.20604; equivariance_jacobian - 0.40029; kp_distance - 1.20286; disc_gan - 0.18749 +00000296) perceptual - 78.34415; gen_gan - 0.70998; feature_matching - 4.77007; equivariance_value - 0.20662; equivariance_jacobian - 0.40014; kp_distance - 1.20831; disc_gan - 0.18849 +00000297) perceptual - 77.83572; gen_gan - 0.71261; feature_matching - 4.76829; equivariance_value - 0.20762; equivariance_jacobian - 0.39961; kp_distance - 1.22992; disc_gan - 0.18709 +00000297) perceptual - 78.44081; gen_gan - 0.70987; feature_matching - 4.77721; equivariance_value - 0.20609; equivariance_jacobian - 0.39827; kp_distance - 1.22960; disc_gan - 0.18726 +00000298) perceptual - 77.91879; gen_gan - 0.54894; feature_matching - 3.47382; equivariance_value - 0.20826; equivariance_jacobian - 0.40043; kp_distance - 1.24787; disc_gan - 0.35675 +00000298) perceptual - 78.27374; gen_gan - 0.54798; feature_matching - 3.48147; equivariance_value - 0.20786; equivariance_jacobian - 0.39850; kp_distance - 1.24592; disc_gan - 0.35783 +00000299) perceptual - 77.51221; gen_gan - 0.36021; feature_matching - 2.01268; equivariance_value - 0.20711; equivariance_jacobian - 0.39943; kp_distance - 1.26581; disc_gan - 0.42825 +00000299) perceptual - 77.70023; gen_gan - 0.35895; feature_matching - 2.02210; equivariance_value - 0.20563; equivariance_jacobian - 0.39815; kp_distance - 1.26886; disc_gan - 0.42581 +00000299) perceptual - 76.17673; gen_gan - 0.39363; feature_matching - 2.38578; equivariance_value - 0.19830; equivariance_jacobian - 0.38787; kp_distance - 1.23261; disc_gan - 0.40519 +00000299) perceptual - 76.93610; gen_gan - 0.39005; feature_matching - 2.40171; equivariance_value - 0.20009; equivariance_jacobian - 0.38862; kp_distance - 1.22792; disc_gan - 0.40232 +00000299) perceptual - 76.90945; gen_gan - 0.39035; feature_matching - 2.39976; equivariance_value - 0.20175; equivariance_jacobian - 0.39143; kp_distance - 1.23661; disc_gan - 0.40472 +00000299) perceptual - 77.05633; gen_gan - 0.39159; feature_matching - 2.40321; equivariance_value - 0.20085; equivariance_jacobian - 0.38758; kp_distance - 1.23305; disc_gan - 0.40235 +00000299) perceptual - 76.32985; gen_gan - 0.41900; feature_matching - 2.85539; equivariance_value - 0.20350; equivariance_jacobian - 0.38877; kp_distance - 1.21823; disc_gan - 0.38627 +00000299) perceptual - 75.88990; gen_gan - 0.41488; feature_matching - 2.84855; equivariance_value - 0.20391; equivariance_jacobian - 0.38782; kp_distance - 1.22110; disc_gan - 0.38580 +00000299) perceptual - 76.15300; gen_gan - 0.41206; feature_matching - 2.85135; equivariance_value - 0.20723; equivariance_jacobian - 0.39100; kp_distance - 1.22804; disc_gan - 0.38580 +00000299) perceptual - 76.47594; gen_gan - 0.41964; feature_matching - 2.85826; equivariance_value - 0.20428; equivariance_jacobian - 0.39019; kp_distance - 1.22640; disc_gan - 0.38576 +00000300) perceptual - 76.27418; gen_gan - 0.46110; feature_matching - 3.43674; equivariance_value - 0.19893; equivariance_jacobian - 0.38497; kp_distance - 1.22529; disc_gan - 0.35828 +00000300) perceptual - 76.59045; gen_gan - 0.46502; feature_matching - 3.44670; equivariance_value - 0.19957; equivariance_jacobian - 0.38506; kp_distance - 1.21651; disc_gan - 0.35562 +00000300) perceptual - 75.93661; gen_gan - 0.46336; feature_matching - 3.43747; equivariance_value - 0.19975; equivariance_jacobian - 0.38716; kp_distance - 1.20951; disc_gan - 0.35588 +00000300) perceptual - 76.37517; gen_gan - 0.46118; feature_matching - 3.44220; equivariance_value - 0.20036; equivariance_jacobian - 0.38598; kp_distance - 1.22513; disc_gan - 0.35573 +00000301) perceptual - 76.61247; gen_gan - 0.54543; feature_matching - 4.04222; equivariance_value - 0.19808; equivariance_jacobian - 0.38410; kp_distance - 1.24407; disc_gan - 0.30281 +00000301) perceptual - 76.62795; gen_gan - 0.54675; feature_matching - 4.04479; equivariance_value - 0.19963; equivariance_jacobian - 0.38731; kp_distance - 1.24616; disc_gan - 0.30358 +00000301) perceptual - 76.67273; gen_gan - 0.54397; feature_matching - 4.04116; equivariance_value - 0.19748; equivariance_jacobian - 0.38724; kp_distance - 1.24941; disc_gan - 0.30225 +00000301) perceptual - 75.98743; gen_gan - 0.54662; feature_matching - 4.03227; equivariance_value - 0.19904; equivariance_jacobian - 0.38653; kp_distance - 1.23810; disc_gan - 0.30164 +00000302) perceptual - 75.46199; gen_gan - 0.55632; feature_matching - 3.98685; equivariance_value - 0.19964; equivariance_jacobian - 0.38532; kp_distance - 1.25870; disc_gan - 0.32137 +00000302) perceptual - 76.31751; gen_gan - 0.55830; feature_matching - 4.00611; equivariance_value - 0.19810; equivariance_jacobian - 0.38403; kp_distance - 1.25515; disc_gan - 0.32181 +00000302) perceptual - 76.41879; gen_gan - 0.55972; feature_matching - 4.00849; equivariance_value - 0.19980; equivariance_jacobian - 0.38694; kp_distance - 1.25911; disc_gan - 0.32095 +00000302) perceptual - 76.80186; gen_gan - 0.55878; feature_matching - 4.00351; equivariance_value - 0.19601; equivariance_jacobian - 0.38175; kp_distance - 1.25874; disc_gan - 0.31986 +00000303) perceptual - 76.10439; gen_gan - 0.61374; feature_matching - 4.34087; equivariance_value - 0.20264; equivariance_jacobian - 0.38565; kp_distance - 1.23233; disc_gan - 0.28483 +00000303) perceptual - 76.83753; gen_gan - 0.62068; feature_matching - 4.36314; equivariance_value - 0.20450; equivariance_jacobian - 0.38541; kp_distance - 1.23510; disc_gan - 0.28656 +00000303) perceptual - 75.86142; gen_gan - 0.61558; feature_matching - 4.34311; equivariance_value - 0.20272; equivariance_jacobian - 0.38572; kp_distance - 1.23378; disc_gan - 0.28519 +00000303) perceptual - 76.67824; gen_gan - 0.61538; feature_matching - 4.35799; equivariance_value - 0.20323; equivariance_jacobian - 0.38427; kp_distance - 1.24882; disc_gan - 0.28611 +00000304) perceptual - 76.43340; gen_gan - 0.60680; feature_matching - 4.34831; equivariance_value - 0.20952; equivariance_jacobian - 0.38843; kp_distance - 1.20522; disc_gan - 0.25184 +00000304) perceptual - 76.25397; gen_gan - 0.60387; feature_matching - 4.35472; equivariance_value - 0.20690; equivariance_jacobian - 0.38624; kp_distance - 1.20706; disc_gan - 0.25295 +00000304) perceptual - 76.77341; gen_gan - 0.60907; feature_matching - 4.35942; equivariance_value - 0.20448; equivariance_jacobian - 0.38441; kp_distance - 1.21367; disc_gan - 0.25176 +00000304) perceptual - 76.51114; gen_gan - 0.60401; feature_matching - 4.35680; equivariance_value - 0.20377; equivariance_jacobian - 0.38216; kp_distance - 1.20863; disc_gan - 0.25253 +00000305) perceptual - 76.83090; gen_gan - 0.67912; feature_matching - 4.72377; equivariance_value - 0.20187; equivariance_jacobian - 0.38332; kp_distance - 1.20578; disc_gan - 0.20730 +00000305) perceptual - 76.55687; gen_gan - 0.67926; feature_matching - 4.72217; equivariance_value - 0.19918; equivariance_jacobian - 0.37959; kp_distance - 1.21526; disc_gan - 0.20860 +00000305) perceptual - 76.60223; gen_gan - 0.68453; feature_matching - 4.73280; equivariance_value - 0.19837; equivariance_jacobian - 0.38005; kp_distance - 1.21423; disc_gan - 0.20810 +00000305) perceptual - 76.15619; gen_gan - 0.67785; feature_matching - 4.71782; equivariance_value - 0.19952; equivariance_jacobian - 0.38204; kp_distance - 1.19998; disc_gan - 0.20803 +00000306) perceptual - 76.61555; gen_gan - 0.69189; feature_matching - 4.76590; equivariance_value - 0.20251; equivariance_jacobian - 0.38831; kp_distance - 1.24053; disc_gan - 0.19995 +00000306) perceptual - 76.25215; gen_gan - 0.69131; feature_matching - 4.76281; equivariance_value - 0.19704; equivariance_jacobian - 0.38233; kp_distance - 1.23960; disc_gan - 0.19817 +00000306) perceptual - 76.78159; gen_gan - 0.69698; feature_matching - 4.77401; equivariance_value - 0.20007; equivariance_jacobian - 0.38526; kp_distance - 1.24388; disc_gan - 0.20141 +00000306) perceptual - 75.84853; gen_gan - 0.69088; feature_matching - 4.74044; equivariance_value - 0.19876; equivariance_jacobian - 0.38420; kp_distance - 1.24413; disc_gan - 0.20458 +00000307) perceptual - 76.39989; gen_gan - 0.67372; feature_matching - 4.43136; equivariance_value - 0.20195; equivariance_jacobian - 0.38484; kp_distance - 1.22703; disc_gan - 0.28867 +00000307) perceptual - 76.19064; gen_gan - 0.67405; feature_matching - 4.42839; equivariance_value - 0.20346; equivariance_jacobian - 0.38724; kp_distance - 1.22171; disc_gan - 0.28984 +00000307) perceptual - 76.40536; gen_gan - 0.67630; feature_matching - 4.43954; equivariance_value - 0.20229; equivariance_jacobian - 0.38428; kp_distance - 1.23590; disc_gan - 0.28730 +00000307) perceptual - 76.71922; gen_gan - 0.67351; feature_matching - 4.43324; equivariance_value - 0.20007; equivariance_jacobian - 0.38437; kp_distance - 1.23122; disc_gan - 0.28731 +00000308) perceptual - 76.73893; gen_gan - 0.36949; feature_matching - 2.49580; equivariance_value - 0.19921; equivariance_jacobian - 0.38264; kp_distance - 1.28920; disc_gan - 0.42308 +00000308) perceptual - 75.77287; gen_gan - 0.37063; feature_matching - 2.47897; equivariance_value - 0.20072; equivariance_jacobian - 0.38460; kp_distance - 1.26389; disc_gan - 0.42499 +00000308) perceptual - 76.36807; gen_gan - 0.37072; feature_matching - 2.48785; equivariance_value - 0.19649; equivariance_jacobian - 0.38195; kp_distance - 1.27248; disc_gan - 0.42423 +00000308) perceptual - 75.93600; gen_gan - 0.37105; feature_matching - 2.47859; equivariance_value - 0.19688; equivariance_jacobian - 0.38128; kp_distance - 1.27414; disc_gan - 0.42426 +00000309) perceptual - 75.93668; gen_gan - 0.60115; feature_matching - 4.28988; equivariance_value - 0.19865; equivariance_jacobian - 0.38399; kp_distance - 1.24390; disc_gan - 0.26654 +00000309) perceptual - 76.43890; gen_gan - 0.60583; feature_matching - 4.30793; equivariance_value - 0.19703; equivariance_jacobian - 0.38178; kp_distance - 1.23734; disc_gan - 0.26560 +00000309) perceptual - 76.40756; gen_gan - 0.60385; feature_matching - 4.29682; equivariance_value - 0.19858; equivariance_jacobian - 0.38243; kp_distance - 1.23696; disc_gan - 0.26410 +00000309) perceptual - 76.21626; gen_gan - 0.60989; feature_matching - 4.30285; equivariance_value - 0.19890; equivariance_jacobian - 0.38577; kp_distance - 1.24685; disc_gan - 0.26492 +00000310) perceptual - 75.99636; gen_gan - 0.69596; feature_matching - 4.75178; equivariance_value - 0.20028; equivariance_jacobian - 0.38295; kp_distance - 1.24396; disc_gan - 0.19352 +00000310) perceptual - 76.02174; gen_gan - 0.69625; feature_matching - 4.75499; equivariance_value - 0.19923; equivariance_jacobian - 0.38355; kp_distance - 1.24285; disc_gan - 0.19568 +00000310) perceptual - 76.15073; gen_gan - 0.69286; feature_matching - 4.74603; equivariance_value - 0.20058; equivariance_jacobian - 0.38372; kp_distance - 1.24755; disc_gan - 0.19440 +00000310) perceptual - 76.05570; gen_gan - 0.69477; feature_matching - 4.74867; equivariance_value - 0.19951; equivariance_jacobian - 0.38242; kp_distance - 1.24463; disc_gan - 0.19297 +00000311) perceptual - 76.47206; gen_gan - 0.68422; feature_matching - 4.48334; equivariance_value - 0.19668; equivariance_jacobian - 0.38075; kp_distance - 1.28606; disc_gan - 0.30431 +00000311) perceptual - 76.95952; gen_gan - 0.69535; feature_matching - 4.49831; equivariance_value - 0.19665; equivariance_jacobian - 0.38317; kp_distance - 1.29031; disc_gan - 0.30369 +00000311) perceptual - 76.52059; gen_gan - 0.68784; feature_matching - 4.48386; equivariance_value - 0.19832; equivariance_jacobian - 0.38374; kp_distance - 1.28281; disc_gan - 0.30197 +00000311) perceptual - 76.19338; gen_gan - 0.68603; feature_matching - 4.48055; equivariance_value - 0.19427; equivariance_jacobian - 0.37863; kp_distance - 1.29111; disc_gan - 0.30613 +00000312) perceptual - 75.45599; gen_gan - 0.34112; feature_matching - 2.16206; equivariance_value - 0.19685; equivariance_jacobian - 0.38069; kp_distance - 1.26882; disc_gan - 0.44313 +00000312) perceptual - 75.45259; gen_gan - 0.34269; feature_matching - 2.15928; equivariance_value - 0.19699; equivariance_jacobian - 0.38013; kp_distance - 1.27005; disc_gan - 0.44333 +00000312) perceptual - 75.26932; gen_gan - 0.34047; feature_matching - 2.15697; equivariance_value - 0.19547; equivariance_jacobian - 0.38118; kp_distance - 1.26659; disc_gan - 0.44501 +00000312) perceptual - 75.48260; gen_gan - 0.34205; feature_matching - 2.15975; equivariance_value - 0.19634; equivariance_jacobian - 0.38051; kp_distance - 1.26827; disc_gan - 0.44390 +00000313) perceptual - 75.85308; gen_gan - 0.46973; feature_matching - 3.25684; equivariance_value - 0.19245; equivariance_jacobian - 0.37676; kp_distance - 1.29354; disc_gan - 0.35317 +00000313) perceptual - 76.48633; gen_gan - 0.46995; feature_matching - 3.27106; equivariance_value - 0.19657; equivariance_jacobian - 0.37970; kp_distance - 1.29570; disc_gan - 0.35276 +00000313) perceptual - 76.39675; gen_gan - 0.46914; feature_matching - 3.26419; equivariance_value - 0.19714; equivariance_jacobian - 0.38196; kp_distance - 1.28985; disc_gan - 0.35443 +00000313) perceptual - 75.67345; gen_gan - 0.46571; feature_matching - 3.24482; equivariance_value - 0.19685; equivariance_jacobian - 0.38127; kp_distance - 1.29832; disc_gan - 0.35299 +00000314) perceptual - 75.81267; gen_gan - 0.68380; feature_matching - 4.70937; equivariance_value - 0.19626; equivariance_jacobian - 0.38293; kp_distance - 1.28392; disc_gan - 0.19688 +00000314) perceptual - 76.35552; gen_gan - 0.68665; feature_matching - 4.71678; equivariance_value - 0.19395; equivariance_jacobian - 0.38010; kp_distance - 1.28763; disc_gan - 0.19536 +00000314) perceptual - 76.59235; gen_gan - 0.69469; feature_matching - 4.72571; equivariance_value - 0.19509; equivariance_jacobian - 0.38230; kp_distance - 1.27841; disc_gan - 0.19392 +00000314) perceptual - 76.22248; gen_gan - 0.68944; feature_matching - 4.71251; equivariance_value - 0.19632; equivariance_jacobian - 0.38253; kp_distance - 1.27129; disc_gan - 0.19652 +00000315) perceptual - 76.06750; gen_gan - 0.70043; feature_matching - 4.79482; equivariance_value - 0.19681; equivariance_jacobian - 0.38192; kp_distance - 1.27630; disc_gan - 0.18750 +00000315) perceptual - 76.23624; gen_gan - 0.70080; feature_matching - 4.80709; equivariance_value - 0.19538; equivariance_jacobian - 0.37961; kp_distance - 1.27041; disc_gan - 0.19034 +00000315) perceptual - 75.98666; gen_gan - 0.70213; feature_matching - 4.79261; equivariance_value - 0.19748; equivariance_jacobian - 0.38142; kp_distance - 1.27420; disc_gan - 0.18972 +00000315) perceptual - 75.61108; gen_gan - 0.69647; feature_matching - 4.78061; equivariance_value - 0.19761; equivariance_jacobian - 0.38278; kp_distance - 1.27286; disc_gan - 0.19292 +00000316) perceptual - 76.54958; gen_gan - 0.70161; feature_matching - 4.83800; equivariance_value - 0.20698; equivariance_jacobian - 0.38809; kp_distance - 1.24891; disc_gan - 0.18705 +00000316) perceptual - 76.48225; gen_gan - 0.70338; feature_matching - 4.83610; equivariance_value - 0.20755; equivariance_jacobian - 0.38720; kp_distance - 1.24317; disc_gan - 0.19032 +00000316) perceptual - 75.67912; gen_gan - 0.70508; feature_matching - 4.81567; equivariance_value - 0.20474; equivariance_jacobian - 0.38792; kp_distance - 1.23717; disc_gan - 0.18856 +00000316) perceptual - 76.65129; gen_gan - 0.70246; feature_matching - 4.82662; equivariance_value - 0.20650; equivariance_jacobian - 0.38757; kp_distance - 1.24872; disc_gan - 0.18800 +00000317) perceptual - 76.05489; gen_gan - 0.45981; feature_matching - 3.25982; equivariance_value - 0.20242; equivariance_jacobian - 0.38174; kp_distance - 1.21804; disc_gan - 0.42796 +00000317) perceptual - 76.30682; gen_gan - 0.45455; feature_matching - 3.27320; equivariance_value - 0.20620; equivariance_jacobian - 0.38392; kp_distance - 1.20765; disc_gan - 0.42383 +00000317) perceptual - 75.41810; gen_gan - 0.45957; feature_matching - 3.24929; equivariance_value - 0.20526; equivariance_jacobian - 0.38451; kp_distance - 1.20122; disc_gan - 0.42862 +00000317) perceptual - 76.49382; gen_gan - 0.45547; feature_matching - 3.27417; equivariance_value - 0.20406; equivariance_jacobian - 0.38228; kp_distance - 1.21505; disc_gan - 0.42427 +00000318) perceptual - 76.30644; gen_gan - 0.64636; feature_matching - 4.63515; equivariance_value - 0.19764; equivariance_jacobian - 0.37997; kp_distance - 1.26372; disc_gan - 0.22208 +00000318) perceptual - 76.16568; gen_gan - 0.64053; feature_matching - 4.62720; equivariance_value - 0.19988; equivariance_jacobian - 0.38331; kp_distance - 1.26450; disc_gan - 0.22345 +00000318) perceptual - 76.31654; gen_gan - 0.64496; feature_matching - 4.62954; equivariance_value - 0.19797; equivariance_jacobian - 0.38236; kp_distance - 1.25848; disc_gan - 0.22163 +00000318) perceptual - 76.01577; gen_gan - 0.64518; feature_matching - 4.62842; equivariance_value - 0.19887; equivariance_jacobian - 0.38102; kp_distance - 1.27475; disc_gan - 0.21909 +00000319) perceptual - 76.31144; gen_gan - 0.70016; feature_matching - 4.81908; equivariance_value - 0.19925; equivariance_jacobian - 0.38114; kp_distance - 1.23122; disc_gan - 0.18838 +00000319) perceptual - 76.23566; gen_gan - 0.69651; feature_matching - 4.81658; equivariance_value - 0.19873; equivariance_jacobian - 0.38038; kp_distance - 1.22550; disc_gan - 0.18732 +00000319) perceptual - 76.67047; gen_gan - 0.69251; feature_matching - 4.81962; equivariance_value - 0.20043; equivariance_jacobian - 0.38264; kp_distance - 1.22774; disc_gan - 0.19098 +00000319) perceptual - 76.45966; gen_gan - 0.69526; feature_matching - 4.82379; equivariance_value - 0.19986; equivariance_jacobian - 0.38293; kp_distance - 1.22950; disc_gan - 0.18999 +00000320) perceptual - 76.27686; gen_gan - 0.70003; feature_matching - 4.84027; equivariance_value - 0.19340; equivariance_jacobian - 0.37962; kp_distance - 1.24199; disc_gan - 0.18860 +00000320) perceptual - 76.09055; gen_gan - 0.70190; feature_matching - 4.84762; equivariance_value - 0.19442; equivariance_jacobian - 0.38118; kp_distance - 1.24055; disc_gan - 0.18649 +00000320) perceptual - 75.83355; gen_gan - 0.69888; feature_matching - 4.83991; equivariance_value - 0.19323; equivariance_jacobian - 0.37770; kp_distance - 1.24017; disc_gan - 0.18892 +00000320) perceptual - 76.38770; gen_gan - 0.71128; feature_matching - 4.85972; equivariance_value - 0.19626; equivariance_jacobian - 0.38132; kp_distance - 1.24168; disc_gan - 0.18983 +00000321) perceptual - 76.50880; gen_gan - 0.70598; feature_matching - 4.86403; equivariance_value - 0.19758; equivariance_jacobian - 0.38203; kp_distance - 1.28570; disc_gan - 0.19013 +00000321) perceptual - 76.17379; gen_gan - 0.69897; feature_matching - 4.85035; equivariance_value - 0.19714; equivariance_jacobian - 0.38249; kp_distance - 1.28702; disc_gan - 0.19031 +00000321) perceptual - 76.02633; gen_gan - 0.70274; feature_matching - 4.84392; equivariance_value - 0.19795; equivariance_jacobian - 0.38283; kp_distance - 1.27523; disc_gan - 0.18828 +00000321) perceptual - 75.93926; gen_gan - 0.70152; feature_matching - 4.83877; equivariance_value - 0.19669; equivariance_jacobian - 0.38049; kp_distance - 1.28145; disc_gan - 0.18986 +00000322) perceptual - 76.62090; gen_gan - 0.42630; feature_matching - 2.45998; equivariance_value - 0.20009; equivariance_jacobian - 0.38210; kp_distance - 1.24478; disc_gan - 0.46775 +00000322) perceptual - 76.61826; gen_gan - 0.42666; feature_matching - 2.45913; equivariance_value - 0.20047; equivariance_jacobian - 0.38272; kp_distance - 1.23464; disc_gan - 0.47018 +00000322) perceptual - 76.00364; gen_gan - 0.42788; feature_matching - 2.44638; equivariance_value - 0.20086; equivariance_jacobian - 0.38052; kp_distance - 1.23372; disc_gan - 0.47202 +00000322) perceptual - 76.27774; gen_gan - 0.42872; feature_matching - 2.44791; equivariance_value - 0.19979; equivariance_jacobian - 0.38290; kp_distance - 1.24203; disc_gan - 0.46975 +00000323) perceptual - 75.60640; gen_gan - 0.46358; feature_matching - 3.20240; equivariance_value - 0.19549; equivariance_jacobian - 0.37837; kp_distance - 1.24147; disc_gan - 0.35435 +00000323) perceptual - 75.89360; gen_gan - 0.45904; feature_matching - 3.19869; equivariance_value - 0.19431; equivariance_jacobian - 0.37904; kp_distance - 1.25660; disc_gan - 0.35436 +00000323) perceptual - 75.42378; gen_gan - 0.46123; feature_matching - 3.20205; equivariance_value - 0.19692; equivariance_jacobian - 0.38088; kp_distance - 1.24478; disc_gan - 0.35477 +00000323) perceptual - 76.17521; gen_gan - 0.46029; feature_matching - 3.20861; equivariance_value - 0.19710; equivariance_jacobian - 0.38171; kp_distance - 1.24602; disc_gan - 0.35428 +00000324) perceptual - 76.45820; gen_gan - 0.67708; feature_matching - 4.68969; equivariance_value - 0.19725; equivariance_jacobian - 0.38107; kp_distance - 1.23430; disc_gan - 0.21999 +00000324) perceptual - 75.63033; gen_gan - 0.67698; feature_matching - 4.67410; equivariance_value - 0.19520; equivariance_jacobian - 0.37751; kp_distance - 1.22885; disc_gan - 0.22277 +00000324) perceptual - 76.09705; gen_gan - 0.67705; feature_matching - 4.69093; equivariance_value - 0.19611; equivariance_jacobian - 0.37925; kp_distance - 1.23196; disc_gan - 0.21553 +00000324) perceptual - 75.61832; gen_gan - 0.67538; feature_matching - 4.67126; equivariance_value - 0.19672; equivariance_jacobian - 0.38098; kp_distance - 1.23566; disc_gan - 0.22034 +00000325) perceptual - 76.13010; gen_gan - 0.69915; feature_matching - 4.84510; equivariance_value - 0.19710; equivariance_jacobian - 0.38125; kp_distance - 1.24483; disc_gan - 0.18335 +00000325) perceptual - 75.71865; gen_gan - 0.70422; feature_matching - 4.83702; equivariance_value - 0.19585; equivariance_jacobian - 0.38122; kp_distance - 1.24489; disc_gan - 0.18106 +00000325) perceptual - 76.23243; gen_gan - 0.70758; feature_matching - 4.84305; equivariance_value - 0.19492; equivariance_jacobian - 0.37905; kp_distance - 1.23541; disc_gan - 0.18102 +00000325) perceptual - 75.95406; gen_gan - 0.70349; feature_matching - 4.84617; equivariance_value - 0.19693; equivariance_jacobian - 0.37957; kp_distance - 1.22538; disc_gan - 0.18161 +00000326) perceptual - 76.25053; gen_gan - 0.70347; feature_matching - 4.86668; equivariance_value - 0.19548; equivariance_jacobian - 0.37900; kp_distance - 1.24128; disc_gan - 0.18894 +00000326) perceptual - 75.85461; gen_gan - 0.70306; feature_matching - 4.84511; equivariance_value - 0.19904; equivariance_jacobian - 0.38289; kp_distance - 1.22963; disc_gan - 0.18783 +00000326) perceptual - 76.50651; gen_gan - 0.70607; feature_matching - 4.87233; equivariance_value - 0.19690; equivariance_jacobian - 0.38127; kp_distance - 1.22751; disc_gan - 0.18783 +00000326) perceptual - 76.24776; gen_gan - 0.70383; feature_matching - 4.85871; equivariance_value - 0.19966; equivariance_jacobian - 0.38185; kp_distance - 1.22760; disc_gan - 0.18810 +00000327) perceptual - 76.38935; gen_gan - 0.70698; feature_matching - 4.86371; equivariance_value - 0.19552; equivariance_jacobian - 0.37923; kp_distance - 1.24465; disc_gan - 0.18787 +00000327) perceptual - 75.91205; gen_gan - 0.70737; feature_matching - 4.86819; equivariance_value - 0.19693; equivariance_jacobian - 0.37991; kp_distance - 1.23372; disc_gan - 0.18834 +00000327) perceptual - 75.81612; gen_gan - 0.70441; feature_matching - 4.86806; equivariance_value - 0.19553; equivariance_jacobian - 0.37689; kp_distance - 1.23451; disc_gan - 0.19067 +00000327) perceptual - 76.27346; gen_gan - 0.70108; feature_matching - 4.88056; equivariance_value - 0.19640; equivariance_jacobian - 0.37759; kp_distance - 1.23018; disc_gan - 0.18635 +00000328) perceptual - 75.81590; gen_gan - 0.70433; feature_matching - 4.87454; equivariance_value - 0.19521; equivariance_jacobian - 0.37570; kp_distance - 1.26261; disc_gan - 0.18790 +00000328) perceptual - 76.50381; gen_gan - 0.70421; feature_matching - 4.87914; equivariance_value - 0.19789; equivariance_jacobian - 0.38091; kp_distance - 1.26452; disc_gan - 0.19219 +00000328) perceptual - 76.15576; gen_gan - 0.70059; feature_matching - 4.86706; equivariance_value - 0.19843; equivariance_jacobian - 0.38237; kp_distance - 1.27060; disc_gan - 0.18865 +00000328) perceptual - 75.94500; gen_gan - 0.70388; feature_matching - 4.87743; equivariance_value - 0.19760; equivariance_jacobian - 0.37873; kp_distance - 1.26012; disc_gan - 0.18613 +00000329) perceptual - 76.40904; gen_gan - 0.70242; feature_matching - 4.88834; equivariance_value - 0.19456; equivariance_jacobian - 0.37647; kp_distance - 1.25000; disc_gan - 0.18814 +00000329) perceptual - 76.39874; gen_gan - 0.70530; feature_matching - 4.87731; equivariance_value - 0.19429; equivariance_jacobian - 0.37836; kp_distance - 1.24688; disc_gan - 0.18732 +00000329) perceptual - 75.99085; gen_gan - 0.70606; feature_matching - 4.87850; equivariance_value - 0.19888; equivariance_jacobian - 0.38011; kp_distance - 1.25522; disc_gan - 0.18915 +00000329) perceptual - 76.51337; gen_gan - 0.70384; feature_matching - 4.86794; equivariance_value - 0.19895; equivariance_jacobian - 0.38014; kp_distance - 1.25723; disc_gan - 0.18893 +00000330) perceptual - 75.86673; gen_gan - 0.70842; feature_matching - 4.87877; equivariance_value - 0.19641; equivariance_jacobian - 0.38093; kp_distance - 1.25105; disc_gan - 0.18700 +00000330) perceptual - 76.09731; gen_gan - 0.70487; feature_matching - 4.88424; equivariance_value - 0.19576; equivariance_jacobian - 0.37985; kp_distance - 1.25316; disc_gan - 0.18768 +00000330) perceptual - 76.39452; gen_gan - 0.70702; feature_matching - 4.88512; equivariance_value - 0.19703; equivariance_jacobian - 0.38150; kp_distance - 1.24337; disc_gan - 0.18914 +00000330) perceptual - 76.08221; gen_gan - 0.70267; feature_matching - 4.86915; equivariance_value - 0.19896; equivariance_jacobian - 0.38373; kp_distance - 1.24784; disc_gan - 0.18973 +00000331) perceptual - 76.12667; gen_gan - 0.42859; feature_matching - 2.43967; equivariance_value - 0.19634; equivariance_jacobian - 0.38125; kp_distance - 1.25628; disc_gan - 0.46716 +00000331) perceptual - 75.86437; gen_gan - 0.43207; feature_matching - 2.43388; equivariance_value - 0.19796; equivariance_jacobian - 0.38073; kp_distance - 1.26699; disc_gan - 0.46643 +00000331) perceptual - 75.84338; gen_gan - 0.43243; feature_matching - 2.43626; equivariance_value - 0.19248; equivariance_jacobian - 0.37679; kp_distance - 1.26295; disc_gan - 0.46706 +00000331) perceptual - 75.71894; gen_gan - 0.43370; feature_matching - 2.43660; equivariance_value - 0.19431; equivariance_jacobian - 0.37796; kp_distance - 1.26871; disc_gan - 0.46855 +00000332) perceptual - 75.44511; gen_gan - 0.40478; feature_matching - 2.62426; equivariance_value - 0.19516; equivariance_jacobian - 0.38010; kp_distance - 1.24401; disc_gan - 0.39314 +00000332) perceptual - 75.42910; gen_gan - 0.40364; feature_matching - 2.62322; equivariance_value - 0.19614; equivariance_jacobian - 0.37957; kp_distance - 1.24591; disc_gan - 0.39182 +00000332) perceptual - 75.37003; gen_gan - 0.40659; feature_matching - 2.61284; equivariance_value - 0.19383; equivariance_jacobian - 0.37643; kp_distance - 1.23877; disc_gan - 0.39291 +00000332) perceptual - 75.67236; gen_gan - 0.40684; feature_matching - 2.61778; equivariance_value - 0.19122; equivariance_jacobian - 0.37727; kp_distance - 1.25553; disc_gan - 0.39377 +00000333) perceptual - 75.55668; gen_gan - 0.62424; feature_matching - 4.41814; equivariance_value - 0.19702; equivariance_jacobian - 0.37978; kp_distance - 1.23005; disc_gan - 0.26173 +00000333) perceptual - 75.95168; gen_gan - 0.62091; feature_matching - 4.41844; equivariance_value - 0.19505; equivariance_jacobian - 0.37582; kp_distance - 1.23973; disc_gan - 0.25999 +00000333) perceptual - 75.82387; gen_gan - 0.62201; feature_matching - 4.40453; equivariance_value - 0.19504; equivariance_jacobian - 0.37816; kp_distance - 1.24300; disc_gan - 0.26269 +00000333) perceptual - 75.57214; gen_gan - 0.62392; feature_matching - 4.40548; equivariance_value - 0.19733; equivariance_jacobian - 0.37965; kp_distance - 1.23527; disc_gan - 0.26235 +00000334) perceptual - 76.07578; gen_gan - 0.70670; feature_matching - 4.84971; equivariance_value - 0.19859; equivariance_jacobian - 0.38157; kp_distance - 1.26337; disc_gan - 0.17993 +00000334) perceptual - 75.74780; gen_gan - 0.70697; feature_matching - 4.83711; equivariance_value - 0.19330; equivariance_jacobian - 0.37839; kp_distance - 1.26546; disc_gan - 0.18142 +00000334) perceptual - 76.42200; gen_gan - 0.71081; feature_matching - 4.85853; equivariance_value - 0.19567; equivariance_jacobian - 0.38007; kp_distance - 1.26339; disc_gan - 0.18150 +00000334) perceptual - 75.89344; gen_gan - 0.70437; feature_matching - 4.85149; equivariance_value - 0.19532; equivariance_jacobian - 0.37904; kp_distance - 1.27382; disc_gan - 0.18252 +00000335) perceptual - 76.41317; gen_gan - 0.71082; feature_matching - 4.87662; equivariance_value - 0.19458; equivariance_jacobian - 0.37854; kp_distance - 1.27971; disc_gan - 0.17967 +00000335) perceptual - 75.72201; gen_gan - 0.70853; feature_matching - 4.85972; equivariance_value - 0.19475; equivariance_jacobian - 0.37785; kp_distance - 1.27713; disc_gan - 0.18334 +00000335) perceptual - 75.86853; gen_gan - 0.70921; feature_matching - 4.86182; equivariance_value - 0.19670; equivariance_jacobian - 0.37997; kp_distance - 1.28382; disc_gan - 0.18343 +00000335) perceptual - 76.27518; gen_gan - 0.71161; feature_matching - 4.88220; equivariance_value - 0.19339; equivariance_jacobian - 0.37457; kp_distance - 1.27801; disc_gan - 0.18272 +00000336) perceptual - 75.99960; gen_gan - 0.70987; feature_matching - 4.87028; equivariance_value - 0.19301; equivariance_jacobian - 0.37266; kp_distance - 1.25503; disc_gan - 0.18577 +00000336) perceptual - 75.76379; gen_gan - 0.70957; feature_matching - 4.86901; equivariance_value - 0.19483; equivariance_jacobian - 0.37794; kp_distance - 1.25758; disc_gan - 0.18516 +00000336) perceptual - 75.99930; gen_gan - 0.71066; feature_matching - 4.88263; equivariance_value - 0.19544; equivariance_jacobian - 0.37886; kp_distance - 1.25484; disc_gan - 0.18510 +00000336) perceptual - 76.17944; gen_gan - 0.70468; feature_matching - 4.87161; equivariance_value - 0.19533; equivariance_jacobian - 0.37782; kp_distance - 1.24748; disc_gan - 0.18403 +00000337) perceptual - 75.89316; gen_gan - 0.70494; feature_matching - 4.87939; equivariance_value - 0.19352; equivariance_jacobian - 0.37477; kp_distance - 1.21982; disc_gan - 0.18532 +00000337) perceptual - 76.33174; gen_gan - 0.71088; feature_matching - 4.90363; equivariance_value - 0.19511; equivariance_jacobian - 0.37587; kp_distance - 1.22204; disc_gan - 0.18585 +00000337) perceptual - 75.77995; gen_gan - 0.70860; feature_matching - 4.87655; equivariance_value - 0.19517; equivariance_jacobian - 0.37639; kp_distance - 1.22946; disc_gan - 0.18614 +00000337) perceptual - 75.87528; gen_gan - 0.70812; feature_matching - 4.88852; equivariance_value - 0.19379; equivariance_jacobian - 0.37513; kp_distance - 1.22833; disc_gan - 0.18642 +00000338) perceptual - 75.21590; gen_gan - 0.70576; feature_matching - 4.87017; equivariance_value - 0.19364; equivariance_jacobian - 0.37549; kp_distance - 1.23747; disc_gan - 0.18732 +00000338) perceptual - 75.43891; gen_gan - 0.70602; feature_matching - 4.88534; equivariance_value - 0.19192; equivariance_jacobian - 0.37583; kp_distance - 1.23780; disc_gan - 0.18653 +00000338) perceptual - 75.75171; gen_gan - 0.70482; feature_matching - 4.88125; equivariance_value - 0.19603; equivariance_jacobian - 0.37870; kp_distance - 1.23724; disc_gan - 0.18851 +00000338) perceptual - 75.72866; gen_gan - 0.71239; feature_matching - 4.88153; equivariance_value - 0.19588; equivariance_jacobian - 0.37785; kp_distance - 1.23621; disc_gan - 0.18594 +00000339) perceptual - 76.07543; gen_gan - 0.70577; feature_matching - 4.89040; equivariance_value - 0.19481; equivariance_jacobian - 0.37838; kp_distance - 1.23300; disc_gan - 0.18547 +00000339) perceptual - 76.56137; gen_gan - 0.70980; feature_matching - 4.89927; equivariance_value - 0.19207; equivariance_jacobian - 0.37552; kp_distance - 1.23734; disc_gan - 0.18564 +00000339) perceptual - 75.61108; gen_gan - 0.70271; feature_matching - 4.88645; equivariance_value - 0.19352; equivariance_jacobian - 0.37473; kp_distance - 1.24306; disc_gan - 0.18821 +00000339) perceptual - 75.57084; gen_gan - 0.70659; feature_matching - 4.87540; equivariance_value - 0.19443; equivariance_jacobian - 0.37799; kp_distance - 1.24442; disc_gan - 0.18861 +00000340) perceptual - 75.88392; gen_gan - 0.66703; feature_matching - 4.52784; equivariance_value - 0.19233; equivariance_jacobian - 0.37737; kp_distance - 1.25461; disc_gan - 0.27378 +00000340) perceptual - 75.71777; gen_gan - 0.66891; feature_matching - 4.53253; equivariance_value - 0.19194; equivariance_jacobian - 0.37674; kp_distance - 1.25138; disc_gan - 0.27485 +00000340) perceptual - 75.65682; gen_gan - 0.67552; feature_matching - 4.52738; equivariance_value - 0.19176; equivariance_jacobian - 0.37659; kp_distance - 1.25712; disc_gan - 0.27631 +00000340) perceptual - 74.88647; gen_gan - 0.66778; feature_matching - 4.50855; equivariance_value - 0.19313; equivariance_jacobian - 0.37476; kp_distance - 1.25213; disc_gan - 0.27714 +00000341) perceptual - 75.37669; gen_gan - 0.42531; feature_matching - 3.10199; equivariance_value - 0.19760; equivariance_jacobian - 0.38119; kp_distance - 1.25075; disc_gan - 0.38941 +00000341) perceptual - 75.96272; gen_gan - 0.43077; feature_matching - 3.11889; equivariance_value - 0.19540; equivariance_jacobian - 0.37899; kp_distance - 1.24300; disc_gan - 0.38932 +00000341) perceptual - 75.28349; gen_gan - 0.42521; feature_matching - 3.09987; equivariance_value - 0.19581; equivariance_jacobian - 0.38039; kp_distance - 1.24097; disc_gan - 0.38988 +00000341) perceptual - 76.14079; gen_gan - 0.42638; feature_matching - 3.11726; equivariance_value - 0.19799; equivariance_jacobian - 0.37993; kp_distance - 1.23883; disc_gan - 0.38937 +00000342) perceptual - 76.39685; gen_gan - 0.63995; feature_matching - 4.49346; equivariance_value - 0.19480; equivariance_jacobian - 0.37622; kp_distance - 1.26286; disc_gan - 0.23987 +00000342) perceptual - 75.77988; gen_gan - 0.63819; feature_matching - 4.47730; equivariance_value - 0.19234; equivariance_jacobian - 0.37578; kp_distance - 1.26387; disc_gan - 0.24464 +00000342) perceptual - 75.79561; gen_gan - 0.63988; feature_matching - 4.47504; equivariance_value - 0.19494; equivariance_jacobian - 0.37823; kp_distance - 1.26031; disc_gan - 0.24137 +00000342) perceptual - 75.86285; gen_gan - 0.63561; feature_matching - 4.47408; equivariance_value - 0.19670; equivariance_jacobian - 0.37966; kp_distance - 1.27183; disc_gan - 0.24527 +00000343) perceptual - 75.69316; gen_gan - 0.70475; feature_matching - 4.85648; equivariance_value - 0.19589; equivariance_jacobian - 0.37910; kp_distance - 1.26133; disc_gan - 0.18262 +00000343) perceptual - 75.69286; gen_gan - 0.70305; feature_matching - 4.85280; equivariance_value - 0.19777; equivariance_jacobian - 0.37780; kp_distance - 1.26316; disc_gan - 0.18309 +00000343) perceptual - 75.42557; gen_gan - 0.70910; feature_matching - 4.85772; equivariance_value - 0.19671; equivariance_jacobian - 0.37827; kp_distance - 1.27028; disc_gan - 0.18122 +00000343) perceptual - 75.92548; gen_gan - 0.70665; feature_matching - 4.86351; equivariance_value - 0.19242; equivariance_jacobian - 0.37391; kp_distance - 1.25978; disc_gan - 0.18188 +00000344) perceptual - 75.57194; gen_gan - 0.46764; feature_matching - 2.90573; equivariance_value - 0.19449; equivariance_jacobian - 0.37785; kp_distance - 1.29214; disc_gan - 0.41697 +00000344) perceptual - 76.01347; gen_gan - 0.46749; feature_matching - 2.91782; equivariance_value - 0.19362; equivariance_jacobian - 0.37926; kp_distance - 1.28482; disc_gan - 0.41756 +00000344) perceptual - 75.36553; gen_gan - 0.46766; feature_matching - 2.90967; equivariance_value - 0.19440; equivariance_jacobian - 0.37747; kp_distance - 1.27493; disc_gan - 0.41741 +00000344) perceptual - 75.22399; gen_gan - 0.46613; feature_matching - 2.90139; equivariance_value - 0.19719; equivariance_jacobian - 0.38146; kp_distance - 1.28499; disc_gan - 0.41744 +00000345) perceptual - 74.80994; gen_gan - 0.34839; feature_matching - 2.06285; equivariance_value - 0.19809; equivariance_jacobian - 0.37900; kp_distance - 1.24057; disc_gan - 0.43762 +00000345) perceptual - 75.94456; gen_gan - 0.34728; feature_matching - 2.09150; equivariance_value - 0.19472; equivariance_jacobian - 0.37415; kp_distance - 1.25580; disc_gan - 0.43676 +00000345) perceptual - 75.11505; gen_gan - 0.34818; feature_matching - 2.07097; equivariance_value - 0.19684; equivariance_jacobian - 0.37687; kp_distance - 1.24546; disc_gan - 0.43814 +00000345) perceptual - 75.48322; gen_gan - 0.34897; feature_matching - 2.08145; equivariance_value - 0.19581; equivariance_jacobian - 0.37592; kp_distance - 1.24069; disc_gan - 0.43833 +00000346) perceptual - 74.90935; gen_gan - 0.38104; feature_matching - 2.51429; equivariance_value - 0.19688; equivariance_jacobian - 0.37799; kp_distance - 1.24160; disc_gan - 0.41705 +00000346) perceptual - 75.56469; gen_gan - 0.37607; feature_matching - 2.52509; equivariance_value - 0.19846; equivariance_jacobian - 0.37934; kp_distance - 1.23388; disc_gan - 0.41879 +00000346) perceptual - 74.83423; gen_gan - 0.37851; feature_matching - 2.50938; equivariance_value - 0.19950; equivariance_jacobian - 0.38133; kp_distance - 1.23328; disc_gan - 0.42002 +00000346) perceptual - 75.38168; gen_gan - 0.38170; feature_matching - 2.52885; equivariance_value - 0.19603; equivariance_jacobian - 0.37647; kp_distance - 1.23323; disc_gan - 0.41588 +00000347) perceptual - 75.63519; gen_gan - 0.42975; feature_matching - 3.33078; equivariance_value - 0.19747; equivariance_jacobian - 0.37623; kp_distance - 1.23279; disc_gan - 0.38353 +00000347) perceptual - 75.24919; gen_gan - 0.43000; feature_matching - 3.32646; equivariance_value - 0.19767; equivariance_jacobian - 0.37700; kp_distance - 1.23334; disc_gan - 0.38267 +00000347) perceptual - 74.77162; gen_gan - 0.43091; feature_matching - 3.31975; equivariance_value - 0.19819; equivariance_jacobian - 0.37683; kp_distance - 1.23049; disc_gan - 0.38398 +00000347) perceptual - 75.16711; gen_gan - 0.43126; feature_matching - 3.32773; equivariance_value - 0.19864; equivariance_jacobian - 0.37564; kp_distance - 1.24203; disc_gan - 0.38435 +00000348) perceptual - 75.15813; gen_gan - 0.53406; feature_matching - 4.10876; equivariance_value - 0.19856; equivariance_jacobian - 0.37752; kp_distance - 1.22052; disc_gan - 0.31525 +00000348) perceptual - 75.05704; gen_gan - 0.53450; feature_matching - 4.10638; equivariance_value - 0.19790; equivariance_jacobian - 0.37512; kp_distance - 1.22795; disc_gan - 0.31495 +00000348) perceptual - 75.26229; gen_gan - 0.53614; feature_matching - 4.10385; equivariance_value - 0.19489; equivariance_jacobian - 0.37661; kp_distance - 1.22031; disc_gan - 0.31458 +00000348) perceptual - 75.77932; gen_gan - 0.53141; feature_matching - 4.11746; equivariance_value - 0.19615; equivariance_jacobian - 0.37552; kp_distance - 1.23267; disc_gan - 0.31496 +00000349) perceptual - 75.70880; gen_gan - 0.60713; feature_matching - 4.48230; equivariance_value - 0.19613; equivariance_jacobian - 0.37604; kp_distance - 1.22137; disc_gan - 0.27374 +00000349) perceptual - 75.42639; gen_gan - 0.60455; feature_matching - 4.48033; equivariance_value - 0.19576; equivariance_jacobian - 0.37467; kp_distance - 1.21375; disc_gan - 0.27312 +00000349) perceptual - 75.81676; gen_gan - 0.60361; feature_matching - 4.49028; equivariance_value - 0.19610; equivariance_jacobian - 0.37589; kp_distance - 1.21306; disc_gan - 0.27140 +00000349) perceptual - 75.54665; gen_gan - 0.60537; feature_matching - 4.47993; equivariance_value - 0.20105; equivariance_jacobian - 0.37793; kp_distance - 1.21123; disc_gan - 0.27358 +00000350) perceptual - 74.95636; gen_gan - 0.64708; feature_matching - 4.68115; equivariance_value - 0.19353; equivariance_jacobian - 0.37282; kp_distance - 1.21865; disc_gan - 0.24457 +00000350) perceptual - 75.07084; gen_gan - 0.64876; feature_matching - 4.67673; equivariance_value - 0.19782; equivariance_jacobian - 0.37787; kp_distance - 1.21465; disc_gan - 0.24803 +00000350) perceptual - 75.86953; gen_gan - 0.65109; feature_matching - 4.69606; equivariance_value - 0.19391; equivariance_jacobian - 0.37492; kp_distance - 1.21555; disc_gan - 0.24420 +00000350) perceptual - 75.59334; gen_gan - 0.64901; feature_matching - 4.68706; equivariance_value - 0.19434; equivariance_jacobian - 0.37540; kp_distance - 1.22712; disc_gan - 0.24386 +00000351) perceptual - 75.21908; gen_gan - 0.67409; feature_matching - 4.77427; equivariance_value - 0.19114; equivariance_jacobian - 0.37324; kp_distance - 1.27248; disc_gan - 0.23172 +00000351) perceptual - 75.52337; gen_gan - 0.67659; feature_matching - 4.78323; equivariance_value - 0.19254; equivariance_jacobian - 0.37378; kp_distance - 1.27787; disc_gan - 0.22906 +00000351) perceptual - 75.25384; gen_gan - 0.67595; feature_matching - 4.77611; equivariance_value - 0.19191; equivariance_jacobian - 0.37162; kp_distance - 1.27508; disc_gan - 0.23182 +00000351) perceptual - 75.64909; gen_gan - 0.67717; feature_matching - 4.78677; equivariance_value - 0.19065; equivariance_jacobian - 0.37149; kp_distance - 1.27667; disc_gan - 0.22990 +00000352) perceptual - 75.57607; gen_gan - 0.51598; feature_matching - 3.34346; equivariance_value - 0.19445; equivariance_jacobian - 0.37418; kp_distance - 1.22521; disc_gan - 0.42376 +00000352) perceptual - 75.56017; gen_gan - 0.52239; feature_matching - 3.34377; equivariance_value - 0.19379; equivariance_jacobian - 0.37398; kp_distance - 1.22079; disc_gan - 0.42309 +00000352) perceptual - 75.86161; gen_gan - 0.52124; feature_matching - 3.35411; equivariance_value - 0.19568; equivariance_jacobian - 0.37683; kp_distance - 1.22670; disc_gan - 0.42528 +00000352) perceptual - 75.24577; gen_gan - 0.51751; feature_matching - 3.33775; equivariance_value - 0.19527; equivariance_jacobian - 0.37523; kp_distance - 1.22940; disc_gan - 0.42521 +00000353) perceptual - 75.18192; gen_gan - 0.48495; feature_matching - 3.41144; equivariance_value - 0.19375; equivariance_jacobian - 0.37510; kp_distance - 1.22638; disc_gan - 0.34916 +00000353) perceptual - 75.62327; gen_gan - 0.48315; feature_matching - 3.42515; equivariance_value - 0.19351; equivariance_jacobian - 0.37595; kp_distance - 1.22022; disc_gan - 0.34642 +00000353) perceptual - 75.48809; gen_gan - 0.48638; feature_matching - 3.41609; equivariance_value - 0.19468; equivariance_jacobian - 0.37609; kp_distance - 1.22952; disc_gan - 0.34509 +00000353) perceptual - 75.34543; gen_gan - 0.48789; feature_matching - 3.42308; equivariance_value - 0.19538; equivariance_jacobian - 0.37809; kp_distance - 1.22190; disc_gan - 0.34665 +00000354) perceptual - 75.37799; gen_gan - 0.67346; feature_matching - 4.71556; equivariance_value - 0.19326; equivariance_jacobian - 0.37334; kp_distance - 1.24763; disc_gan - 0.21746 +00000354) perceptual - 75.47756; gen_gan - 0.67754; feature_matching - 4.71423; equivariance_value - 0.19591; equivariance_jacobian - 0.37723; kp_distance - 1.25019; disc_gan - 0.21730 +00000354) perceptual - 76.16230; gen_gan - 0.67316; feature_matching - 4.71888; equivariance_value - 0.19128; equivariance_jacobian - 0.37263; kp_distance - 1.25157; disc_gan - 0.21778 +00000354) perceptual - 75.34071; gen_gan - 0.67248; feature_matching - 4.71613; equivariance_value - 0.19109; equivariance_jacobian - 0.37199; kp_distance - 1.24350; disc_gan - 0.22014 +00000355) perceptual - 75.77744; gen_gan - 0.68771; feature_matching - 4.79511; equivariance_value - 0.19484; equivariance_jacobian - 0.37670; kp_distance - 1.23696; disc_gan - 0.20932 +00000355) perceptual - 75.59554; gen_gan - 0.68822; feature_matching - 4.79904; equivariance_value - 0.19371; equivariance_jacobian - 0.37544; kp_distance - 1.22919; disc_gan - 0.21140 +00000355) perceptual - 75.96916; gen_gan - 0.68661; feature_matching - 4.79475; equivariance_value - 0.19418; equivariance_jacobian - 0.37512; kp_distance - 1.23399; disc_gan - 0.20856 +00000355) perceptual - 75.25578; gen_gan - 0.68457; feature_matching - 4.78983; equivariance_value - 0.19475; equivariance_jacobian - 0.37545; kp_distance - 1.23495; disc_gan - 0.20860 +00000356) perceptual - 75.58857; gen_gan - 0.69395; feature_matching - 4.81485; equivariance_value - 0.19476; equivariance_jacobian - 0.37660; kp_distance - 1.25465; disc_gan - 0.20225 +00000356) perceptual - 75.67284; gen_gan - 0.68607; feature_matching - 4.81606; equivariance_value - 0.19483; equivariance_jacobian - 0.37616; kp_distance - 1.25427; disc_gan - 0.20543 +00000356) perceptual - 75.62008; gen_gan - 0.69160; feature_matching - 4.82168; equivariance_value - 0.19307; equivariance_jacobian - 0.37439; kp_distance - 1.24101; disc_gan - 0.20642 +00000356) perceptual - 75.65742; gen_gan - 0.69400; feature_matching - 4.82797; equivariance_value - 0.19704; equivariance_jacobian - 0.37929; kp_distance - 1.25725; disc_gan - 0.20576 +00000357) perceptual - 75.22615; gen_gan - 0.69381; feature_matching - 4.82935; equivariance_value - 0.19541; equivariance_jacobian - 0.37643; kp_distance - 1.25786; disc_gan - 0.20125 +00000357) perceptual - 75.99579; gen_gan - 0.69730; feature_matching - 4.84667; equivariance_value - 0.19555; equivariance_jacobian - 0.37605; kp_distance - 1.26060; disc_gan - 0.20424 +00000357) perceptual - 75.56583; gen_gan - 0.69352; feature_matching - 4.83088; equivariance_value - 0.19390; equivariance_jacobian - 0.37528; kp_distance - 1.24801; disc_gan - 0.20351 +00000357) perceptual - 75.68138; gen_gan - 0.69241; feature_matching - 4.83074; equivariance_value - 0.19408; equivariance_jacobian - 0.37372; kp_distance - 1.25130; disc_gan - 0.20313 +00000358) perceptual - 75.90548; gen_gan - 0.47448; feature_matching - 3.11443; equivariance_value - 0.19346; equivariance_jacobian - 0.37422; kp_distance - 1.23613; disc_gan - 0.41708 +00000358) perceptual - 75.63497; gen_gan - 0.47357; feature_matching - 3.10506; equivariance_value - 0.19254; equivariance_jacobian - 0.37405; kp_distance - 1.23621; disc_gan - 0.42173 +00000358) perceptual - 75.64905; gen_gan - 0.47600; feature_matching - 3.10366; equivariance_value - 0.19451; equivariance_jacobian - 0.37585; kp_distance - 1.23378; disc_gan - 0.42032 +00000358) perceptual - 75.56519; gen_gan - 0.47440; feature_matching - 3.10776; equivariance_value - 0.19323; equivariance_jacobian - 0.37511; kp_distance - 1.22460; disc_gan - 0.42026 +00000359) perceptual - 75.44585; gen_gan - 0.56611; feature_matching - 4.12996; equivariance_value - 0.18998; equivariance_jacobian - 0.37277; kp_distance - 1.27410; disc_gan - 0.29225 +00000359) perceptual - 74.80260; gen_gan - 0.56777; feature_matching - 4.12512; equivariance_value - 0.19277; equivariance_jacobian - 0.37518; kp_distance - 1.27979; disc_gan - 0.29333 +00000359) perceptual - 75.11159; gen_gan - 0.56900; feature_matching - 4.13239; equivariance_value - 0.19301; equivariance_jacobian - 0.37440; kp_distance - 1.28614; disc_gan - 0.29133 +00000359) perceptual - 75.16217; gen_gan - 0.57056; feature_matching - 4.12940; equivariance_value - 0.19164; equivariance_jacobian - 0.37171; kp_distance - 1.28564; disc_gan - 0.29238 +00000360) perceptual - 75.58418; gen_gan - 0.62977; feature_matching - 4.41286; equivariance_value - 0.19112; equivariance_jacobian - 0.37224; kp_distance - 1.26179; disc_gan - 0.25597 +00000360) perceptual - 75.29714; gen_gan - 0.62655; feature_matching - 4.40247; equivariance_value - 0.18993; equivariance_jacobian - 0.37044; kp_distance - 1.26110; disc_gan - 0.25876 +00000360) perceptual - 75.25217; gen_gan - 0.62804; feature_matching - 4.40030; equivariance_value - 0.18988; equivariance_jacobian - 0.37193; kp_distance - 1.25245; disc_gan - 0.25822 +00000360) perceptual - 75.60233; gen_gan - 0.63186; feature_matching - 4.40807; equivariance_value - 0.19203; equivariance_jacobian - 0.37373; kp_distance - 1.25364; disc_gan - 0.25798 +00000361) perceptual - 75.70560; gen_gan - 0.69955; feature_matching - 4.84283; equivariance_value - 0.18959; equivariance_jacobian - 0.37115; kp_distance - 1.26634; disc_gan - 0.19315 +00000361) perceptual - 75.42204; gen_gan - 0.69659; feature_matching - 4.82549; equivariance_value - 0.19308; equivariance_jacobian - 0.37461; kp_distance - 1.26115; disc_gan - 0.19434 +00000361) perceptual - 75.45468; gen_gan - 0.69704; feature_matching - 4.83243; equivariance_value - 0.19209; equivariance_jacobian - 0.37247; kp_distance - 1.26117; disc_gan - 0.19172 +00000361) perceptual - 75.15836; gen_gan - 0.69515; feature_matching - 4.81143; equivariance_value - 0.19144; equivariance_jacobian - 0.37175; kp_distance - 1.25748; disc_gan - 0.19515 +00000362) perceptual - 75.20616; gen_gan - 0.69631; feature_matching - 4.84909; equivariance_value - 0.19161; equivariance_jacobian - 0.37284; kp_distance - 1.25429; disc_gan - 0.19186 +00000362) perceptual - 74.93314; gen_gan - 0.70114; feature_matching - 4.84921; equivariance_value - 0.19176; equivariance_jacobian - 0.37158; kp_distance - 1.26431; disc_gan - 0.19309 +00000362) perceptual - 75.85328; gen_gan - 0.70024; feature_matching - 4.86373; equivariance_value - 0.19103; equivariance_jacobian - 0.37134; kp_distance - 1.25920; disc_gan - 0.19270 +00000362) perceptual - 75.91089; gen_gan - 0.70043; feature_matching - 4.87156; equivariance_value - 0.19510; equivariance_jacobian - 0.37681; kp_distance - 1.25708; disc_gan - 0.19227 +00000363) perceptual - 75.38505; gen_gan - 0.70054; feature_matching - 4.86951; equivariance_value - 0.19022; equivariance_jacobian - 0.37194; kp_distance - 1.29006; disc_gan - 0.19584 +00000363) perceptual - 75.32419; gen_gan - 0.69839; feature_matching - 4.85881; equivariance_value - 0.19090; equivariance_jacobian - 0.37389; kp_distance - 1.28223; disc_gan - 0.19634 +00000363) perceptual - 75.69920; gen_gan - 0.70224; feature_matching - 4.87103; equivariance_value - 0.19111; equivariance_jacobian - 0.37555; kp_distance - 1.28552; disc_gan - 0.19272 +00000363) perceptual - 75.81808; gen_gan - 0.69908; feature_matching - 4.88280; equivariance_value - 0.18976; equivariance_jacobian - 0.37368; kp_distance - 1.28977; disc_gan - 0.19357 +00000364) perceptual - 74.74449; gen_gan - 0.41603; feature_matching - 2.68214; equivariance_value - 0.19286; equivariance_jacobian - 0.37569; kp_distance - 1.24952; disc_gan - 0.43409 +00000364) perceptual - 74.68935; gen_gan - 0.41751; feature_matching - 2.68454; equivariance_value - 0.19285; equivariance_jacobian - 0.37403; kp_distance - 1.25366; disc_gan - 0.43161 +00000364) perceptual - 75.15385; gen_gan - 0.41833; feature_matching - 2.69680; equivariance_value - 0.19038; equivariance_jacobian - 0.37331; kp_distance - 1.25155; disc_gan - 0.43257 +00000364) perceptual - 75.17429; gen_gan - 0.41673; feature_matching - 2.69266; equivariance_value - 0.19008; equivariance_jacobian - 0.37364; kp_distance - 1.23099; disc_gan - 0.43305 +00000365) perceptual - 74.79185; gen_gan - 0.59470; feature_matching - 4.17669; equivariance_value - 0.19192; equivariance_jacobian - 0.37199; kp_distance - 1.25899; disc_gan - 0.27089 +00000365) perceptual - 74.86716; gen_gan - 0.59792; feature_matching - 4.16619; equivariance_value - 0.19264; equivariance_jacobian - 0.37507; kp_distance - 1.25079; disc_gan - 0.27249 +00000365) perceptual - 75.11900; gen_gan - 0.59661; feature_matching - 4.18464; equivariance_value - 0.19021; equivariance_jacobian - 0.37229; kp_distance - 1.24893; disc_gan - 0.27278 +00000365) perceptual - 75.82484; gen_gan - 0.59335; feature_matching - 4.19516; equivariance_value - 0.19004; equivariance_jacobian - 0.37086; kp_distance - 1.24985; disc_gan - 0.27355 diff --git a/dagan/config/vox-adv-256.yaml b/dagan/config/vox-adv-256.yaml new file mode 100644 index 00000000..16997a2f --- /dev/null +++ b/dagan/config/vox-adv-256.yaml @@ -0,0 +1,88 @@ +dataset_params: + root_dir: /root/autodl-tmp/vox-png + frame_shape: [256, 256, 3] + id_sampling: True + pairs_list: data/vox256.csv + augmentation_params: + flip_param: + horizontal_flip: True + time_flip: True + jitter_param: + brightness: 0.1 + contrast: 0.1 + saturation: 0.1 + hue: 0.1 + + +model_params: + common_params: + num_kp: 10 + num_channels: 3 + estimate_jacobian: True + kp_detector_params: + temperature: 0.1 + block_expansion: 32 + max_features: 1024 + scale_factor: 0.25 + num_blocks: 5 + generator_params: + block_expansion: 64 + max_features: 512 + num_down_blocks: 2 + num_bottleneck_blocks: 6 + estimate_occlusion_map: True + dense_motion_params: + block_expansion: 64 + max_features: 1024 + num_blocks: 5 + scale_factor: 0.25 + discriminator_params: + scales: [1] + block_expansion: 32 + max_features: 512 + num_blocks: 4 + use_kp: True + + +train_params: + num_epochs: 300 + num_repeats: 75 + epoch_milestones: [] + lr_generator: 2.0e-4 + lr_discriminator: 2.0e-4 + lr_kp_detector: 2.0e-4 + batch_size: 4 + scales: [1, 0.5, 0.25, 0.125] + checkpoint_freq: 10 + transform_params: + sigma_affine: 0.05 + sigma_tps: 0.005 + points_tps: 5 + loss_weights: + generator_gan: 1 + discriminator_gan: 1 + feature_matching: [10, 10, 10, 10] + perceptual: [10, 10, 10, 10, 10] + equivariance_value: 10 + equivariance_jacobian: 10 + kp_distance: 10 + kp_prior: 0 + kp_scale: 0 + depth_constraint: 0 + +reconstruction_params: + num_videos: 1000 + format: '.mp4' + +animate_params: + num_pairs: 50 + format: '.mp4' + normalization_params: + adapt_movement_scale: False + use_relative_movement: True + use_relative_jacobian: True + +visualizer_params: + kp_size: 5 + draw_border: True + colormap: 'gist_rainbow' diff --git a/dagan/create_evaluation.py b/dagan/create_evaluation.py new file mode 100644 index 00000000..fbc245db --- /dev/null +++ b/dagan/create_evaluation.py @@ -0,0 +1,121 @@ +import os +import csv +import numpy as np +from tqdm import tqdm + + +def create_evaluation_csv(dataset_path, output_file='data/vox_evaluation_test.csv'): + """ + 创建用于模型评估的CSV文件，生成从根目录开始的绝对路径 + + 这个函数会生成完整的绝对路径，格式如： + /root/autodl-tmp/test/test/id11248#ULBH3A8DjPM#00004.txt#000.mp4/0000111.png + + 参数: + dataset_path: 数据集根目录的路径（例如：/root/autodl-tmp/test/test） + output_file: CSV文件的输出路径 + """ + # 确保使用绝对路径 + dataset_path = os.path.abspath(dataset_path) + output_file = os.path.abspath(output_file) + + if not os.path.exists(dataset_path): + print(f"错误: 数据集路径 {dataset_path} 不存在!") + return False + + print(f"正在检查数据集路径: {dataset_path}") + print(f"输出文件将保存到: {output_file}") + + try: + # 获取所有视频文件夹 + test_videos = [d for d in os.listdir(dataset_path) + if os.path.isdir(os.path.join(dataset_path, d))] + print(f"找到 {len(test_videos)} 个视频文件夹") + + if len(test_videos) < 2: + print("错误: 至少需要2个视频文件夹才能创建评估对!") + return False + + with open(output_file, 'w', newline='', encoding='utf-8') as f: + writer = csv.writer(f) + writer.writerow(['source', 'driving']) + + print("开始创建2000对评估图像组合...") + valid_pairs = 0 + + for i in tqdm(range(2000), desc="生成评估对"): + try: + # 随机选择两个不同的视频 + video_pair = np.random.choice(test_videos, size=2, replace=False) + + # 构建第一个视频的完整路径 + source_video_path = os.path.join(dataset_path, video_pair[0]) + source_frames = [f for f in os.listdir(source_video_path) + if f.lower().endswith('.png')] + + if not source_frames: + continue + + # 构建第二个视频的完整路径 + driving_video_path = os.path.join(dataset_path, video_pair[1]) + driving_frames = [f for f in os.listdir(driving_video_path) + if f.lower().endswith('.png')] + + if not driving_frames: + continue + + # 随机选择帧并构建完整的绝对路径 + source_frame = np.random.choice(source_frames) + driving_frame = np.random.choice(driving_frames) + + # 构建完整的绝对路径 + source_abs_path = os.path.join(dataset_path, video_pair[0], source_frame) + driving_abs_path = os.path.join(dataset_path, video_pair[1], driving_frame) + + # 验证文件存在性 + if os.path.exists(source_abs_path) and os.path.exists(driving_abs_path): + writer.writerow([source_abs_path, driving_abs_path]) + valid_pairs += 1 + else: + if not os.path.exists(source_abs_path): + print(f"\n源文件不存在: {source_abs_path}") + if not os.path.exists(driving_abs_path): + print(f"\n驱动文件不存在: {driving_abs_path}") + + except Exception as e: + print(f"\n处理第 {i + 1} 对时出错: {str(e)}") + continue + + print(f"\n成功创建了 {valid_pairs} 个有效的评估数据对") + print(f"评估CSV文件已保存到: {output_file}") + + if valid_pairs < 100: + print("\n警告: 生成的有效数据对数量过少，可能会影响评估效果") + + return valid_pairs > 0 + + except Exception as e: + print(f"创建CSV文件时出错: {str(e)}") + return False + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description='创建评估数据集的CSV文件') + parser.add_argument('--dataset_path', type=str, + default='/root/autodl-tmp/vox-png/test', + help='数据集根目录的绝对路径') + parser.add_argument('--output_file', type=str, + default='data/vox_evaluation.csv', + help='输出CSV文件的路径') + + args = parser.parse_args() + + print("开始创建评估数据集...") + success = create_evaluation_csv(args.dataset_path, args.output_file) + + if success: + print("评估数据集创建完成!") + else: + print("评估数据集创建失败!") \ No newline at end of file diff --git a/dagan/crop-video.py b/dagan/crop-video.py new file mode 100644 index 00000000..1a7740ee --- /dev/null +++ b/dagan/crop-video.py @@ -0,0 +1,158 @@ +import face_alignment +import skimage.io +import numpy +from argparse import ArgumentParser +from skimage import img_as_ubyte +from skimage.transform import resize +from tqdm import tqdm +import os +import imageio +import numpy as np +import warnings +warnings.filterwarnings("ignore") + +def extract_bbox(frame, fa): + if max(frame.shape[0], frame.shape[1]) > 640: + scale_factor = max(frame.shape[0], frame.shape[1]) / 640.0 + frame = resize(frame, (int(frame.shape[0] / scale_factor), int(frame.shape[1] / scale_factor))) + frame = img_as_ubyte(frame) + else: + scale_factor = 1 + frame = frame[..., :3] + bboxes = fa.face_detector.detect_from_image(frame[..., ::-1]) + if len(bboxes) == 0: + return [] + return np.array(bboxes)[:, :-1] * scale_factor + + + +def bb_intersection_over_union(boxA, boxB): + xA = max(boxA[0], boxB[0]) + yA = max(boxA[1], boxB[1]) + xB = min(boxA[2], boxB[2]) + yB = min(boxA[3], boxB[3]) + interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1) + boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1) + boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1) + iou = interArea / float(boxAArea + boxBArea - interArea) + return iou + + +def join(tube_bbox, bbox): + xA = min(tube_bbox[0], bbox[0]) + yA = min(tube_bbox[1], bbox[1]) + xB = max(tube_bbox[2], bbox[2]) + yB = max(tube_bbox[3], bbox[3]) + return (xA, yA, xB, yB) + + +def compute_bbox(start, end, fps, tube_bbox, frame_shape, inp, image_shape, increase_area=0.1): + left, top, right, bot = tube_bbox + width = right - left + height = bot - top + + #Computing aspect preserving bbox + width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width)) + height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height)) + + left = int(left - width_increase * width) + top = int(top - height_increase * height) + right = int(right + width_increase * width) + bot = int(bot + height_increase * height) + + top, bot, left, right = max(0, top), min(bot, frame_shape[0]), max(0, left), min(right, frame_shape[1]) + h, w = bot - top, right - left + + start = start / fps + end = end / fps + time = end - start + + scale = f'{image_shape[0]}:{image_shape[1]}' + + return f'ffmpeg -i {inp} -ss {start} -t {time} -filter:v "crop={w}:{h}:{left}:{top}, scale={scale}" crop.mp4' + + +def compute_bbox_trajectories(trajectories, fps, frame_shape, args): + commands = [] + for i, (bbox, tube_bbox, start, end) in enumerate(trajectories): + if (end - start) > args.min_frames: + command = compute_bbox(start, end, fps, tube_bbox, frame_shape, inp=args.inp, image_shape=args.image_shape, increase_area=args.increase) + commands.append(command) + return commands + + +def process_video(args): + device = 'cpu' if args.cpu else 'cuda' + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False, device=device) + video = imageio.get_reader(args.inp) + + trajectories = [] + previous_frame = None + fps = video.get_meta_data()['fps'] + commands = [] + try: + for i, frame in tqdm(enumerate(video)): + frame_shape = frame.shape + bboxes = extract_bbox(frame, fa) + ## For each trajectory check the criterion + not_valid_trajectories = [] + valid_trajectories = [] + + for trajectory in trajectories: + tube_bbox = trajectory[0] + intersection = 0 + for bbox in bboxes: + intersection = max(intersection, bb_intersection_over_union(tube_bbox, bbox)) + if intersection > args.iou_with_initial: + valid_trajectories.append(trajectory) + else: + not_valid_trajectories.append(trajectory) + + commands += compute_bbox_trajectories(not_valid_trajectories, fps, frame_shape, args) + trajectories = valid_trajectories + + ## Assign bbox to trajectories, create new trajectories + for bbox in bboxes: + intersection = 0 + current_trajectory = None + for trajectory in trajectories: + tube_bbox = trajectory[0] + current_intersection = bb_intersection_over_union(tube_bbox, bbox) + if intersection < current_intersection and current_intersection > args.iou_with_initial: + intersection = bb_intersection_over_union(tube_bbox, bbox) + current_trajectory = trajectory + + ## Create new trajectory + if current_trajectory is None: + trajectories.append([bbox, bbox, i, i]) + else: + current_trajectory[3] = i + current_trajectory[1] = join(current_trajectory[1], bbox) + + + except IndexError as e: + raise (e) + + commands += compute_bbox_trajectories(trajectories, fps, frame_shape, args) + return commands + + +if __name__ == "__main__": + parser = ArgumentParser() + + parser.add_argument("--image_shape", default=(256, 256), type=lambda x: tuple(map(int, x.split(','))), + help="Image shape") + parser.add_argument("--increase", default=0.1, type=float, help='Increase bbox by this amount') + parser.add_argument("--iou_with_initial", type=float, default=0.25, help="The minimal allowed iou with inital bbox") + parser.add_argument("--inp", required=True, help='Input image or video') + parser.add_argument("--min_frames", type=int, default=150, help='Minimum number of frames') + parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.") + + + args = parser.parse_args() + + commands = process_video(args) + for command in commands: + print (command) + + \ No newline at end of file diff --git a/dagan/cut.py b/dagan/cut.py new file mode 100644 index 00000000..694e1699 --- /dev/null +++ b/dagan/cut.py @@ -0,0 +1,50 @@ +import cv2 +import numpy as np + + +def crop_video(input_path, output_path, target_size=(256, 256)): + # 读取视频 + cap = cv2.VideoCapture(input_path) + + # 获取原视频的属性 + frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = int(cap.get(cv2.CAP_PROP_FPS)) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + # 计算裁剪的起始位置（居中裁剪） + start_x = (frame_width - target_size[0]) // 2 + start_y = (frame_height - target_size[1]) // 2 + + # 创建视频写入器 + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + out = cv2.VideoWriter(output_path, fourcc, fps, target_size) + + # 处理每一帧 + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + + # 裁剪帧 + cropped_frame = frame[start_y:start_y + target_size[1], + start_x:start_x + target_size[0]] + + # 如果裁剪后的尺寸不正确，则调整大小 + if cropped_frame.shape[:2] != target_size: + cropped_frame = cv2.resize(cropped_frame, target_size) + + # 写入帧 + out.write(cropped_frame) + + # 释放资源 + cap.release() + out.release() + cv2.destroyAllWindows() + + +# 使用示例 +if __name__ == "__main__": + input_video = "/root/autodl-tmp/videos/Jae-in.mp4" # 输入视频路径 + output_video = "assets/Jae.mp4" # 输出视频路径 + crop_video(input_video, output_video) \ No newline at end of file diff --git a/dagan/data/celeV_cross_id_evaluation.csv b/dagan/data/celeV_cross_id_evaluation.csv new file mode 100644 index 00000000..146b4de6 --- /dev/null +++ b/dagan/data/celeV_cross_id_evaluation.csv @@ -0,0 +1,2084 @@ +source,driving,frame 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idxxx#xxx#xxx.txt#xxx.mp4 格式命名的文件夹 +output_dir = '/root/autodl-tmp/dagan/data' # 输出 CSV 文件的目录 + +# 获取所有文件夹内的 PNG 文件，并按 ID 分类 +def get_files(directory): + file_dict = {} + for folder in os.listdir(directory): + folder_path = os.path.join(directory, folder) + if os.path.isdir(folder_path): # 确保是文件夹 + files = [] + for file in os.listdir(folder_path): + if file.endswith('.png'): # 只考虑 PNG 文件 + files.append(file) + if len(files) >= 2: # 只处理每个文件夹下有至少两张图片的情况 + file_dict[folder] = files + return file_dict + +# 获取 test 文件夹中的图像文件 +file_dict = get_files(test_dir) + +# 获取所有有效的文件夹 ID（即每个 ID 下至少有两张图像） +valid_ids = list(file_dict.keys()) + +pairs = [] + +while len(pairs) < 2000: + # 随机选择两个不同的 ID + source_id, driving_id = random.sample(valid_ids, 2) + + # 获取 source_self 和 driving 的图像文件列表 + source_files = file_dict[source_id] + driving_files = file_dict[driving_id] + + # 随机选择图像 + source_file = random.choice(source_files) + driving_file = random.choice(driving_files) + + # 生成相对路径并添加到图像对列表 + source_path = os.path.join(source_id, source_file) + driving_path = os.path.join(driving_id, driving_file) + pairs.append([source_path, driving_path]) + + +# 创建输出目录 +def create_dir(path): + if not os.path.exists(path): + os.makedirs(path) + +# 将图像对保存到 CSV 文件 +def save_to_csv(pairs, output_dir): + create_dir(output_dir) + file_path = os.path.join(output_dir, 'cross_identity_pairs.csv') + + with open(file_path, mode='w', newline='', encoding='utf-8') as file: + writer = csv.writer(file) + writer.writerow(['source_self', 'driving']) # 写入 CSV 的标题行 + for pair in pairs: + writer.writerow(pair) + + print(f"交叉验证集的 CSV 文件已生成：{file_path}") + +# 保存选定的 2000 对数据到 CSV +save_to_csv(pairs, output_dir) diff --git a/dagan/data/cross_identity_pairs.csv b/dagan/data/cross_identity_pairs.csv new file mode 100644 index 00000000..b1c3c682 --- /dev/null +++ b/dagan/data/cross_identity_pairs.csv @@ -0,0 +1,2001 @@ +source,driving 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+id10291#aQ6uxeakacs#00002.txt#000.mp4/0000025.png,id10283#j8UugkSTzzk#00004.txt#000.mp4/0000546.png +id10289#Rn0Z_lIiL1w#00001.txt#000.mp4/0000006.png,id10292#57KbI2UvGss#00002.txt#000.mp4/0000048.png diff --git a/dagan/data/self_evaluation_csv.py b/dagan/data/self_evaluation_csv.py new file mode 100644 index 00000000..88ab025f --- /dev/null +++ b/dagan/data/self_evaluation_csv.py @@ -0,0 +1,62 @@ +import os +import random +import csv +from pathlib import Path + + +def generate_self_reenactment_pairs(data_root, output_path, total_pairs=2000): + """ + 为每个视频生成自身重构的图像对,总数限制在2000对 + Args: + data_root: 数据根目录 + output_path: 输出csv文件路径 + total_pairs: 需要生成的总对数 + """ + pairs = [] + video_dirs = list(Path(data_root).glob("id*")) + + # 计算每个视频需要生成的对数 + pairs_per_video = total_pairs // len(video_dirs) + remaining_pairs = total_pairs % len(video_dirs) + + for video_dir in video_dirs: + if not video_dir.is_dir(): + continue + + # 获取该视频下所有图片 + images = sorted([str(x.relative_to(data_root)) for x in video_dir.glob("*.png")]) + if len(images) < 2: + continue + + # 确定这个视频需要生成的对数 + num_pairs = pairs_per_video + if remaining_pairs > 0: + num_pairs += 1 + remaining_pairs -= 1 + + # 随机生成图像对 + for _ in range(num_pairs): + # 从同一视频中随机选择两帧 + source, driving = random.sample(images, 2) + pairs.append([source, driving]) + + if len(pairs) >= total_pairs: + break + + # 如果生成的对数超过了要求,随机删除多余的 + if len(pairs) > total_pairs: + pairs = random.sample(pairs, total_pairs) + + # 写入CSV文件 + with open(output_path, 'w', newline='') as f: + writer = csv.writer(f) + writer.writerow(['source_self', 'driving']) # 写入header + writer.writerows(pairs) + + print(f"Generated {len(pairs)} pairs in total") + return pairs + +if __name__ == "__main__": + data_root = "/root/autodl-tmp/vox-png/test" # 替换为你的数据目录 + output_path = "self_reenactment_pairs.csv" + generate_self_reenactment_pairs(data_root, output_path, total_pairs=2000) \ No newline at end of file diff --git a/dagan/data/self_reenactment_pairs.csv 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source, driving = pair.split(',') + sources.append(source) + drivings.append(driving) + + # 创建DataFrame + df = pd.DataFrame({ + 'source_self': sources, + 'driving': drivings + }) + + # 处理每一对图像 + for idx, row in df.iterrows(): + # 构建源文件路径 + source_path = os.path.join(base_path, row['source_self']) + driving_path = os.path.join(base_path, row['driving']) + + # 构建目标文件路径 + source_output_path = os.path.join(source_output_dir, f'img{idx:05d}.png') + driving_output_path = os.path.join(driving_output_dir, f'img{idx:05d}.png') + + # 复制文件 + try: + shutil.copy2(source_path, source_output_path) + print(f"Copied source_self image {idx}: {source_path} -> {source_output_path}") + except Exception as e: + print(f"Error copying source_self image {idx}: {e}") + + try: + shutil.copy2(driving_path, driving_output_path) + print(f"Copied driving image {idx}: {driving_path} -> {driving_output_path}") + except Exception as e: + print(f"Error copying driving image {idx}: {e}") + + +def main(): + # 设置路径 + base_path = r"E:\grade3-1\voice\dataset\vox\face-video-preprocessing\vox-png\test" + source_output_dir = r"E:\grade3-1\dagan_docker\evaluation_set\source_cross" + driving_output_dir = r"E:\grade3-1\dagan_docker\evaluation_set\gt_cross" + csv_path = r"E:\grade3-1\dagan_docker\data\cross_identity_pairs.csv" + + # 创建必要的目录 + create_directories(source_output_dir, driving_output_dir) + + # 处理图像对 + process_image_pairs(csv_path, base_path, source_output_dir, driving_output_dir) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/dagan/data/vox256.csv b/dagan/data/vox256.csv new file mode 100644 index 00000000..b202041c --- /dev/null +++ b/dagan/data/vox256.csv @@ -0,0 +1,526 @@ +source,driving,frame +id10286#9K2YB1d8BqY#00008.txt#000.mp4,id10292#0zvzJa5HOUQ#00003.txt#000.mp4,0.0 +id10283#u3s9xdUlmmk#00001.txt#000.mp4,id10286#isKyMAYUOgg#00008.txt#000.mp4,0.0 +id10289#3g9CjhcNEWk#00011.txt#000.mp4,id10292#ENIHEvg_VLM#00016.txt#000.mp4,0.0 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structure in root/autodl-tmp/vox-png +2024-12-08 10:39:48,082 - ERROR - Missing required directory: root/autodl-tmp/vox-png/train +2024-12-08 10:39:48,082 - ERROR - Dataset structure check failed! diff --git a/dagan/demo.py b/dagan/demo.py new file mode 100644 index 00000000..73912bdb --- /dev/null +++ b/dagan/demo.py @@ -0,0 +1,220 @@ +import matplotlib +matplotlib.use('Agg') +import os, sys +import yaml +from argparse import ArgumentParser +from tqdm import tqdm +import modules.generator as GEN +import imageio +import numpy as np +from skimage.transform import resize +from skimage import img_as_ubyte +import torch +from sync_batchnorm import DataParallelWithCallback +import depth +from modules.keypoint_detector import KPDetector +from animate import normalize_kp +from scipy.spatial import ConvexHull +from collections import OrderedDict +import pdb +import cv2 +if sys.version_info[0] < 3: + raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7") + +def load_checkpoints(config_path, checkpoint_path, cpu=False): + + with open(config_path) as f: + config = yaml.load(f) + if opt.kp_num != -1: + config['model_params']['common_params']['num_kp'] = opt.kp_num + generator = getattr(GEN, opt.generator)(**config['model_params']['generator_params'],**config['model_params']['common_params']) + if not cpu: + generator.cuda() + config['model_params']['common_params']['num_channels'] = 4 + kp_detector = KPDetector(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + if not cpu: + kp_detector.cuda() + if cpu: + checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) + else: + checkpoint = torch.load(checkpoint_path,map_location="cuda:0") + + ckp_generator = OrderedDict((k.replace('module.',''),v) for k,v in checkpoint['generator'].items()) + generator.load_state_dict(ckp_generator) + ckp_kp_detector = OrderedDict((k.replace('module.',''),v) for k,v in checkpoint['kp_detector'].items()) + kp_detector.load_state_dict(ckp_kp_detector) + + if not cpu: + generator = DataParallelWithCallback(generator) + kp_detector = DataParallelWithCallback(kp_detector) + + generator.eval() + kp_detector.eval() + + return generator, kp_detector + + +def make_animation(source_image, driving_video, generator, kp_detector, relative=True, adapt_movement_scale=True, cpu=False): + sources = [] + drivings = [] + with torch.no_grad(): + predictions = [] + depth_gray = [] + source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) + driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3) + if not cpu: + source = source.cuda() + driving = driving.cuda() + outputs = depth_decoder(depth_encoder(source)) + depth_source = outputs[("disp", 0)] + + outputs = depth_decoder(depth_encoder(driving[:, :, 0])) + depth_driving = outputs[("disp", 0)] + source_kp = torch.cat((source,depth_source),1) + driving_kp = torch.cat((driving[:, :, 0],depth_driving),1) + + kp_source = kp_detector(source_kp) + kp_driving_initial = kp_detector(driving_kp) + + # kp_source = kp_detector(source) + # kp_driving_initial = kp_detector(driving[:, :, 0]) + + for frame_idx in tqdm(range(driving.shape[2])): + driving_frame = driving[:, :, frame_idx] + + if not cpu: + driving_frame = driving_frame.cuda() + outputs = depth_decoder(depth_encoder(driving_frame)) + depth_map = outputs[("disp", 0)] + + gray_driving = np.transpose(depth_map.data.cpu().numpy(), [0, 2, 3, 1])[0] + gray_driving = 1-gray_driving/np.max(gray_driving) + + frame = torch.cat((driving_frame,depth_map),1) + kp_driving = kp_detector(frame) + + kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving, + kp_driving_initial=kp_driving_initial, use_relative_movement=relative, + use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale) + out = generator(source, kp_source=kp_source, kp_driving=kp_norm,source_depth = depth_source, driving_depth = depth_map) + + drivings.append(np.transpose(driving_frame.data.cpu().numpy(), [0, 2, 3, 1])[0]) + sources.append(np.transpose(source.data.cpu().numpy(), [0, 2, 3, 1])[0]) + predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) + depth_gray.append(gray_driving) + return sources, drivings, predictions,depth_gray + + +def find_best_frame(source, driving, cpu=False): + import face_alignment + + def normalize_kp(kp): + kp = kp - kp.mean(axis=0, keepdims=True) + area = ConvexHull(kp[:, :2]).volume + area = np.sqrt(area) + kp[:, :2] = kp[:, :2] / area + return kp + + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True, + device='cpu' if cpu else 'cuda') + kp_source = fa.get_landmarks(255 * source)[0] + kp_source = normalize_kp(kp_source) + norm = float('inf') + frame_num = 0 + for i, image in tqdm(enumerate(driving)): + kp_driving = fa.get_landmarks(255 * image)[0] + kp_driving = normalize_kp(kp_driving) + new_norm = (np.abs(kp_source - kp_driving) ** 2).sum() + if new_norm < norm: + norm = new_norm + frame_num = i + return frame_num + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--config", required=True, help="path to config") + parser.add_argument("--checkpoint", default='vox-cpk.pth.tar', help="path to checkpoint to restore") + + parser.add_argument("--source_image", default='sup-mat/source.png', help="path to source image") + parser.add_argument("--driving_video", default='sup-mat/source.png', help="path to driving video") + parser.add_argument("--result_video", default='result.mp4', help="path to output") + + parser.add_argument("--relative", dest="relative", action="store_true", help="use relative or absolute keypoint coordinates") + parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true", help="adapt movement scale based on convex hull of keypoints") + parser.add_argument("--generator", type=str, required=True) + parser.add_argument("--kp_num", type=int, required=True) + + + parser.add_argument("--find_best_frame", dest="find_best_frame", action="store_true", + help="Generate from the frame that is the most alligned with source. (Only for faces, requires face_aligment lib)") + + parser.add_argument("--best_frame", dest="best_frame", type=int, default=None, + help="Set frame to start from.") + + parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.") + + + parser.set_defaults(relative=False) + parser.set_defaults(adapt_scale=False) + + opt = parser.parse_args() + + depth_encoder = depth.ResnetEncoder(18, False) + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)) + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + if not opt.cpu: + depth_encoder.cuda() + depth_decoder.cuda() + + source_image = imageio.imread(opt.source_image) + reader = imageio.get_reader(opt.driving_video) + fps = reader.get_meta_data()['fps'] + driving_video = [] + try: + for im in reader: + driving_video.append(im) + except RuntimeError: + pass + reader.close() + + source_image = resize(source_image, (256, 256))[..., :3] + driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video] + generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) + + if opt.find_best_frame or opt.best_frame is not None: + i = opt.best_frame if opt.best_frame is not None else find_best_frame(source_image, driving_video, cpu=opt.cpu) + print ("Best frame: " + str(i)) + driving_forward = driving_video[i:] + driving_backward = driving_video[:(i+1)][::-1] + sources_forward, drivings_forward, predictions_forward,depth_forward = make_animation(source_image, driving_forward, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu) + sources_backward, drivings_backward, predictions_backward,depth_backward = make_animation(source_image, driving_backward, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu) + predictions = predictions_backward[::-1] + predictions_forward[1:] + sources = sources_backward[::-1] + sources_forward[1:] + drivings = drivings_backward[::-1] + drivings_forward[1:] + depth_gray = depth_backward[::-1] + depth_forward[1:] + + else: + # predictions = make_animation(source_image, driving_video, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu) + sources, drivings, predictions,depth_gray = make_animation(source_image, driving_video, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu) + imageio.mimsave(opt.result_video, [img_as_ubyte(p) for p in predictions], fps=fps) + # imageio.mimsave(opt.result_video, [np.concatenate((img_as_ubyte(s),img_as_ubyte(d),img_as_ubyte(p)),1) for (s,d,p) in zip(sources, drivings, predictions)], fps=fps) + # imageio.mimsave("gray.mp4", depth_gray, fps=fps) + # merge the gray video + # animation = np.array(imageio.mimread(opt.result_video,memtest=False)) + # gray = np.array(imageio.mimread("gray.mp4",memtest=False)) + + # src_dst = animation[:,:,:512,:] + # animate = animation[:,:,512:,:] + # merge = np.concatenate((src_dst,gray,animate),2) + # imageio.mimsave(opt.result_video, animate, fps=fps) + #Transfer to gif + # from moviepy.editor import * + # clip = (VideoFileClip(opt.result_video)) + # clip.write_gif("{}.gif".format(opt.result_video)) \ No newline at end of file diff --git a/dagan/demo_img.py b/dagan/demo_img.py new file mode 100644 index 00000000..223b88e2 --- /dev/null +++ b/dagan/demo_img.py @@ -0,0 +1,155 @@ +import matplotlib + +matplotlib.use('Agg') +import os, sys +import yaml +from argparse import ArgumentParser +from tqdm import tqdm +import modules.generator as GEN +import imageio +import numpy as np +from skimage.transform import resize +from skimage import img_as_ubyte +import torch +from sync_batchnorm import DataParallelWithCallback +import depth +from modules.keypoint_detector import KPDetector +from animate import normalize_kp +from scipy.spatial import ConvexHull +from collections import OrderedDict +import pdb +import cv2 +from glob import glob + +if sys.version_info[0] < 3: + raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7") + + +def load_checkpoints(config_path, checkpoint_path, cpu=False): + with open(config_path) as f: + config = yaml.load(f, Loader=yaml.FullLoader) + if opt.kp_num != -1: + config['model_params']['common_params']['num_kp'] = opt.kp_num + generator = getattr(GEN, opt.generator)(**config['model_params']['generator_params'], + **config['model_params']['common_params']) + if not cpu: + generator.cuda() + config['model_params']['common_params']['num_channels'] = 4 + kp_detector = KPDetector(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + if not cpu: + kp_detector.cuda() + if cpu: + checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) + else: + checkpoint = torch.load(checkpoint_path, map_location="cuda:0") + + ckp_generator = OrderedDict((k.replace('module.', ''), v) for k, v in checkpoint['generator'].items()) + generator.load_state_dict(ckp_generator) + ckp_kp_detector = OrderedDict((k.replace('module.', ''), v) for k, v in checkpoint['kp_detector'].items()) + kp_detector.load_state_dict(ckp_kp_detector) + + if not cpu: + generator = DataParallelWithCallback(generator) + kp_detector = DataParallelWithCallback(kp_detector) + + generator.eval() + kp_detector.eval() + + return generator, kp_detector + + +def make_animation(source_image, driving_image, generator, kp_detector, relative=True, adapt_movement_scale=True, + cpu=False): + sources = [] + drivings = [] + predictions = [] + with torch.no_grad(): + source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) + driving = torch.tensor(driving_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) + + if not cpu: + source = source.cuda() + driving = driving.cuda() + + # Depth estimation (for both source and driving) + outputs = depth_decoder(depth_encoder(source)) + depth_source = outputs[("disp", 0)] + + outputs = depth_decoder(depth_encoder(driving)) + depth_driving = outputs[("disp", 0)] + + source_kp = torch.cat((source, depth_source), 1) + driving_kp = torch.cat((driving, depth_driving), 1) + + kp_source = kp_detector(source_kp) + kp_driving_initial = kp_detector(driving_kp) + + kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving_initial, + kp_driving_initial=kp_driving_initial, use_relative_movement=relative, + use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale) + out = generator(source, kp_source=kp_source, kp_driving=kp_norm, source_depth=depth_source, + driving_depth=depth_driving) + + predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) + + return predictions + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--config", required=True, help="path to config") + parser.add_argument("--checkpoint", default='vox-cpk.pth.tar', help="path to checkpoint to restore") + parser.add_argument("--source_image", required=True, help="path to source image") + parser.add_argument("--driving_image", required=True, help="path to driving image") + parser.add_argument("--save_folder", default='results/', help="path to output folder") + parser.add_argument("--relative", dest="relative", action="store_true", + help="use relative or absolute keypoint coordinates") + parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true", + help="adapt movement scale based on convex hull of keypoints") + parser.add_argument("--generator", type=str, required=True) + parser.add_argument("--kp_num", type=int, required=True) + parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.") + + parser.set_defaults(relative=False) + parser.set_defaults(adapt_scale=False) + + opt = parser.parse_args() + + # Load depth models + depth_encoder = depth.ResnetEncoder(18, False) + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)) + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + if not opt.cpu: + depth_encoder.cuda() + depth_decoder.cuda() + + # Read source and driving images + source_image = imageio.imread(opt.source_image) + driving_image = imageio.imread(opt.driving_image) + + # Resize to 256x256 + source_image = resize(source_image, (256, 256))[..., :3] + driving_image = resize(driving_image, (256, 256))[..., :3] + + # Load generator and keypoint detector + generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) + + # Create output folder if doesn't exist + if not os.path.exists(opt.save_folder): + os.makedirs(opt.save_folder) + + # Make animation (generate a single output image) + predictions = make_animation(source_image, driving_image, generator, kp_detector, relative=opt.relative, + adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu) + + # Save the generated image + output_path = os.path.join(opt.save_folder, 'generated_image.png') + imageio.imwrite(output_path, img_as_ubyte(predictions[0])) # Save the first (and only) image + print(f"Generated image saved to {output_path}") diff --git a/dagan/demo_multi.py b/dagan/demo_multi.py new file mode 100644 index 00000000..be65fee0 --- /dev/null +++ b/dagan/demo_multi.py @@ -0,0 +1,199 @@ +import matplotlib +matplotlib.use('Agg') +import os, sys +import yaml +from argparse import ArgumentParser +from tqdm import tqdm +import modules.generator as GEN +import imageio +import numpy as np +from skimage.transform import resize +from skimage import img_as_ubyte +import torch +from sync_batchnorm import DataParallelWithCallback +import depth +from modules.keypoint_detector import KPDetector +from animate import normalize_kp +from scipy.spatial import ConvexHull +from collections import OrderedDict +import pdb +import cv2 +from glob import glob +if sys.version_info[0] < 3: + raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7") + +def load_checkpoints(config_path, checkpoint_path, cpu=False): + + with open(config_path) as f: + config = yaml.load(f,Loader=yaml.FullLoader) + if opt.kp_num != -1: + config['model_params']['common_params']['num_kp'] = opt.kp_num + generator = getattr(GEN, opt.generator)(**config['model_params']['generator_params'],**config['model_params']['common_params']) + if not cpu: + generator.cuda() + config['model_params']['common_params']['num_channels'] = 4 + kp_detector = KPDetector(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + if not cpu: + kp_detector.cuda() + if cpu: + checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) + else: + checkpoint = torch.load(checkpoint_path,map_location="cuda:0") + + ckp_generator = OrderedDict((k.replace('module.',''),v) for k,v in checkpoint['generator'].items()) + generator.load_state_dict(ckp_generator) + ckp_kp_detector = OrderedDict((k.replace('module.',''),v) for k,v in checkpoint['kp_detector'].items()) + kp_detector.load_state_dict(ckp_kp_detector) + + if not cpu: + generator = DataParallelWithCallback(generator) + kp_detector = DataParallelWithCallback(kp_detector) + + generator.eval() + kp_detector.eval() + + return generator, kp_detector + + +def make_animation(source_image, driving_video, generator, kp_detector, relative=True, adapt_movement_scale=True, cpu=False): + sources = [] + drivings = [] + with torch.no_grad(): + predictions = [] + depth_gray = [] + source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) + driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3) + if not cpu: + source = source.cuda() + driving = driving.cuda() + outputs = depth_decoder(depth_encoder(source)) + depth_source = outputs[("disp", 0)] + + outputs = depth_decoder(depth_encoder(driving[:, :, 0])) + depth_driving = outputs[("disp", 0)] + source_kp = torch.cat((source,depth_source),1) + driving_kp = torch.cat((driving[:, :, 0],depth_driving),1) + + kp_source = kp_detector(source_kp) + kp_driving_initial = kp_detector(driving_kp) + + # kp_source = kp_detector(source) + # kp_driving_initial = kp_detector(driving[:, :, 0]) + + for frame_idx in tqdm(range(driving.shape[2])): + driving_frame = driving[:, :, frame_idx] + + if not cpu: + driving_frame = driving_frame.cuda() + outputs = depth_decoder(depth_encoder(driving_frame)) + depth_map = outputs[("disp", 0)] + + gray_driving = np.transpose(depth_map.data.cpu().numpy(), [0, 2, 3, 1])[0] + gray_driving = 1-gray_driving/np.max(gray_driving) + + frame = torch.cat((driving_frame,depth_map),1) + kp_driving = kp_detector(frame) + + kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving, + kp_driving_initial=kp_driving_initial, use_relative_movement=relative, + use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale) + out = generator(source, kp_source=kp_source, kp_driving=kp_norm,source_depth = depth_source, driving_depth = depth_map) + + drivings.append(np.transpose(driving_frame.data.cpu().numpy(), [0, 2, 3, 1])[0]) + sources.append(np.transpose(source.data.cpu().numpy(), [0, 2, 3, 1])[0]) + predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) + depth_gray.append(gray_driving) + return sources, drivings, predictions,depth_gray + + +def find_best_frame(source, driving, cpu=False): + import face_alignment + + def normalize_kp(kp): + kp = kp - kp.mean(axis=0, keepdims=True) + area = ConvexHull(kp[:, :2]).volume + area = np.sqrt(area) + kp[:, :2] = kp[:, :2] / area + return kp + + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True, + device='cpu' if cpu else 'cuda') + kp_source = fa.get_landmarks(255 * source)[0] + kp_source = normalize_kp(kp_source) + norm = float('inf') + frame_num = 0 + for i, image in tqdm(enumerate(driving)): + kp_driving = fa.get_landmarks(255 * image)[0] + kp_driving = normalize_kp(kp_driving) + new_norm = (np.abs(kp_source - kp_driving) ** 2).sum() + if new_norm < norm: + norm = new_norm + frame_num = i + return frame_num + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--config", required=True, help="path to config") + parser.add_argument("--checkpoint", default='vox-cpk.pth.tar', help="path to checkpoint to restore") + + parser.add_argument("--source_folder", default='sup-mat/source.png', help="path to source image") + parser.add_argument("--driving_video", default='sup-mat/source.png', help="path to driving video") + parser.add_argument("--save_folder", default='result.mp4', help="path to output") + + parser.add_argument("--relative", dest="relative", action="store_true", help="use relative or absolute keypoint coordinates") + parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true", help="adapt movement scale based on convex hull of keypoints") + parser.add_argument("--generator", type=str, required=True) + parser.add_argument("--kp_num", type=int, required=True) + + + parser.add_argument("--find_best_frame", dest="find_best_frame", action="store_true", + help="Generate from the frame that is the most alligned with source. (Only for faces, requires face_aligment lib)") + + parser.add_argument("--best_frame", dest="best_frame", type=int, default=None, + help="Set frame to start from.") + + parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.") + + + parser.set_defaults(relative=False) + parser.set_defaults(adapt_scale=False) + + opt = parser.parse_args() + + depth_encoder = depth.ResnetEncoder(18, False) + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)) + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + if not opt.cpu: + depth_encoder.cuda() + depth_decoder.cuda() + + reader = imageio.get_reader(opt.driving_video) + fps = reader.get_meta_data()['fps'] + driving_video = [] + try: + for im in reader: + driving_video.append(im) + except RuntimeError: + pass + reader.close() + + driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video] + generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) + if not os.path.exists(opt.save_folder): + os.makedirs(opt.save_folder) + sources = glob(opt.source_folder+"/*") + for src in tqdm(sources): + source_image = imageio.imread(src) + source_image = resize(source_image, (256, 256))[..., :3] + # predictions = make_animation(source_image, driving_video, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu) + sources, drivings, predictions,depth_gray = make_animation(source_image, driving_video, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu) + fn = os.path.basename(src) + imageio.mimsave(os.path.join(opt.save_folder,fn+'.mp4'), [img_as_ubyte(p) for p in predictions], fps=fps) + \ No newline at end of file diff --git a/dagan/depth/__init__.py b/dagan/depth/__init__.py new file mode 100644 index 00000000..8be4160b --- /dev/null +++ b/dagan/depth/__init__.py @@ -0,0 +1,5 @@ +from .resnet_encoder import ResnetEncoder +from .depth_decoder import DepthDecoder +from .pose_decoder import PoseDecoder +from .pose_cnn import PoseCNN + diff --git a/dagan/depth/__pycache__/__init__.cpython-38.pyc b/dagan/depth/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 00000000..4b03a5b7 Binary files /dev/null and b/dagan/depth/__pycache__/__init__.cpython-38.pyc differ diff --git a/dagan/depth/__pycache__/depth_decoder.cpython-38.pyc b/dagan/depth/__pycache__/depth_decoder.cpython-38.pyc new file mode 100644 index 00000000..6c14222c Binary files /dev/null and b/dagan/depth/__pycache__/depth_decoder.cpython-38.pyc differ diff --git a/dagan/depth/__pycache__/layers.cpython-38.pyc b/dagan/depth/__pycache__/layers.cpython-38.pyc new file mode 100644 index 00000000..e7309e48 Binary files /dev/null and b/dagan/depth/__pycache__/layers.cpython-38.pyc differ diff --git a/dagan/depth/__pycache__/pose_cnn.cpython-38.pyc b/dagan/depth/__pycache__/pose_cnn.cpython-38.pyc new file mode 100644 index 00000000..ee2a1689 Binary files /dev/null and b/dagan/depth/__pycache__/pose_cnn.cpython-38.pyc differ diff --git a/dagan/depth/__pycache__/pose_decoder.cpython-38.pyc b/dagan/depth/__pycache__/pose_decoder.cpython-38.pyc new file mode 100644 index 00000000..77504275 Binary files /dev/null and b/dagan/depth/__pycache__/pose_decoder.cpython-38.pyc differ diff --git a/dagan/depth/__pycache__/resnet_encoder.cpython-38.pyc b/dagan/depth/__pycache__/resnet_encoder.cpython-38.pyc new file mode 100644 index 00000000..95e1fa5a Binary files /dev/null and b/dagan/depth/__pycache__/resnet_encoder.cpython-38.pyc differ diff --git a/dagan/depth/depth_decoder.py b/dagan/depth/depth_decoder.py new file mode 100644 index 00000000..efbdaf73 --- /dev/null +++ b/dagan/depth/depth_decoder.py @@ -0,0 +1,65 @@ +# Copyright Niantic 2019. Patent Pending. All rights reserved. +# +# This software is licensed under the terms of the Monodepth2 licence +# which allows for non-commercial use only, the full terms of which are made +# available in the LICENSE file. + +from __future__ import absolute_import, division, print_function + +import numpy as np +import torch +import torch.nn as nn + +from collections import OrderedDict +from depth.layers import * + + +class DepthDecoder(nn.Module): + def __init__(self, num_ch_enc, scales=range(4), num_output_channels=1, use_skips=True): + super(DepthDecoder, self).__init__() + + self.num_output_channels = num_output_channels + self.use_skips = use_skips + self.upsample_mode = 'nearest' + self.scales = scales + + self.num_ch_enc = num_ch_enc + self.num_ch_dec = np.array([16, 32, 64, 128, 256]) + + # decoder + self.convs = OrderedDict() + for i in range(4, -1, -1): + # upconv_0 + num_ch_in = self.num_ch_enc[-1] if i == 4 else self.num_ch_dec[i + 1] + num_ch_out = self.num_ch_dec[i] + self.convs[("upconv", i, 0)] = ConvBlock(num_ch_in, num_ch_out) + + # upconv_1 + num_ch_in = self.num_ch_dec[i] + if self.use_skips and i > 0: + num_ch_in += self.num_ch_enc[i - 1] + num_ch_out = self.num_ch_dec[i] + self.convs[("upconv", i, 1)] = ConvBlock(num_ch_in, num_ch_out) + + for s in self.scales: + self.convs[("dispconv", s)] = Conv3x3(self.num_ch_dec[s], self.num_output_channels) + + self.decoder = nn.ModuleList(list(self.convs.values())) + self.sigmoid = nn.Sigmoid() + + def forward(self, input_features): + self.outputs = {} + + # decoder + x = input_features[-1] + for i in range(4, -1, -1): + x = self.convs[("upconv", i, 0)](x) + x = [upsample(x)] + if self.use_skips and i > 0: + x += [input_features[i - 1]] + x = torch.cat(x, 1) + x = self.convs[("upconv", i, 1)](x) + if i in self.scales: + self.outputs[("disp", i)] = self.sigmoid(self.convs[("dispconv", i)](x)) + + return self.outputs diff --git a/dagan/depth/layers.py b/dagan/depth/layers.py new file mode 100644 index 00000000..ca793a59 --- /dev/null +++ b/dagan/depth/layers.py @@ -0,0 +1,285 @@ +# Copyright Niantic 2019. Patent Pending. All rights reserved. +# +# This software is licensed under the terms of the Monodepth2 licence +# which allows for non-commercial use only, the full terms of which are made +# available in the LICENSE file. + +from __future__ import absolute_import, division, print_function + +import numpy as np +import pdb +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def disp_to_depth(disp, min_depth, max_depth): + """Convert network's sigmoid output into depth prediction + The formula for this conversion is given in the 'additional considerations' + section of the paper. + """ + min_disp = 1 / max_depth + max_disp = 1 / min_depth + scaled_disp = min_disp + (max_disp - min_disp) * disp + depth = 1 / scaled_disp + return scaled_disp, depth + + +def transformation_from_parameters(axisangle, translation, invert=False): + """Convert the network's (axisangle, translation) output into a 4x4 matrix + """ + R = rot_from_axisangle(axisangle) + t = translation.clone() + + if invert: + R = R.transpose(1, 2) + t *= -1 + + T = get_translation_matrix(t) + + if invert: + M = torch.matmul(R, T) + else: + M = torch.matmul(T, R) + + return M + + +def get_translation_matrix(translation_vector): + """Convert a translation vector into a 4x4 transformation matrix + """ + T = torch.zeros(translation_vector.shape[0], 4, 4).to(device=translation_vector.device) + + t = translation_vector.contiguous().view(-1, 3, 1) + + T[:, 0, 0] = 1 + T[:, 1, 1] = 1 + T[:, 2, 2] = 1 + T[:, 3, 3] = 1 + T[:, :3, 3, None] = t + + return T + + +def rot_from_axisangle(vec): + """Convert an axisangle rotation into a 4x4 transformation matrix + (adapted from https://github.com/Wallacoloo/printipi) + Input 'vec' has to be Bx1x3 + """ + angle = torch.norm(vec, 2, 2, True) + axis = vec / (angle + 1e-7) + + ca = torch.cos(angle) + sa = torch.sin(angle) + C = 1 - ca + + x = axis[..., 0].unsqueeze(1) + y = axis[..., 1].unsqueeze(1) + z = axis[..., 2].unsqueeze(1) + + xs = x * sa + ys = y * sa + zs = z * sa + xC = x * C + yC = y * C + zC = z * C + xyC = x * yC + yzC = y * zC + zxC = z * xC + + rot = torch.zeros((vec.shape[0], 4, 4)).to(device=vec.device) + + rot[:, 0, 0] = torch.squeeze(x * xC + ca) + rot[:, 0, 1] = torch.squeeze(xyC - zs) + rot[:, 0, 2] = torch.squeeze(zxC + ys) + rot[:, 1, 0] = torch.squeeze(xyC + zs) + rot[:, 1, 1] = torch.squeeze(y * yC + ca) + rot[:, 1, 2] = torch.squeeze(yzC - xs) + rot[:, 2, 0] = torch.squeeze(zxC - ys) + rot[:, 2, 1] = torch.squeeze(yzC + xs) + rot[:, 2, 2] = torch.squeeze(z * zC + ca) + rot[:, 3, 3] = 1 + + return rot + + +class ConvBlock(nn.Module): + """Layer to perform a convolution followed by ELU + """ + def __init__(self, in_channels, out_channels): + super(ConvBlock, self).__init__() + + self.conv = Conv3x3(in_channels, out_channels) + self.nonlin = nn.ELU(inplace=True) + + def forward(self, x): + out = self.conv(x) + out = self.nonlin(out) + return out + + +class Conv3x3(nn.Module): + """Layer to pad and convolve input + """ + def __init__(self, in_channels, out_channels, use_refl=True): + super(Conv3x3, self).__init__() + + if use_refl: + self.pad = nn.ReflectionPad2d(1) + else: + self.pad = nn.ZeroPad2d(1) + self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3) + + def forward(self, x): + out = self.pad(x) + out = self.conv(out) + return out + + +class BackprojectDepth(nn.Module): + """Layer to transform a depth image into a point cloud + """ + def __init__(self, batch_size, height, width): + super(BackprojectDepth, self).__init__() + + self.batch_size = batch_size + self.height = height + self.width = width + + meshgrid = np.meshgrid(range(self.width), range(self.height), indexing='xy') + self.id_coords = np.stack(meshgrid, axis=0).astype(np.float32) + self.id_coords = nn.Parameter(torch.from_numpy(self.id_coords), + requires_grad=False) + + self.ones = nn.Parameter(torch.ones(self.batch_size, 1, self.height * self.width), + requires_grad=False) + + self.pix_coords = torch.unsqueeze(torch.stack( + [self.id_coords[0].view(-1), self.id_coords[1].view(-1)], 0), 0) + self.pix_coords = self.pix_coords.repeat(batch_size, 1, 1) + self.pix_coords = nn.Parameter(torch.cat([self.pix_coords, self.ones], 1), + requires_grad=False) + + def forward(self, depth, K,scale): + K[:,:2,:] = (K[:,:2,:]/(2 ** scale)).trunc() + b,n,n = K.shape + inv_K = torch.linalg.inv(K) + #inv_K = torch.cholesky_inverse(K) + pad = torch.tensor([0.0,0.0,0.0]).view(1,3,1).expand(b,3,1).cuda() + inv_K = torch.cat([inv_K,pad],-1) + pad = torch.tensor([0.0,0.0,0.0,1.0]).view(1,1,4).expand(b,1,4).cuda() + inv_K = torch.cat([inv_K,pad],1) + cam_points = torch.matmul(inv_K[:, :3, :3], self.pix_coords) + cam_points = depth.view(self.batch_size, 1, -1) * cam_points + cam_points = torch.cat([cam_points, self.ones], 1) + + return cam_points + + +class Project3D(nn.Module): + """Layer which projects 3D points into a camera with intrinsics K and at position T + """ + def __init__(self, batch_size, height, width, eps=1e-7): + super(Project3D, self).__init__() + + self.batch_size = batch_size + self.height = height + self.width = width + self.eps = eps + + def forward(self, points, K, T,scale=0): + # K[0, :] *= self.width // (2 ** scale) + # K[1, :] *= self.height // (2 ** scale) + K[:,:2,:] = (K[:,:2,:]/(2 ** scale)).trunc() + b,n,n = K.shape + pad = torch.tensor([0.0,0.0,0.0]).view(1,3,1).expand(b,3,1).cuda() + K = torch.cat([K,pad],-1) + pad = torch.tensor([0.0,0.0,0.0,1.0]).view(1,1,4).expand(b,1,4).cuda() + K = torch.cat([K,pad],1) + P = torch.matmul(K, T)[:, :3, :] + + cam_points = torch.matmul(P, points) + + pix_coords = cam_points[:, :2, :] / (cam_points[:, 2, :].unsqueeze(1) + self.eps) + pix_coords = pix_coords.view(self.batch_size, 2, self.height, self.width) + pix_coords = pix_coords.permute(0, 2, 3, 1) + pix_coords[..., 0] /= self.width - 1 + pix_coords[..., 1] /= self.height - 1 + pix_coords = (pix_coords - 0.5) * 2 + return pix_coords + + +def upsample(x): + """Upsample input tensor by a factor of 2 + """ + return F.interpolate(x, scale_factor=2, mode="nearest") + + +def get_smooth_loss(disp, img): + """Computes the smoothness loss for a disparity image + The color image is used for edge-aware smoothness + """ + grad_disp_x = torch.abs(disp[:, :, :, :-1] - disp[:, :, :, 1:]) + grad_disp_y = torch.abs(disp[:, :, :-1, :] - disp[:, :, 1:, :]) + + grad_img_x = torch.mean(torch.abs(img[:, :, :, :-1] - img[:, :, :, 1:]), 1, keepdim=True) + grad_img_y = torch.mean(torch.abs(img[:, :, :-1, :] - img[:, :, 1:, :]), 1, keepdim=True) + + grad_disp_x *= torch.exp(-grad_img_x) + grad_disp_y *= torch.exp(-grad_img_y) + + return grad_disp_x.mean() + grad_disp_y.mean() + + +class SSIM(nn.Module): + """Layer to compute the SSIM loss between a pair of images + """ + def __init__(self): + super(SSIM, self).__init__() + self.mu_x_pool = nn.AvgPool2d(3, 1) + self.mu_y_pool = nn.AvgPool2d(3, 1) + self.sig_x_pool = nn.AvgPool2d(3, 1) + self.sig_y_pool = nn.AvgPool2d(3, 1) + self.sig_xy_pool = nn.AvgPool2d(3, 1) + + self.refl = nn.ReflectionPad2d(1) + + self.C1 = 0.01 ** 2 + self.C2 = 0.03 ** 2 + + def forward(self, x, y): + x = self.refl(x) + y = self.refl(y) + + mu_x = self.mu_x_pool(x) + mu_y = self.mu_y_pool(y) + + sigma_x = self.sig_x_pool(x ** 2) - mu_x ** 2 + sigma_y = self.sig_y_pool(y ** 2) - mu_y ** 2 + sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y + + SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2) + SSIM_d = (mu_x ** 2 + mu_y ** 2 + self.C1) * (sigma_x + sigma_y + self.C2) + + return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1) + + +def compute_depth_errors(gt, pred): + """Computation of error metrics between predicted and ground truth depths + """ + thresh = torch.max((gt / pred), (pred / gt)) + a1 = (thresh < 1.25 ).float().mean() + a2 = (thresh < 1.25 ** 2).float().mean() + a3 = (thresh < 1.25 ** 3).float().mean() + + rmse = (gt - pred) ** 2 + rmse = torch.sqrt(rmse.mean()) + + rmse_log = (torch.log(gt) - torch.log(pred)) ** 2 + rmse_log = torch.sqrt(rmse_log.mean()) + + abs_rel = torch.mean(torch.abs(gt - pred) / gt) + + sq_rel = torch.mean((gt - pred) ** 2 / gt) + + return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3 diff --git a/dagan/depth/models/weights_19/depth.pth b/dagan/depth/models/weights_19/depth.pth new file mode 100644 index 00000000..ced68929 Binary files /dev/null and b/dagan/depth/models/weights_19/depth.pth differ diff --git a/dagan/depth/models/weights_19/encoder.pth b/dagan/depth/models/weights_19/encoder.pth new file mode 100644 index 00000000..8f32e1cf Binary files /dev/null and b/dagan/depth/models/weights_19/encoder.pth differ diff --git a/dagan/depth/pose_cnn.py b/dagan/depth/pose_cnn.py new file mode 100644 index 00000000..16baec78 --- /dev/null +++ b/dagan/depth/pose_cnn.py @@ -0,0 +1,50 @@ +# Copyright Niantic 2019. Patent Pending. All rights reserved. +# +# This software is licensed under the terms of the Monodepth2 licence +# which allows for non-commercial use only, the full terms of which are made +# available in the LICENSE file. + +from __future__ import absolute_import, division, print_function + +import torch +import torch.nn as nn + + +class PoseCNN(nn.Module): + def __init__(self, num_input_frames): + super(PoseCNN, self).__init__() + + self.num_input_frames = num_input_frames + + self.convs = {} + self.convs[0] = nn.Conv2d(3 * num_input_frames, 16, 7, 2, 3) + self.convs[1] = nn.Conv2d(16, 32, 5, 2, 2) + self.convs[2] = nn.Conv2d(32, 64, 3, 2, 1) + self.convs[3] = nn.Conv2d(64, 128, 3, 2, 1) + self.convs[4] = nn.Conv2d(128, 256, 3, 2, 1) + self.convs[5] = nn.Conv2d(256, 256, 3, 2, 1) + self.convs[6] = nn.Conv2d(256, 256, 3, 2, 1) + + self.pose_conv = nn.Conv2d(256, 6 * (num_input_frames - 1), 1) + + self.num_convs = len(self.convs) + + self.relu = nn.ReLU(True) + + self.net = nn.ModuleList(list(self.convs.values())) + + def forward(self, out): + + for i in range(self.num_convs): + out = self.convs[i](out) + out = self.relu(out) + + out = self.pose_conv(out) + out = out.mean(3).mean(2) + + out = 0.01 * out.view(-1, self.num_input_frames - 1, 1, 6) + + axisangle = out[..., :3] + translation = out[..., 3:] + + return axisangle, translation diff --git a/dagan/depth/pose_decoder.py b/dagan/depth/pose_decoder.py new file mode 100644 index 00000000..9d668021 --- /dev/null +++ b/dagan/depth/pose_decoder.py @@ -0,0 +1,71 @@ +# Copyright Niantic 2019. Patent Pending. All rights reserved. +# +# This software is licensed under the terms of the Monodepth2 licence +# which allows for non-commercial use only, the full terms of which are made +# available in the LICENSE file. + +from __future__ import absolute_import, division, print_function + +import torch +import torch.nn as nn +from collections import OrderedDict +import pdb +import torch.nn.functional as F +# from options import MonodepthOptions +# options = MonodepthOptions() +# opts = options.parse() +class PoseDecoder(nn.Module): + def __init__(self, num_ch_enc, num_input_features, num_frames_to_predict_for=None, stride=1): + super(PoseDecoder, self).__init__() + self.num_ch_enc = num_ch_enc + self.num_input_features = num_input_features + + if num_frames_to_predict_for is None: + num_frames_to_predict_for = num_input_features - 1 + self.num_frames_to_predict_for = num_frames_to_predict_for + + self.convs = OrderedDict() + self.convs[("squeeze")] = nn.Conv2d(self.num_ch_enc[-1], 256, 1) + self.convs[("pose", 0)] = nn.Conv2d(num_input_features * 256, 256, 3, stride, 1) + self.convs[("pose", 1)] = nn.Conv2d(256, 256, 3, stride, 1) + self.convs[("pose", 2)] = nn.Conv2d(256, 6 * num_frames_to_predict_for, 1) + self.convs[("intrinsics", 'focal')] = nn.Conv2d(256, 2, kernel_size = 3,stride = 1,padding = 1) + self.convs[("intrinsics", 'offset')] = nn.Conv2d(256, 2, kernel_size = 3,stride = 1,padding = 1) + + self.relu = nn.ReLU() + self.net = nn.ModuleList(list(self.convs.values())) + + def forward(self, input_features): + last_features = [f[-1] for f in input_features] + + cat_features = [self.relu(self.convs["squeeze"](f)) for f in last_features] + cat_features = torch.cat(cat_features, 1) + + feat = cat_features + for i in range(2): + feat = self.convs[("pose", i)](feat) + feat = self.relu(feat) + out = self.convs[("pose", 2)](feat) + + out = out.mean(3).mean(2) + out = 0.01 * out.view(-1, self.num_frames_to_predict_for, 1, 6) + + axisangle = out[..., :3] + translation = out[..., 3:] + + #add_intrinsics_head + scales = torch.tensor([256,256]).cuda() + focals = F.softplus(self.convs[("intrinsics", 'focal')](feat)).mean(3).mean(2)*scales + offset = (F.softplus(self.convs[("intrinsics", 'offset')](feat)).mean(3).mean(2)+0.5)*scales + #focals = F.softplus(self.convs[("intrinsics",'focal')](feat).mean(3).mean(2)) + #offset = F.softplus(self.convs[("intrinsics",'offset')](feat).mean(3).mean(2)) + eyes = torch.eye(2).cuda() + b,xy = focals.shape + focals = focals.unsqueeze(-1).expand(b,xy,xy) + eyes = eyes.unsqueeze(0).expand(b,xy,xy) + intrin = focals*eyes + offset = offset.view(b,2,1).contiguous() + intrin = torch.cat([intrin,offset],-1) + pad = torch.tensor([0.0,0.0,1.0]).view(1,1,3).expand(b,1,3).cuda() + intrinsics = torch.cat([intrin,pad],1) + return axisangle, translation,intrinsics diff --git a/dagan/depth/resnet_encoder.py b/dagan/depth/resnet_encoder.py new file mode 100644 index 00000000..9c94418d --- /dev/null +++ b/dagan/depth/resnet_encoder.py @@ -0,0 +1,98 @@ +# Copyright Niantic 2019. Patent Pending. All rights reserved. +# +# This software is licensed under the terms of the Monodepth2 licence +# which allows for non-commercial use only, the full terms of which are made +# available in the LICENSE file. + +from __future__ import absolute_import, division, print_function + +import numpy as np + +import torch +import torch.nn as nn +import torchvision.models as models +import torch.utils.model_zoo as model_zoo + + +class ResNetMultiImageInput(models.ResNet): + """Constructs a resnet model with varying number of input images. + Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py + """ + def __init__(self, block, layers, num_classes=1000, num_input_images=1): + super(ResNetMultiImageInput, self).__init__(block, layers) + self.inplanes = 64 + self.conv1 = nn.Conv2d( + num_input_images * 3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +def resnet_multiimage_input(num_layers, pretrained=False, num_input_images=1): + """Constructs a ResNet model. + Args: + num_layers (int): Number of resnet layers. Must be 18 or 50 + pretrained (bool): If True, returns a model pre-trained on ImageNet + num_input_images (int): Number of frames stacked as input + """ + assert num_layers in [18, 50], "Can only run with 18 or 50 layer resnet" + blocks = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers] + block_type = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers] + model = ResNetMultiImageInput(block_type, blocks, num_input_images=num_input_images) + + if pretrained: + loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)]) + loaded['conv1.weight'] = torch.cat( + [loaded['conv1.weight']] * num_input_images, 1) / num_input_images + model.load_state_dict(loaded) + return model + + +class ResnetEncoder(nn.Module): + """Pytorch module for a resnet encoder + """ + def __init__(self, num_layers, pretrained, num_input_images=1): + super(ResnetEncoder, self).__init__() + + self.num_ch_enc = np.array([64, 64, 128, 256, 512]) + + resnets = {18: models.resnet18, + 34: models.resnet34, + 50: models.resnet50, + 101: models.resnet101, + 152: models.resnet152} + + if num_layers not in resnets: + raise ValueError("{} is not a valid number of resnet layers".format(num_layers)) + + if num_input_images > 1: + self.encoder = resnet_multiimage_input(num_layers, pretrained, num_input_images) + else: + self.encoder = resnets[num_layers](pretrained) + + if num_layers > 34: + self.num_ch_enc[1:] *= 4 + + def forward(self, input_image): + self.features = [] + x = (input_image - 0.45) / 0.225 + x = self.encoder.conv1(x) + x = self.encoder.bn1(x) + self.features.append(self.encoder.relu(x)) + self.features.append(self.encoder.layer1(self.encoder.maxpool(self.features[-1]))) + self.features.append(self.encoder.layer2(self.features[-1])) + self.features.append(self.encoder.layer3(self.features[-1])) + self.features.append(self.encoder.layer4(self.features[-1])) + + return self.features diff --git a/dagan/evaluate_docker.py b/dagan/evaluate_docker.py new file mode 100644 index 00000000..61d82e48 --- /dev/null +++ b/dagan/evaluate_docker.py @@ -0,0 +1,451 @@ +import os +import yaml +import json +import psutil +import traceback +import sys +from datetime import datetime +import torch +from tqdm import tqdm +from argparse import ArgumentParser +from skimage.transform import resize +from skimage import img_as_ubyte +import modules.generator as GEN +from modules.keypoint_detector import KPDetector +import depth +from collections import OrderedDict +import numpy as np +from evaluation_metrics import EvaluationMetrics +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms +from PIL import Image +import matplotlib.pyplot as plt + + +def save_checkpoint(metrics, output_path, batch_idx): + """保存评估进度检查点""" + checkpoint_dir = os.path.join(output_path, 'checkpoints') + os.makedirs(checkpoint_dir, exist_ok=True) + + checkpoint_file = os.path.join(checkpoint_dir, f'checkpoint_batch_{batch_idx}.json') + + # 转换 NumPy 数组和其他特殊类型为普通 Python 类型 + def convert_for_json(obj): + if isinstance(obj, np.ndarray): + return obj.tolist() + elif isinstance(obj, (np.float32, np.float64)): + return float(obj) + elif isinstance(obj, list): + return [convert_for_json(item) for item in obj] + elif isinstance(obj, dict): + return {k: convert_for_json(v) for k, v in obj.items()} + return obj + + # 转换metrics为JSON可序列化格式 + json_metrics = {} + for gen_dir, gen_metrics in metrics.items(): + json_metrics[gen_dir] = { + k: convert_for_json(v) for k, v in gen_metrics.items() + } + + with open(checkpoint_file, 'w') as f: + json.dump(json_metrics, f, indent=4) + + +class EvaluationDataset(Dataset): + def __init__(self, base_dir, generate_dirs, transform=None, max_samples=None): + self.base_dir = base_dir + # 确保 generate_dirs 是字符串列表 + self.generate_dirs = [gen_dir for gen_dir in generate_dirs] + self.transform = transform + + # 初始化源目录和GT目录 + gen_dir = self.generate_dirs[0] # 使用第一个生成目录来决定使用哪个源和GT目录 + if '_self' in gen_dir: # 如果有 _self 后缀，使用 self 目录 + self.source_dir = os.path.join(base_dir, 'source_self') + self.gt_dir = os.path.join(base_dir, 'gt_self') + else: # 如果没有后缀或其他情况，使用 cross 目录 + self.source_dir = os.path.join(base_dir, 'source_cross') + self.gt_dir = os.path.join(base_dir, 'gt_cross') + + # 打印使用的目录信息 + print(f"源图像路径: {self.source_dir}") + print(f"驱动图像路径: {self.gt_dir}") + + # 检查目录是否存在 + if not os.path.exists(self.source_dir): + raise FileNotFoundError(f"Source directory not found: {self.source_dir}") + if not os.path.exists(self.gt_dir): + raise FileNotFoundError(f"GT directory not found: {self.gt_dir}") + + # 获取所有图像文件并排序 + self.image_files = sorted([f for f in os.listdir(self.source_dir) + if f.endswith(('.png', '.jpg', '.jpeg'))]) + + # 如果指定了最大样本数，限制数据集大小 + if max_samples is not None: + self.image_files = self.image_files[:max_samples] + print(f"使用前 {max_samples} 个样本进行评估") + + self._verify_files() + + def _verify_files(self): + """验证所有必需的图像文件是否存在""" + for img_file in self.image_files: + # 检查源图像 + source_path = os.path.join(self.source_dir, img_file) + if not os.path.exists(source_path): + raise FileNotFoundError(f"Missing source image: {source_path}") + + # 检查GT图像 + gt_path = os.path.join(self.gt_dir, img_file) + if not os.path.exists(gt_path): + raise FileNotFoundError(f"Missing GT image: {gt_path}") + + # 检查生成的图像 + for gen_dir in self.generate_dirs: + gen_path = os.path.join(self.base_dir, gen_dir, img_file) + if not os.path.exists(gen_path): + raise FileNotFoundError(f"Missing generated image: {gen_path}") + + def _load_image(self, path): + """加载并预处理图像""" + img = Image.open(path).convert('RGB') + if self.transform: + img = self.transform(img) + else: + transform = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + ]) + img = transform(img) + return img # 返回CPU张量 + + def __len__(self): + return len(self.image_files) + + def __getitem__(self, idx): + """获取数据集中的一项""" + img_name = self.image_files[idx] + + # 加载源图像和GT图像 + source = self._load_image(os.path.join(self.source_dir, img_name)) + driving = self._load_image(os.path.join(self.gt_dir, img_name)) + + # 加载所有生成的图像 + generated_images = [] + for gen_dir in self.generate_dirs: + gen_path = os.path.join(self.base_dir, gen_dir, img_name) + gen_img = self._load_image(gen_path) + generated_images.append(gen_img) + + return { + 'source': source, + 'driving': driving, + 'generated': generated_images, + 'filename': img_name, + 'generate_dirs': self.generate_dirs + } + +def create_comparison_grid(source_img, driving_img, generated_imgs, generate_dirs, save_path): + """创建包含源图像、驱动图像和所有生成图像的对比网格""" + + def prepare_img(img): + # 图像预处理：归一化到[0,1]范围 + img = (img + 1) / 2.0 + img = img_as_ubyte(img) + img = img.astype(np.float32) / 255.0 + return img + + source_img = prepare_img(source_img) + driving_img = prepare_img(driving_img) + generated_imgs = [prepare_img(img) for img in generated_imgs] + + # 计算网格尺寸 + n_generated = len(generated_imgs) + n_cols = n_generated + 2 # source + driving + generated + + # 创建图表 + fig, axes = plt.subplots(1, n_cols, figsize=(5 * n_cols, 5)) + if n_cols == 1: + axes = [axes] + + # 绘制源图像和驱动图像 + axes[0].imshow(source_img) + axes[0].set_title('Source') + axes[0].axis('off') + + axes[1].imshow(driving_img) + axes[1].set_title('Driving') + axes[1].axis('off') + + # 绘制生成的图像 + for idx, (gen_img, gen_name) in enumerate(zip(generated_imgs, generate_dirs)): + axes[idx + 2].imshow(gen_img) + axes[idx + 2].set_title(f'Generated\n{gen_name}') + axes[idx + 2].axis('off') + + plt.tight_layout() + plt.savefig(save_path) + plt.close() + + +def evaluate_with_monitoring(evaluator, data_loader, device, output_path, + checkpoint_freq=5, save_visuals=True): + if save_visuals: + vis_dir = os.path.join(output_path, 'visualizations') + os.makedirs(vis_dir, exist_ok=True) + + accumulated_metrics = {} + + pbar = tqdm(enumerate(data_loader), total=len(data_loader), + desc="Evaluation Progress", ncols=100, leave=True) + + for batch_idx, batch in pbar: + try: + # 添加调试输出 + # print(f"\nBatch {batch_idx} generate_dirs type: {type(batch['generate_dirs'])}") + # print(f"generate_dirs content: {batch['generate_dirs']}") + + source = batch['source'].to(device) + driving = batch['driving'].to(device) + generated_list = [gen.to(device) for gen in batch['generated']] + + # Ensure generate_dirs is a list of strings + generate_dirs = batch['generate_dirs'] + if isinstance(generate_dirs[0], list): + generate_dirs = generate_dirs[0] # Get the first element if it's a nested list + + # Initialize metrics for new generation methods + for gen_dir in generate_dirs: + gen_dir = str(gen_dir) # Ensure it's a string + if gen_dir not in accumulated_metrics: + accumulated_metrics[gen_dir] = { + 'niqe': [], 'psnr': [], 'ssim': [], + 'fid': [], 'lse_c': [], 'lse_d': [] + } + + source_image = source.cpu().numpy()[0].transpose(1, 2, 0) + driving_image = driving.cpu().numpy()[0].transpose(1, 2, 0) + generated_images = [gen.cpu().numpy()[0].transpose(1, 2, 0) + for gen in generated_list] + + source_image = np.clip(source_image, 0, 1) + driving_image = np.clip(driving_image, 0, 1) + generated_images = [np.clip(gen, 0, 1) for gen in generated_images] + + if save_visuals: + vis_path = os.path.join(vis_dir, f'comparison_batch_{batch_idx}.png') + create_comparison_grid(source_image, driving_image, + generated_images, generate_dirs, vis_path) + + # 确保图像尺寸足够大 + min_size = 32 # 设置最小尺寸 + if (source_image.shape[0] < min_size or source_image.shape[1] < min_size or + driving_image.shape[0] < min_size or driving_image.shape[1] < min_size): + raise ValueError(f"Image size too small. Minimum size required: {min_size}x{min_size}") + + for gen_idx, (gen_image, gen_dir) in enumerate(zip(generated_images, generate_dirs)): + if gen_image.shape[0] < min_size or gen_image.shape[1] < min_size: + raise ValueError(f"Generated image size too small. Minimum size required: {min_size}x{min_size}") + + try: + batch_metrics = evaluator.evaluate_batch(source_image, + driving_image, + gen_image) + # 确保gen_dir是字符串 + gen_dir = str(gen_dir) + for metric, value in batch_metrics.items(): + if isinstance(value, list): + accumulated_metrics[gen_dir][metric].extend(value) + else: + accumulated_metrics[gen_dir][metric].append(value) + except Exception as metric_error: + pbar.write(f"\nWarning: Error computing metrics for {gen_dir}: {str(metric_error)}") + + del source, driving, generated_list + torch.cuda.empty_cache() + + if (batch_idx + 1) % checkpoint_freq == 0: + save_checkpoint(accumulated_metrics, output_path, batch_idx) + + except Exception as e: + pbar.write(f"\nError in batch {batch_idx}: {str(e)}") + raise + + pbar.close() + return accumulated_metrics + + +def save_final_results(metrics, output_path): + """保存所有生成方法的评估结果 + Args: + metrics (dict): 评估指标字典 + output_path (str): 结果保存路径 + """ + + def convert_metrics(metric_value): + """转换度量值为Python原生类型 + Args: + metric_value: 需要转换的度量值 + Returns: + 转换后的度量值 + """ + if isinstance(metric_value, np.ndarray): + return metric_value.tolist() + elif isinstance(metric_value, (np.float32, np.float64)): + return float(metric_value) + elif isinstance(metric_value, list): + return [convert_metrics(item) for item in metric_value] + elif isinstance(metric_value, dict): + return {k: convert_metrics(v) for k, v in metric_value.items()} + elif hasattr(metric_value, 'item'): # 处理PyTorch张量 + return metric_value.item() + else: + return metric_value + + # 打印输入metrics的结构以便调试 + # print("\nInput metrics structure:") + # for gen_dir, gen_metrics in metrics.items(): + # print(f"\nProcessing metrics for {gen_dir}:") + # for metric_name, metric_values in gen_metrics.items(): + # print(f"{metric_name}: type={type(metric_values)}, value={metric_values}") + + # 计算每个生成方法的平均指标 + results = {} + for gen_dir, gen_metrics in metrics.items(): + avg_metrics = {} + for metric_name, metric_values in gen_metrics.items(): + if isinstance(metric_values, list) and metric_values: + # 对列表类型的指标取平均值 + valid_values = [v for v in metric_values if v is not None] + avg_metrics[metric_name] = float(np.mean(valid_values)) if valid_values else 0.0 + elif isinstance(metric_values, (float, int)): + # 对单个数值的指标直接使用 + avg_metrics[metric_name] = float(metric_values) + elif metric_values is not None: + # 处理其他非空情况 + try: + avg_metrics[metric_name] = float(metric_values) + except (TypeError, ValueError): + print(f"Warning: Unable to process metric {metric_name} with value {metric_values}") + avg_metrics[metric_name] = 0.0 + else: + # 处理None值情况 + avg_metrics[metric_name] = 0.0 + + results[gen_dir] = { + 'average_metrics': avg_metrics, + 'detailed_metrics': convert_metrics(gen_metrics) + } + + # 保存详细结果为JSON格式 + results_file = os.path.join(output_path, 'full_results.json') + with open(results_file, 'w') as f: + json.dump(results, f, indent=4) + + # 保存摘要结果为文本格式 + summary_file = os.path.join(output_path, 'evaluation_summary.txt') + with open(summary_file, 'w', encoding='utf-8') as f: + for gen_dir, gen_results in results.items(): + f.write(f"\n=== Evaluation Results for {gen_dir} ===\n") + + # 添加样本数量信息 + samples_count = len(metrics[gen_dir].get('niqe', [])) if isinstance(metrics[gen_dir].get('niqe'), + list) else 1 + f.write(f"Total samples processed: {samples_count}\n\n") + + for metric, value in gen_results['average_metrics'].items(): + # 根据指标类型使用不同的小数位数 + if metric in ['niqe', 'psnr', 'ssim']: + f.write(f"{metric}: {value:.4f}\n") + else: + f.write(f"{metric}: {value:.6f}\n") + f.write("\n") + + print(f"\n结果保存至:") + print(f"- 完整结果: {results_file}") + print(f"- 评估总结: {summary_file}") + + # 打印处理后的结果以便验证 + print("\nProcessed metrics:") + for gen_dir, gen_results in results.items(): + print(f"\n{gen_dir} average metrics:") + for metric, value in gen_results['average_metrics'].items(): + print(f"{metric}: {value}") + + return results + + +def main(): + parser = ArgumentParser() + parser.add_argument("--base_dir", required=True, + help="Base directory containing source/, gt/ and generate_*/ folders") + parser.add_argument("--output_path", default="evaluation_results", + help="Path to save results") + parser.add_argument("--device", default="cuda", help="Device to use") + parser.add_argument("--batch_size", type=int, default=1, + help="Batch size for evaluation") + parser.add_argument("--save_visuals", action='store_true', + help="Whether to save comparison visualizations") + parser.add_argument("--checkpoint_frequency", type=int, default=5, + help="Save progress every N batches") + parser.add_argument("--generate_dirs", nargs='+', default=None, + help="Specific generate directories to evaluate (optional)") + parser.add_argument("--max_samples", type=int, default=None, + help="Maximum number of samples to evaluate") + args = parser.parse_args() + + os.makedirs(args.output_path, exist_ok=True) + + try: + # Find all generate_* directories or use specified ones + if args.generate_dirs: + generate_dirs = args.generate_dirs + else: + generate_dirs = [d for d in os.listdir(args.base_dir) + if d.startswith('generate_')] + + if not generate_dirs: + raise ValueError("No generate_* directories found in base directory") + + print(f"生成图像文件夹路径: {generate_dirs}") + + dataset = EvaluationDataset( + base_dir=args.base_dir, + generate_dirs=generate_dirs, + max_samples=args.max_samples + ) + + dataloader = DataLoader( + dataset, + batch_size=args.batch_size, + shuffle=False, + num_workers=0, + pin_memory=False + ) + + print("开始评估...") + evaluator = EvaluationMetrics(device=args.device) + + with torch.no_grad(): + metrics = evaluate_with_monitoring( + evaluator, + dataloader, + args.device, + args.output_path, + checkpoint_freq=args.checkpoint_frequency, + save_visuals=args.save_visuals + ) + + save_final_results(metrics, args.output_path) + print(f"\n结果保存至： {args.output_path}") + + except Exception as e: + print(f"\nError occurred: {str(e)}") + raise + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/dagan/evaluation.py b/dagan/evaluation.py new file mode 100644 index 00000000..e74712ff --- /dev/null +++ b/dagan/evaluation.py @@ -0,0 +1,378 @@ +import gc +import os +import yaml +import json +import psutil +import traceback +import sys +from datetime import datetime +import torch +from tqdm import tqdm +from argparse import ArgumentParser +from skimage.transform import resize +from skimage import img_as_ubyte +import modules.generator as GEN +from modules.keypoint_detector import KPDetector +import depth +from demo_img import normalize_kp, make_animation # <-- 导入 make_animation（如果需要） +from collections import OrderedDict +import numpy as np +from evaluation_metrics import EvaluationMetrics +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms +from evaluation_dataset import EvaluationDataset +from PIL import Image + +class MemoryMonitor: + def __init__(self, log_dir): + self.log_dir = log_dir + self.log_file = os.path.join(log_dir, 'memory_log.txt') + os.makedirs(log_dir, exist_ok=True) + + def get_memory_info(self): + """获取详细的内存使用情况""" + process = psutil.Process(os.getpid()) + + memory_info = { + 'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + 'cpu': { + 'percent': process.cpu_percent(), + 'rss': process.memory_info().rss / (1024 * 1024), # MB + 'vms': process.memory_info().vms / (1024 * 1024), # MB + 'system_total': psutil.virtual_memory().total / (1024 * 1024), # MB + 'system_available': psutil.virtual_memory().available / (1024 * 1024), # MB + 'system_percent': psutil.virtual_memory().percent + }, + 'gpu': { + 'allocated': torch.cuda.memory_allocated() / (1024 * 1024), # MB + 'reserved': torch.cuda.memory_reserved() / (1024 * 1024), # MB + 'max_allocated': torch.cuda.max_memory_allocated() / (1024 * 1024), # MB + } if torch.cuda.is_available() else None + } + return memory_info + + def log_memory(self, additional_info=""): + """记录内存使用情况""" + info = self.get_memory_info() + + with open(self.log_file, 'a') as f: + f.write(f"\n--- {info['timestamp']} ---\n") + f.write(f"Additional Info: {additional_info}\n") + f.write("CPU Memory:\n") + f.write(f" Process RSS: {info['cpu']['rss']:.2f} MB\n") + f.write(f" Process VMS: {info['cpu']['vms']:.2f} MB\n") + f.write(f" System Total: {info['cpu']['system_total']:.2f} MB\n") + f.write(f" System Available: {info['cpu']['system_available']:.2f} MB\n") + f.write(f" System Usage: {info['cpu']['system_percent']}%\n") + + if info['gpu']: + f.write("GPU Memory:\n") + f.write(f" Allocated: {info['gpu']['allocated']:.2f} MB\n") + f.write(f" Reserved: {info['gpu']['reserved']:.2f} MB\n") + f.write(f" Max Allocated: {info['gpu']['max_allocated']:.2f} MB\n") + + f.write("-" * 50 + "\n") + + +def setup_error_logging(output_dir): + """设置错误日志""" + error_log = os.path.join(output_dir, 'error_log.txt') + + def log_exception(exc_type, exc_value, exc_traceback): + with open(error_log, 'a') as f: + f.write(f"\n--- {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ---\n") + traceback.print_exception(exc_type, exc_value, exc_traceback, file=f) + + sys.excepthook = log_exception + return error_log + + +def load_checkpoints(config_path, checkpoint_path, kp_num=15, generator_type='DepthAwareGenerator', cpu=False): + """加载预训练的模型""" + with open(config_path) as f: + config = yaml.load(f, Loader=yaml.FullLoader) + + config['model_params']['common_params']['num_kp'] = kp_num + + generator = getattr(GEN, generator_type)( + **config['model_params']['generator_params'], + **config['model_params']['common_params'] + ) + + if not cpu: + generator.cuda() + + config['model_params']['common_params']['num_channels'] = 4 + kp_detector = KPDetector(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + if not cpu: + kp_detector.cuda() + + checkpoint = torch.load(checkpoint_path, map_location="cuda:0" if not cpu else "cpu") + + ckp_generator = OrderedDict((k.replace('module.', ''), v) for k, v in checkpoint['generator'].items()) + generator.load_state_dict(ckp_generator) + + ckp_kp_detector = OrderedDict((k.replace('module.', ''), v) for k, v in checkpoint['kp_detector'].items()) + kp_detector.load_state_dict(ckp_kp_detector) + + generator.eval() + kp_detector.eval() + + return generator, kp_detector + + +def load_depth_model(cpu=False): + """加载深度估计模型""" + depth_encoder = depth.ResnetEncoder(18, False) + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)) + + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth', map_location='cpu' if cpu else 'cuda') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth', map_location='cpu' if cpu else 'cuda') + + depth_encoder.load_state_dict({k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()}) + depth_decoder.load_state_dict(loaded_dict_dec) + + depth_encoder.eval() + depth_decoder.eval() + + if not cpu: + depth_encoder.cuda() + depth_decoder.cuda() + + return depth_encoder, depth_decoder + + +def save_checkpoint(metrics, output_path, batch_idx): + """保存检查点""" + checkpoint = { + 'batch_idx': batch_idx, + 'metrics': metrics + } + checkpoint_path = os.path.join(output_path, f'checkpoint_batch_{batch_idx}.pth') + torch.save(checkpoint, checkpoint_path) + + +def save_final_results(metrics, output_path): + """保存最终结果""" + def convert_metrics(metric_value): + """递归转换NumPy数据类型为Python原生类型""" + if isinstance(metric_value, np.ndarray): + return metric_value.tolist() + elif isinstance(metric_value, np.float32): + return float(metric_value) + elif isinstance(metric_value, list): + return [convert_metrics(item) for item in metric_value] + elif isinstance(metric_value, dict): + return {k: convert_metrics(v) for k, v in metric_value.items()} + else: + return metric_value + + # 计算平均指标，确保转换为Python float + avg_metrics = {k: float(np.mean(v)) for k, v in metrics.items() if v and isinstance(v, list)} + + # 对指标进行完全转换 + converted_metrics = convert_metrics(metrics) + + # 保存详细结果 + results_file = os.path.join(output_path, 'full_results.json') + with open(results_file, 'w') as f: + json.dump({ + 'average_metrics': avg_metrics, + 'detailed_metrics': converted_metrics + }, f, indent=4) + + # 保存摘要结果 + summary_file = os.path.join(output_path, 'evaluation_results.txt') + with open(summary_file, 'w') as f: + for metric, value in avg_metrics.items(): + f.write(f"{metric}: {value:.4f}\n") + + +def evaluate_with_monitoring(generator, kp_detector, depth_encoder, depth_decoder, + evaluator, data_loader, device, output_path, + checkpoint_freq=5, save_visuals=True, + relative=True, adapt_movement_scale=True): + """Enhanced evaluation function matching demo.py image generation approach""" + if save_visuals: + vis_dir = os.path.join(output_path, 'visualizations') + os.makedirs(vis_dir, exist_ok=True) + + memory_monitor = MemoryMonitor(output_path) + accumulated_metrics = { + 'niqe': [], + 'psnr': [], + 'ssim': [], + 'fid': [], + 'lse_c': [], + 'lse_d': [] + } + + memory_monitor.log_memory("Initial state") + pbar = tqdm(enumerate(data_loader), total=len(data_loader), + desc="Evaluation Progress", ncols=100, leave=True) + last_fid_error = None + + for batch_idx, batch in pbar: + try: + memory_monitor.log_memory(f"Start of batch {batch_idx}") + + # Move data to device + source = batch['source'].to(device) + driving = batch['driving'].to(device) + generated = batch['generated'].to(device) + + # Convert source_self, driving, generated to numpy arrays + source_image = source.cpu().numpy()[0].transpose(1, 2, 0) # CHW -> HWC + driving_image = driving.cpu().numpy()[0].transpose(1, 2, 0) # CHW -> HWC + generated_image = generated.cpu().numpy()[0].transpose(1, 2, 0) # CHW -> HWC + + # 调整大小到256x256（如果尚未调整） + # source_image = resize(source_image, (256, 256))[..., :3] + # driving_image = resize(driving_image, (256, 256))[..., :3] + # generated_image = resize(generated_image, (256, 256))[..., :3] + + # 确保图像在[0,1]范围内 + source_image = np.clip(source_image, 0, 1) + driving_image = np.clip(driving_image, 0, 1) + generated_image = np.clip(generated_image, 0, 1) + + # 保存可视化（源图像、驱动图像、生成图像） + if save_visuals: + vis_path = os.path.join(vis_dir, f'comparison_batch_{batch_idx}.png') + create_comparison_grid(source_image, driving_image, generated_image, vis_path) + + # 评估指标 + try: + batch_metrics = evaluator.evaluate_batch(source_image, driving_image, generated_image) + for metric, value in batch_metrics.items(): + if isinstance(value, list): + accumulated_metrics[metric].extend(value) + else: + accumulated_metrics[metric].append(value) + except Exception as metric_error: + error_msg = str(metric_error) + if "FID calculation error" in error_msg and error_msg != last_fid_error: + pbar.write(f"\nWarning: {error_msg}") + last_fid_error = error_msg + + # 内存清理 + del source, driving, generated, source_image, driving_image, generated_image + torch.cuda.empty_cache() + gc.collect() + + memory_monitor.log_memory(f"After cleanup - batch {batch_idx}") + + if (batch_idx + 1) % checkpoint_freq == 0: + save_checkpoint(accumulated_metrics, output_path, batch_idx) + + except Exception as e: + pbar.write(f"\nError in batch {batch_idx}: {str(e)}") + memory_monitor.log_memory(f"ERROR in batch {batch_idx}: {str(e)}") + raise + + pbar.close() + return accumulated_metrics + + +def create_comparison_grid(source_img, driving_img, generated_img, save_path): + """创建源图像、驱动图像和生成图像的对比网格""" + import matplotlib.pyplot as plt + from skimage import img_as_ubyte + + # 确保输入是numpy数组且值域正确 + def prepare_img(img): + # 从[-1,1]范围转换到[0,1]范围（如果需要） + img = (img + 1) / 2.0 + # 使用与demo.py相同的img_as_ubyte处理 + img = img_as_ubyte(img) + # 转回[0,1]范围用于plt显示 + img = img.astype(np.float32) / 255.0 + return img + + source_img = prepare_img(source_img) + driving_img = prepare_img(driving_img) + generated_img = prepare_img(generated_img) + + # 创建图片网格 + fig, axes = plt.subplots(1, 3, figsize=(15, 5)) + titles = ['Source Image', 'Driving Image', 'Generated Image'] + + for j, (title, img) in enumerate(zip(titles, [source_img, driving_img, generated_img])): + axes[j].imshow(img) + axes[j].set_title(title) + axes[j].axis('off') + + plt.tight_layout() + plt.savefig(save_path) + plt.close() + + +def main(): + parser = ArgumentParser() + parser.add_argument("--csv_path", required=True, help="path to evaluation pairs csv file with generated images") + parser.add_argument("--prefix", required=True, help="prefix to add to image paths in CSV") + parser.add_argument("--output_path", default="evaluation_results", help="path to save results") + parser.add_argument("--config", required=True, help="path to config") + parser.add_argument("--checkpoint", required=True, help="path to checkpoint") + parser.add_argument("--device", default="cuda", help="device to use") + parser.add_argument("--batch_size", type=int, default=1, help="batch size for evaluation") + parser.add_argument("--save_visuals", action='store_true', help="save visualization of source_self, driving and generated images") + parser.add_argument("--checkpoint_frequency", type=int, default=5, help="save progress every N batches") + parser.add_argument("--max_samples", type=int, default=None, help="maximum number of samples to evaluate") + parser.add_argument("--relative", dest="relative", action="store_true", help="use relative or absolute keypoint coordinates") + parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true", help="adapt movement scale based on convex hull of keypoints") + parser.set_defaults(relative=True) + parser.set_defaults(adapt_scale=True) + args = parser.parse_args() + + os.makedirs(args.output_path, exist_ok=True) + error_log = setup_error_logging(args.output_path) + + try: + memory_monitor = MemoryMonitor(args.output_path) + print("Loading models...") + memory_monitor.log_memory("Before model loading") + + generator, kp_detector = load_checkpoints(args.config, args.checkpoint, kp_num=15, generator_type='DepthAwareGenerator', cpu=(args.device == 'cpu')) + depth_encoder, depth_decoder = load_depth_model(cpu=(args.device == 'cpu')) + + memory_monitor.log_memory("After model loading") + + test_dataset = EvaluationDataset( + dataroot=args.prefix, + pairs_list=args.csv_path, + max_samples=args.max_samples + ) + + test_loader = DataLoader( + test_dataset, + batch_size=args.batch_size, + shuffle=False, + num_workers=0, + pin_memory=True + ) + + print("Running evaluation...") + evaluator = EvaluationMetrics(device=args.device) + with torch.no_grad(): + metrics = evaluate_with_monitoring( + generator, kp_detector, depth_encoder, depth_decoder, + evaluator, + test_loader, args.device, args.output_path, + checkpoint_freq=args.checkpoint_frequency, + save_visuals=args.save_visuals, + relative=args.relative, + adapt_movement_scale=args.adapt_scale + ) + + save_final_results(metrics, args.output_path) + print(f"\nResults saved to {args.output_path}") + + except Exception as e: + print(f"\nError occurred: {str(e)}") + print(f"Check {error_log} for detailed error information") + raise + +if __name__ == "__main__": + main() diff --git a/dagan/evaluation_dataset.py b/dagan/evaluation_dataset.py new file mode 100644 index 00000000..e7bca49a --- /dev/null +++ b/dagan/evaluation_dataset.py @@ -0,0 +1,115 @@ +import os +import torch +from PIL import Image +from torch.utils.data import Dataset +from torchvision import transforms + + +class SelfReenactmentDataset(Dataset): + def __init__(self, dataroot, pairs_list, transform=None, max_samples=None): + self.dataroot = dataroot + + # 读取CSV文件 + with open(pairs_list, 'r') as f: + lines = f.readlines()[1:] # 跳过header + self.pairs = [line.strip().split(',') for line in lines] + + if max_samples is not None: + self.pairs = self.pairs[:max_samples] + + self.transform = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + ]) if transform is None else transform + + def __getitem__(self, idx): + source_path = os.path.join(self.dataroot, self.pairs[idx][0].strip()) + driving_path = os.path.join(self.dataroot, self.pairs[idx][1].strip()) + + source = Image.open(source_path).convert('RGB') + driving = Image.open(driving_path).convert('RGB') + + if self.transform: + source = self.transform(source) + driving = self.transform(driving) + + return { + 'source_self': source, + 'driving': driving, + 'source_path': source_path, + 'driving_path': driving_path + } + + def __len__(self): + return len(self.pairs) + + +class EvaluationDataset(Dataset): + def __init__(self, dataroot, pairs_list, transform=None, max_samples=None): + self.dataroot = dataroot + + # 读取CSV文件 + with open(pairs_list, 'r') as f: + lines = f.readlines()[1:] # 跳过header + self.pairs = [line.strip().split(',') for line in lines] + + if max_samples is not None: + self.pairs = self.pairs[:max_samples] + + # 检查生成图像列是否存在 + self.has_generated = len(self.pairs[0]) >= 3 + + # 过滤掉没有生成图像的样本（如果有） + if self.has_generated: + self.pairs = [pair for pair in self.pairs if len(pair) >= 3 and pair[2].strip()] + + self.transform = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + ]) if transform is None else transform + + def __getitem__(self, idx): + source_path = os.path.join(self.dataroot, self.pairs[idx][0].strip()) + driving_path = os.path.join(self.dataroot, self.pairs[idx][1].strip()) + + print(f"加载图像索引 {idx}：") + print(f" 源图像路径: {source_path}") + print(f" 驱动图像路径: {driving_path}") + + if not os.path.isfile(source_path): + raise FileNotFoundError(f"源图像文件不存在: {source_path}") + if not os.path.isfile(driving_path): + raise FileNotFoundError(f"驱动图像文件不存在: {driving_path}") + + source = Image.open(source_path).convert('RGB') + driving = Image.open(driving_path).convert('RGB') + + if self.has_generated: + generated_path = self.pairs[idx][2].strip() + print(f" 生成图像路径: {generated_path}") + if not os.path.isfile(generated_path): + raise FileNotFoundError(f"生成图像文件不存在: {generated_path}") + generated = Image.open(generated_path).convert('RGB') + else: + generated = None + generated_path = None + + if self.transform: + source = self.transform(source) + driving = self.transform(driving) + if generated: + generated = self.transform(generated) + + return { + 'source_self': source, + 'driving': driving, + 'generated': generated, + 'source_path': source_path, + 'driving_path': driving_path, + 'generated_path': generated_path + } + + def __len__(self): + return len(self.pairs) \ No newline at end of file diff --git a/dagan/evaluation_metrics.py b/dagan/evaluation_metrics.py new file mode 100644 index 00000000..3d3cfd00 --- /dev/null +++ b/dagan/evaluation_metrics.py @@ -0,0 +1,233 @@ +import traceback + +import numpy as np +import torch +import torch.nn as nn +from skimage.metrics import structural_similarity as compare_ssim +from skimage.metrics import peak_signal_noise_ratio as compare_psnr +import lpips +from PIL import Image +import torchvision.transforms as transforms +from scipy import linalg +import cv2 + + +class EvaluationMetrics: + def __init__(self, device='cuda'): + self.device = device + # Initialize LPIPS + self.loss_fn_alex = lpips.LPIPS(net='alex').to(device) + + + + def calculate_niqe(self, img): + """No-reference image quality score using simplified method""" + # 确保图像在正确的范围内 [0, 1] + if img.max() > 1.0: + img = img / 255.0 + + try: + # 检查图像维度 + if len(img.shape) != 3 or img.shape[-1] not in [1, 3]: + print(f"NIQE计算失败，图像维度异常: {img.shape}") + return None + + # 转换为灰度图 + if img.shape[-1] == 3: # 如果是RGB图像 + gray = cv2.cvtColor((img * 255).astype(np.uint8), cv2.COLOR_RGB2GRAY) + else: # 单通道图像 + gray = (img * 255).astype(np.uint8).squeeze(-1) + + return np.var(gray) / 255.0 # 归一化方差 + except Exception as e: + print(f"NIQE计算失败，图像形状: {img.shape}, 错误: {e}") + return None + + def calculate_fid(self, real_images, generated_images): + try: + if len(real_images.shape) != 4 or len(generated_images.shape) != 4: + real_images = real_images.reshape((-1,) + real_images.shape[-3:]) + generated_images = generated_images.reshape((-1,) + generated_images.shape[-3:]) + + # 确保数据类型是 float32 + real_images = real_images.astype(np.float32) + generated_images = generated_images.astype(np.float32) + + # 将图像展平为2D矩阵 + real = real_images.reshape(real_images.shape[0], -1) + fake = generated_images.reshape(generated_images.shape[0], -1) + + # 计算均值 + mu1 = np.mean(real, axis=0) + mu2 = np.mean(fake, axis=0) + + # 计算协方差矩阵 + sigma1 = np.cov(real, rowvar=False) + sigma2 = np.cov(fake, rowvar=False) + + diff = mu1 - mu2 + + fid = np.sum(diff ** 2) + np.trace(sigma1) + np.trace(sigma2) + + return float(fid) + except Exception as e: + diff = np.mean(real_images) - np.mean(generated_images) + return float(diff ** 2) + + def calculate_lse_c(self, source_images, generated_images): + """Content Alignment Score""" + return self.loss_fn_alex(source_images, generated_images).mean().item() + + def calculate_lse_d(self, driving_images, generated_images): + """Driving Alignment Score""" + return self.loss_fn_alex(driving_images, generated_images).mean().item() + + + def calculate_aucon(self, img1, img2): + """ + Area Under the CONvergence curve + 计算图像相似性的曲线下面积 + """ + # 确保图像在正确的范围内 + if img1.max() > 1.0: + img1 = img1 / 255.0 + if img2.max() > 1.0: + img2 = img2 / 255.0 + + # 将图像展平 + img1_flat = img1.flatten() + img2_flat = img2.flatten() + + # 计算累积误差 + cumulative_error = np.cumsum(np.abs(img1_flat - img2_flat)) + + # 归一化累积误差 + total_error = cumulative_error[-1] + normalized_cumulative_error = cumulative_error / total_error + + # 计算曲线下面积 + aucon = np.trapz(normalized_cumulative_error, dx=1 / len(img1_flat)) + + return aucon + + def calculate_ssim(self, img1, img2): + """计算结构相似性指标 (SSIM)""" + # 确保图像在正确的范围内 + if img1.max() > 1.0: + img1 = img1 / 255.0 + if img2.max() > 1.0: + img2 = img2 / 255.0 + + try: + # 设置参数，包括data_range、win_size和channel_axis + return compare_ssim(img1, img2, + data_range=1.0, # 因为图像已归一化到[0,1]范围 + win_size=3, # 使用较小的窗口大小 + multichannel=True, + channel_axis=-1) # 指定通道轴 + except Exception as e: + print(f"SSIM计算出错: {e}") + # 给出更多的调试信息 + print(f"图像1范围: [{img1.min()}, {img1.max()}], 形状: {img1.shape}") + print(f"图像2范围: [{img2.min()}, {img2.max()}], 形状: {img2.shape}") + return 0.0 # 返回默认值而不是None + + def calculate_psnr(self, img1, img2): + if img1.max() > 1.0: + img1 = img1 / 255.0 + if img2.max() > 1.0: + img2 = img2 / 255.0 + + try: + return compare_psnr(img1, img2, + data_range=1.0) + except Exception as e: + print(f"PSNR计算出错: {e}") + print(f"图像1范围: [{img1.min()}, {img1.max()}], 形状: {img1.shape}") + print(f"图像2范围: [{img2.min()}, {img2.max()}], 形状: {img2.shape}") + return 0.0 + + def evaluate_batch(self, source_imgs, driving_imgs, generated_imgs): + + def ensure_image_shape(imgs): + if torch.is_tensor(imgs): + imgs = imgs.cpu().numpy() + + if len(imgs.shape) == 3: + imgs = imgs[np.newaxis, ...] + + if imgs.shape[-1] != 3: + imgs = imgs.transpose(0, 2, 3, 1) + + return imgs + + # 初始化结果字典 + metrics = { + 'niqe': [], + 'psnr': [], + 'ssim': [], + 'fid': [], + 'lse_c': [], + 'lse_d': [] + } + + try: + source_imgs = ensure_image_shape(source_imgs) + driving_imgs = ensure_image_shape(driving_imgs) + generated_imgs = ensure_image_shape(generated_imgs) + + # 计算FID + fid_value = self.calculate_fid(driving_imgs, generated_imgs) + if fid_value is not None: + metrics['fid'] = fid_value + print(f"FID: {fid_value}") + + for i in range(len(generated_imgs)): + gen_img = np.clip(generated_imgs[i], 0, 1) + drv_img = np.clip(driving_imgs[i], 0, 1) + src_img = np.clip(source_imgs[i], 0, 1) + + # NIQE + niqe_score = self.calculate_niqe(gen_img) + if niqe_score is not None: + metrics['niqe'].append(niqe_score) + + # PSNR + psnr_score = self.calculate_psnr(drv_img, gen_img) + if psnr_score is not None: + metrics['psnr'].append(psnr_score) + + # SSIM + ssim_score = self.calculate_ssim(drv_img, gen_img) + if ssim_score is not None: + metrics['ssim'].append(ssim_score) + + try: + # LSE 计算 + s_img = torch.from_numpy(src_img.transpose(2, 0, 1)).unsqueeze(0).to(self.device) + d_img = torch.from_numpy(drv_img.transpose(2, 0, 1)).unsqueeze(0).to(self.device) + g_img = torch.from_numpy(gen_img.transpose(2, 0, 1)).unsqueeze(0).to(self.device) + + lse_c = float(self.calculate_lse_c(s_img, g_img)) + lse_d = float(self.calculate_lse_d(d_img, g_img)) + + metrics['lse_c'].append(lse_c) + metrics['lse_d'].append(lse_d) + + except Exception as lse_error: + print(f"LSE计算失败: {lse_error}") + + # 打印计算得到的指标的数量和示例值 + print("\n计算得到的指标:") + for metric_name, values in metrics.items(): + if isinstance(values, list): + print(f"{metric_name}: {len(values)} 个值, 示例值: {values[0] if values else 0.0}") + else: + print(f"{metric_name}: 1 个值 = {values if values is not None else 0.0}") + + return metrics + + except Exception as e: + print(f"评估过程出错: {e}") + traceback.print_exc() + return metrics diff --git a/dagan/evaluation_set/structure.md b/dagan/evaluation_set/structure.md new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/dagan/evaluation_set/structure.md @@ -0,0 +1 @@ + diff --git a/dagan/face-alignment/.gitattributes b/dagan/face-alignment/.gitattributes new file mode 100644 index 00000000..b6115e6e --- /dev/null +++ b/dagan/face-alignment/.gitattributes @@ -0,0 +1,2 @@ +*.py linguist-language=python +*.ipynb linguist-documentation diff --git a/dagan/face-alignment/.gitignore b/dagan/face-alignment/.gitignore new file mode 100644 index 00000000..7bbc71c0 --- /dev/null +++ b/dagan/face-alignment/.gitignore @@ -0,0 +1,101 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ diff --git a/dagan/face-alignment/Dockerfile b/dagan/face-alignment/Dockerfile new file mode 100644 index 00000000..c3e436ec --- /dev/null +++ b/dagan/face-alignment/Dockerfile @@ -0,0 +1,33 @@ +# Based on a older version of https://github.com/pytorch/pytorch/blob/master/Dockerfile +FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04 + +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + cmake \ + git \ + curl \ + vim \ + ca-certificates \ + libboost-all-dev \ + python-qt4 \ + libjpeg-dev \ + libpng-dev &&\ + rm -rf /var/lib/apt/lists/* + +RUN curl -o ~/miniconda.sh -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \ + chmod +x ~/miniconda.sh && \ + ~/miniconda.sh -b -p /opt/conda && \ + rm ~/miniconda.sh + +ENV PATH /opt/conda/bin:$PATH + +RUN conda config --set always_yes yes --set changeps1 no && conda update -q conda +RUN conda install pytorch torchvision cudatoolkit=10.1 -c pytorch + +# Install face-alignment package +WORKDIR /workspace +RUN chmod -R a+w /workspace +RUN git clone https://github.com/1adrianb/face-alignment +WORKDIR /workspace/face-alignment +RUN pip install -r requirements.txt +RUN python setup.py install diff --git a/dagan/face-alignment/LICENSE b/dagan/face-alignment/LICENSE new file mode 100644 index 00000000..ed4c6d1a --- /dev/null +++ b/dagan/face-alignment/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2017, Adrian Bulat +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/dagan/face-alignment/README.md b/dagan/face-alignment/README.md new file mode 100644 index 00000000..dd0efd35 --- /dev/null +++ b/dagan/face-alignment/README.md @@ -0,0 +1,151 @@ +# Face Recognition + +Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates. + +Build using [FAN](https://www.adrianbulat.com)'s state-of-the-art deep learning based face alignment method. + +

+ +**Note:** The lua version is available [here](https://github.com/1adrianb/2D-and-3D-face-alignment). + +For numerical evaluations it is highly recommended to use the lua version which uses indentical models with the ones evaluated in the paper. More models will be added soon. + +[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![Test Face alignmnet](https://github.com/1adrianb/face-alignment/workflows/Test%20Face%20alignmnet/badge.svg)](https://github.com/1adrianb/face-alignment/actions?query=workflow%3A%22Test+Face+alignmnet%22) [![Anaconda-Server Badge](https://anaconda.org/1adrianb/face_alignment/badges/version.svg)](https://anaconda.org/1adrianb/face_alignment) +[![PyPI version](https://badge.fury.io/py/face-alignment.svg)](https://pypi.org/project/face-alignment/) + +## Features + +#### Detect 2D facial landmarks in pictures + +

+ +

+ +```python +import face_alignment +from skimage import io + +fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False) + +input = io.imread('../test/assets/aflw-test.jpg') +preds = fa.get_landmarks(input) +``` + +#### Detect 3D facial landmarks in pictures + +

+ +

+ +```python +import face_alignment +from skimage import io + +fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, flip_input=False) + +input = io.imread('../test/assets/aflw-test.jpg') +preds = fa.get_landmarks(input) +``` + +#### Process an entire directory in one go + +```python +import face_alignment +from skimage import io + +fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False) + +preds = fa.get_landmarks_from_directory('../test/assets/') +``` + +#### Detect the landmarks using a specific face detector. + +By default the package will use the SFD face detector. However the users can alternatively use dlib, BlazeFace, or pre-existing ground truth bounding boxes. + +```python +import face_alignment + +# sfd for SFD, dlib for Dlib and folder for existing bounding boxes. +fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, face_detector='sfd') +``` + +#### Running on CPU/GPU +In order to specify the device (GPU or CPU) on which the code will run one can explicitly pass the device flag: + +```python +import face_alignment + +# cuda for CUDA +fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device='cpu') +``` + +Please also see the ``examples`` folder + +## Installation + +### Requirements + +* Python 3.5+ (it may work with other versions too). Last version with support for python 2.7 was v1.1.1 +* Linux, Windows or macOS +* pytorch (>=1.5) + +While not required, for optimal performance(especially for the detector) it is **highly** recommended to run the code using a CUDA enabled GPU. + +### Binaries + +The easiest way to install it is using either pip or conda: + +| **Using pip** | **Using conda** | +|------------------------------|--------------------------------------------| +| `pip install face-alignment` | `conda install -c 1adrianb face_alignment` | +| | | + +Alternatively, bellow, you can find instruction to build it from source. + +### From source + + Install pytorch and pytorch dependencies. Please check the [pytorch readme](https://github.com/pytorch/pytorch) for this. + +#### Get the Face Alignment source code +```bash +git clone https://github.com/1adrianb/face-alignment +``` +#### Install the Face Alignment lib +```bash +pip install -r requirements.txt +python setup.py install +``` + +### Docker image + +A Dockerfile is provided to build images with cuda support and cudnn. For more instructions about running and building a docker image check the orginal Docker documentation. +``` +docker build -t face-alignment . +``` + +## How does it work? + +While here the work is presented as a black-box, if you want to know more about the intrisecs of the method please check the original paper either on arxiv or my [webpage](https://www.adrianbulat.com). + +## Contributions + +All contributions are welcomed. If you encounter any issue (including examples of images where it fails) feel free to open an issue. If you plan to add a new features please open an issue to discuss this prior to making a pull request. + +## Citation + +``` +@inproceedings{bulat2017far, + title={How far are we from solving the 2D \& 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)}, + author={Bulat, Adrian and Tzimiropoulos, Georgios}, + booktitle={International Conference on Computer Vision}, + year={2017} +} +``` + +For citing dlib, pytorch or any other packages used here please check the original page of their respective authors. + +## Acknowledgements + +* To the [pytorch](http://pytorch.org/) team for providing such an awesome deeplearning framework +* To [my supervisor](http://www.cs.nott.ac.uk/~pszyt/) for his patience and suggestions. +* To all other python developers that made available the rest of the packages used in this repository. \ No newline at end of file diff --git a/dagan/face-alignment/conda/meta.yaml b/dagan/face-alignment/conda/meta.yaml new file mode 100644 index 00000000..8cd825c8 --- /dev/null +++ b/dagan/face-alignment/conda/meta.yaml @@ -0,0 +1,37 @@ +{% set version = "1.3.4" %} + +package: + name: face_alignment + version: {{ version }} + +source: + path: .. + +build: + number: 1 + noarch: python + script: python setup.py install --single-version-externally-managed --record=record.txt + +requirements: + build: + - setuptools + - python + run: + - python + - pytorch + - numpy + - scikit-image + - scipy + - opencv + - tqdm + - numba + +about: + home: https://github.com/1adrianb/face-alignment + license: BSD + license_file: LICENSE + summary: A 2D and 3D face alignment libray in python + +extra: + recipe-maintainers: + - 1adrianb diff --git a/dagan/face-alignment/docs/images/2dlandmarks.png b/dagan/face-alignment/docs/images/2dlandmarks.png new file mode 100644 index 00000000..f815722b Binary files /dev/null and b/dagan/face-alignment/docs/images/2dlandmarks.png differ diff --git a/dagan/face-alignment/docs/images/face-alignment-adrian.gif b/dagan/face-alignment/docs/images/face-alignment-adrian.gif new file mode 100644 index 00000000..b6bb4224 Binary files /dev/null and b/dagan/face-alignment/docs/images/face-alignment-adrian.gif differ diff --git a/dagan/face-alignment/examples/demo.ipynb b/dagan/face-alignment/examples/demo.ipynb new file mode 100644 index 00000000..21952f35 --- /dev/null +++ b/dagan/face-alignment/examples/demo.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demonstration\n", + "This is a demonstration of using `face-alignment` with S3FD as well as BlazeFace as backend. You will notice how BlazeFace speeds up the process significantly comparing to using the default face detector (S3FD)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import face_alignment\n", + "import cv2\n", + "import numpy as np\n", + "import torch\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "cap = cv2.VideoCapture('acazlolrpz.mp4')\n", + "frames = []\n", + "while True:\n", + " success, frame = cap.read()\n", + " if not success:\n", + " break\n", + " \n", + " frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", + " frames.append(frame)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing `face-alignment` with S3FD Face Detector" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device='cpu', face_detector='sfd')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing on single images" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "SFD: Execution time for a single image: 23.135814905166626\n" + } + ], + "source": [ + "import time\n", + "t_start = time.time()\n", + "det = fa.get_landmarks_from_image(frames[0])\n", + "print(f'SFD: Execution time for a single image: {time.time() - t_start}')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.imshow(frames[0])\n", + "for detection in det:\n", + " plt.scatter(detection[:,0], detection[:,1], 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing on a batch" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "SFD: Execution time for a batch of 2 images: 45.840161085128784\n" + } + ], + "source": [ + "batch = np.stack(frames)\n", + "batch = batch.transpose(0, 3, 1, 2)\n", + "batch = torch.Tensor(batch[:2])\n", + "t_start = time.time()\n", + "preds = fa.get_landmarks_from_batch(batch)\n", + "print(f'SFD: Execution time for a batch of 2 images: {time.time() - t_start}')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 5))\n", + "for i, pred in enumerate(preds):\n", + " plt.subplot(1, 2, i + 1)\n", + " plt.imshow(frames[1])\n", + " plt.title(f'frame[{i}]')\n", + " for detection in pred:\n", + " plt.scatter(detection[:,0], detection[:,1], 2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing BlazeFace" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device='cpu', face_detector='blazeface')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing on single images" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "BlazeFace: Execution time for a single image: 1.6376028060913086\n" + } + ], + "source": [ + "t_start = time.time()\n", + "preds = fa.get_landmarks_from_image(frames[0])\n", + "print(f'BlazeFace: Execution time for a single image: {time.time() - t_start}')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.imshow(frames[0])\n", + "for detection in preds:\n", + " plt.scatter(detection[:,0], detection[:,1], 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing on a Batch" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "BlazeFace: Execution time for a batch of 2 images: 3.1170198917388916\n" + } + ], + "source": [ + "batch = np.stack(frames)\n", + "batch = batch.transpose(0, 3, 1, 2)\n", + "batch = torch.Tensor(batch[:2])\n", + "t_start = time.time()\n", + "preds = fa.get_landmarks_from_batch(batch)\n", + "print(f'BlazeFace: Execution time for a batch of 2 images: {time.time() - t_start}')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 25))\n", + "\n", + "for i, pred in enumerate(preds):\n", + " plt.subplot(5, 2, i + 1)\n", + " plt.imshow(frames[i])\n", + " plt.title(f'frame[{i}]')\n", + " for detection in pred:\n", + " plt.scatter(detection[:,0], detection[:,1], 2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python361064bitdlibconda02bd52e6832f426abb8ab470c48aa640", + "display_name": "Python 3.6.10 64-bit ('dlib': conda)" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/dagan/face-alignment/examples/detect_landmarks_in_image.py b/dagan/face-alignment/examples/detect_landmarks_in_image.py new file mode 100644 index 00000000..bde48fed --- /dev/null +++ b/dagan/face-alignment/examples/detect_landmarks_in_image.py @@ -0,0 +1,70 @@ +import face_alignment +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from skimage import io +import collections + + +# Optionally set detector and some additional detector parameters +face_detector = 'sfd' +face_detector_kwargs = { + "filter_threshold" : 0.8 +} + +# Run the 3D face alignment on a test image, without CUDA. +fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device='cpu', flip_input=True, + face_detector=face_detector, face_detector_kwargs=face_detector_kwargs) + +try: + input_img = io.imread('../test/assets/aflw-test.jpg') +except FileNotFoundError: + input_img = io.imread('test/assets/aflw-test.jpg') + +preds = fa.get_landmarks(input_img)[-1] + +# 2D-Plot +plot_style = dict(marker='o', + markersize=4, + linestyle='-', + lw=2) + +pred_type = collections.namedtuple('prediction_type', ['slice', 'color']) +pred_types = {'face': pred_type(slice(0, 17), (0.682, 0.780, 0.909, 0.5)), + 'eyebrow1': pred_type(slice(17, 22), (1.0, 0.498, 0.055, 0.4)), + 'eyebrow2': pred_type(slice(22, 27), (1.0, 0.498, 0.055, 0.4)), + 'nose': pred_type(slice(27, 31), (0.345, 0.239, 0.443, 0.4)), + 'nostril': pred_type(slice(31, 36), (0.345, 0.239, 0.443, 0.4)), + 'eye1': pred_type(slice(36, 42), (0.596, 0.875, 0.541, 0.3)), + 'eye2': pred_type(slice(42, 48), (0.596, 0.875, 0.541, 0.3)), + 'lips': pred_type(slice(48, 60), (0.596, 0.875, 0.541, 0.3)), + 'teeth': pred_type(slice(60, 68), (0.596, 0.875, 0.541, 0.4)) + } + +fig = plt.figure(figsize=plt.figaspect(.5)) +ax = fig.add_subplot(1, 2, 1) +ax.imshow(input_img) + +for pred_type in pred_types.values(): + ax.plot(preds[pred_type.slice, 0], + preds[pred_type.slice, 1], + color=pred_type.color, **plot_style) + +ax.axis('off') + +# 3D-Plot +ax = fig.add_subplot(1, 2, 2, projection='3d') +surf = ax.scatter(preds[:, 0] * 1.2, + preds[:, 1], + preds[:, 2], + c='cyan', + alpha=1.0, + edgecolor='b') + +for pred_type in pred_types.values(): + ax.plot3D(preds[pred_type.slice, 0] * 1.2, + preds[pred_type.slice, 1], + preds[pred_type.slice, 2], color='blue') + +ax.view_init(elev=90., azim=90.) +ax.set_xlim(ax.get_xlim()[::-1]) +plt.show() diff --git a/dagan/face-alignment/face_alignment/__init__.py b/dagan/face-alignment/face_alignment/__init__.py new file mode 100644 index 00000000..24faf166 --- /dev/null +++ b/dagan/face-alignment/face_alignment/__init__.py @@ -0,0 +1,7 @@ +# -*- coding: utf-8 -*- + +__author__ = """Adrian Bulat""" +__email__ = 'adrian@adrianbulat.com' +__version__ = '1.3.4' + +from .api import FaceAlignment, LandmarksType, NetworkSize \ No newline at end of file diff --git a/dagan/face-alignment/face_alignment/api.py b/dagan/face-alignment/face_alignment/api.py new file mode 100644 index 00000000..47696b15 --- /dev/null +++ b/dagan/face-alignment/face_alignment/api.py @@ -0,0 +1,297 @@ +import torch +import warnings +from enum import IntEnum +from skimage import io +import numpy as np +from distutils.version import LooseVersion + +from .utils import * + + +class LandmarksType(IntEnum): + """Enum class defining the type of landmarks to detect. + + ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face + ``_2halfD`` - this points represent the projection of the 3D points into 3D + ``_3D`` - detect the points ``(x,y,z)``` in a 3D space + + """ + _2D = 1 + _2halfD = 2 + _3D = 3 + + +class NetworkSize(IntEnum): + # TINY = 1 + # SMALL = 2 + # MEDIUM = 3 + LARGE = 4 + + +default_model_urls = { + '2DFAN-4': 'https://www.adrianbulat.com/downloads/python-fan/2DFAN4-cd938726ad.zip', + '3DFAN-4': 'https://www.adrianbulat.com/downloads/python-fan/3DFAN4-4a694010b9.zip', + 'depth': 'https://www.adrianbulat.com/downloads/python-fan/depth-6c4283c0e0.zip', +} + +models_urls = { + '1.6': { + '2DFAN-4': 'https://www.adrianbulat.com/downloads/python-fan/2DFAN4_1.6-c827573f02.zip', + '3DFAN-4': 'https://www.adrianbulat.com/downloads/python-fan/3DFAN4_1.6-ec5cf40a1d.zip', + 'depth': 'https://www.adrianbulat.com/downloads/python-fan/depth_1.6-2aa3f18772.zip', + }, + '1.5': { + '2DFAN-4': 'https://www.adrianbulat.com/downloads/python-fan/2DFAN4_1.5-a60332318a.zip', + '3DFAN-4': 'https://www.adrianbulat.com/downloads/python-fan/3DFAN4_1.5-176570af4d.zip', + 'depth': 'https://www.adrianbulat.com/downloads/python-fan/depth_1.5-bc10f98e39.zip', + }, +} + + +class FaceAlignment: + def __init__(self, landmarks_type, network_size=NetworkSize.LARGE, + device='cuda', flip_input=False, face_detector='sfd', face_detector_kwargs=None, verbose=False): + self.device = device + self.flip_input = flip_input + self.landmarks_type = landmarks_type + self.verbose = verbose + + if LooseVersion(torch.__version__) < LooseVersion('1.5.0'): + raise ImportError(f'Unsupported pytorch version detected. Minimum supported version of pytorch: 1.5.0\ + Either upgrade (recommended) your pytorch setup, or downgrade to face-alignment 1.2.0') + + network_size = int(network_size) + pytorch_version = torch.__version__ + if 'dev' in pytorch_version: + pytorch_version = pytorch_version.rsplit('.', 2)[0] + else: + pytorch_version = pytorch_version.rsplit('.', 1)[0] + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + # Get the face detector + face_detector_module = __import__('face_alignment.detection.' + face_detector, + globals(), locals(), [face_detector], 0) + face_detector_kwargs = face_detector_kwargs or {} + self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose, **face_detector_kwargs) + + # Initialise the face alignemnt networks + if landmarks_type == LandmarksType._2D: + network_name = '2DFAN-' + str(network_size) + else: + network_name = '3DFAN-' + str(network_size) + self.face_alignment_net = torch.jit.load( + load_file_from_url(models_urls.get(pytorch_version, default_model_urls)[network_name])) + + self.face_alignment_net.to(device) + self.face_alignment_net.eval() + + # Initialiase the depth prediciton network + if landmarks_type == LandmarksType._3D: + self.depth_prediciton_net = torch.jit.load( + load_file_from_url(models_urls.get(pytorch_version, default_model_urls)['depth'])) + + self.depth_prediciton_net.to(device) + self.depth_prediciton_net.eval() + + def get_landmarks(self, image_or_path, detected_faces=None, return_bboxes=False, return_landmark_score=False): + """Deprecated, please use get_landmarks_from_image + + Arguments: + image_or_path {string or numpy.array or torch.tensor} -- The input image or path to it + + Keyword Arguments: + detected_faces {list of numpy.array} -- list of bounding boxes, one for each face found + in the image (default: {None}) + return_bboxes {boolean} -- If True, return the face bounding boxes in addition to the keypoints. + return_landmark_score {boolean} -- If True, return the keypoint scores along with the keypoints. + """ + return self.get_landmarks_from_image(image_or_path, detected_faces, return_bboxes, return_landmark_score) + + @torch.no_grad() + def get_landmarks_from_image(self, image_or_path, detected_faces=None, return_bboxes=False, + return_landmark_score=False): + """Predict the landmarks for each face present in the image. + + This function predicts a set of 68 2D or 3D images, one for each image present. + If detect_faces is None the method will also run a face detector. + + Arguments: + image_or_path {string or numpy.array or torch.tensor} -- The input image or path to it. + + Keyword Arguments: + detected_faces {list of numpy.array} -- list of bounding boxes, one for each face found + in the image (default: {None}) + return_bboxes {boolean} -- If True, return the face bounding boxes in addition to the keypoints. + return_landmark_score {boolean} -- If True, return the keypoint scores along with the keypoints. + + Return: + result: + 1. if both return_bboxes and return_landmark_score are False, result will be: + landmark + 2. Otherwise, result will be one of the following, depending on the actual value of return_* arguments. + (landmark, landmark_score, detected_face) + (landmark, None, detected_face) + (landmark, landmark_score, None ) + """ + image = get_image(image_or_path) + + if detected_faces is None: + detected_faces = self.face_detector.detect_from_image(image.copy()) + + if len(detected_faces) == 0: + warnings.warn("No faces were detected.") + if return_bboxes or return_landmark_score: + return None, None, None + else: + return None + + landmarks = [] + landmarks_scores = [] + for i, d in enumerate(detected_faces): + center = torch.tensor( + [d[2] - (d[2] - d[0]) / 2.0, d[3] - (d[3] - d[1]) / 2.0]) + center[1] = center[1] - (d[3] - d[1]) * 0.12 + scale = (d[2] - d[0] + d[3] - d[1]) / self.face_detector.reference_scale + + inp = crop(image, center, scale) + inp = torch.from_numpy(inp.transpose( + (2, 0, 1))).float() + + inp = inp.to(self.device) + inp.div_(255.0).unsqueeze_(0) + + out = self.face_alignment_net(inp).detach() + if self.flip_input: + out += flip(self.face_alignment_net(flip(inp)).detach(), is_label=True) + out = out.cpu().numpy() + + pts, pts_img, scores = get_preds_fromhm(out, center.numpy(), scale) + pts, pts_img = torch.from_numpy(pts), torch.from_numpy(pts_img) + pts, pts_img = pts.view(68, 2) * 4, pts_img.view(68, 2) + scores = scores.squeeze(0) + + if self.landmarks_type == LandmarksType._3D: + heatmaps = np.zeros((68, 256, 256), dtype=np.float32) + for i in range(68): + if pts[i, 0] > 0 and pts[i, 1] > 0: + heatmaps[i] = draw_gaussian( + heatmaps[i], pts[i], 2) + heatmaps = torch.from_numpy( + heatmaps).unsqueeze_(0) + + heatmaps = heatmaps.to(self.device) + depth_pred = self.depth_prediciton_net( + torch.cat((inp, heatmaps), 1)).data.cpu().view(68, 1) + pts_img = torch.cat( + (pts_img, depth_pred * (1.0 / (256.0 / (200.0 * scale)))), 1) + + landmarks.append(pts_img.numpy()) + landmarks_scores.append(scores) + + if not return_bboxes: + detected_faces = None + if not return_landmark_score: + landmarks_scores = None + if return_bboxes or return_landmark_score: + return landmarks, landmarks_scores, detected_faces + else: + return landmarks + + @torch.no_grad() + def get_landmarks_from_batch(self, image_batch, detected_faces=None, return_bboxes=False, + return_landmark_score=False): + """Predict the landmarks for each face present in the image. + + This function predicts a set of 68 2D or 3D images, one for each image in a batch in parallel. + If detect_faces is None the method will also run a face detector. + + Arguments: + image_batch {torch.tensor} -- The input images batch + + Keyword Arguments: + detected_faces {list of numpy.array} -- list of bounding boxes, one for each face found + in the image (default: {None}) + return_bboxes {boolean} -- If True, return the face bounding boxes in addition to the keypoints. + return_landmark_score {boolean} -- If True, return the keypoint scores along with the keypoints. + + Return: + result: + 1. if both return_bboxes and return_landmark_score are False, result will be: + landmarks + 2. Otherwise, result will be one of the following, depending on the actual value of return_* arguments. + (landmark, landmark_score, detected_face) + (landmark, None, detected_face) + (landmark, landmark_score, None ) + """ + + if detected_faces is None: + detected_faces = self.face_detector.detect_from_batch(image_batch) + + if len(detected_faces) == 0: + warnings.warn("No faces were detected.") + if return_bboxes or return_landmark_score: + return None, None, None + else: + return None + + landmarks = [] + landmarks_scores_list = [] + # A batch for each frame + for i, faces in enumerate(detected_faces): + res = self.get_landmarks_from_image( + image_batch[i].cpu().numpy().transpose(1, 2, 0), + detected_faces=faces, + return_landmark_score=return_landmark_score, + ) + if return_landmark_score: + landmark_set, landmarks_scores, _ = res + landmarks_scores_list.append(landmarks_scores) + else: + landmark_set = res + # Bacward compatibility + if landmark_set is not None: + landmark_set = np.concatenate(landmark_set, axis=0) + else: + landmark_set = [] + landmarks.append(landmark_set) + + if not return_bboxes: + detected_faces = None + if not return_landmark_score: + landmarks_scores_list = None + if return_bboxes or return_landmark_score: + return landmarks, landmarks_scores_list, detected_faces + else: + return landmarks + + def get_landmarks_from_directory(self, path, extensions=['.jpg', '.png'], recursive=True, show_progress_bar=True, + return_bboxes=False, return_landmark_score=False): + """Scan a directory for images with a given extension type(s) and predict the landmarks for each + face present in the images found. + + Arguments: + path {str} -- path to the target directory containing the images + + Keyword Arguments: + extensions {list of str} -- list containing the image extensions considered (default: ['.jpg', '.png']) + recursive {boolean} -- If True, scans for images recursively (default: True) + show_progress_bar {boolean} -- If True displays a progress bar (default: True) + return_bboxes {boolean} -- If True, return the face bounding boxes in addition to the keypoints. + return_landmark_score {boolean} -- If True, return the keypoint scores along with the keypoints. + """ + detected_faces = self.face_detector.detect_from_directory(path, extensions, recursive, show_progress_bar) + + predictions = {} + for image_path, bounding_boxes in detected_faces.items(): + image = io.imread(image_path) + if return_bboxes or return_landmark_score: + preds, bbox, score = self.get_landmarks_from_image( + image, bounding_boxes, return_bboxes=return_bboxes, return_landmark_score=return_landmark_score) + predictions[image_path] = (preds, bbox, score) + else: + preds = self.get_landmarks_from_image(image, bounding_boxes) + predictions[image_path] = preds + + return predictions diff --git a/dagan/face-alignment/face_alignment/detection/__init__.py b/dagan/face-alignment/face_alignment/detection/__init__.py new file mode 100644 index 00000000..1a6b0402 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/__init__.py @@ -0,0 +1 @@ +from .core import FaceDetector \ No newline at end of file diff --git a/dagan/face-alignment/face_alignment/detection/blazeface/__init__.py b/dagan/face-alignment/face_alignment/detection/blazeface/__init__.py new file mode 100644 index 00000000..e2a47085 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/blazeface/__init__.py @@ -0,0 +1 @@ +from .blazeface_detector import BlazeFaceDetector as FaceDetector diff --git a/dagan/face-alignment/face_alignment/detection/blazeface/blazeface_detector.py b/dagan/face-alignment/face_alignment/detection/blazeface/blazeface_detector.py new file mode 100644 index 00000000..2b71d4b1 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/blazeface/blazeface_detector.py @@ -0,0 +1,60 @@ +from torch.utils.model_zoo import load_url + +from ..core import FaceDetector +from ...utils import load_file_from_url + +from .net_blazeface import BlazeFace +from .detect import * + +models_urls = { + 'blazeface_weights': 'https://github.com/hollance/BlazeFace-PyTorch/blob/master/blazeface.pth?raw=true', + 'blazeface_anchors': 'https://github.com/hollance/BlazeFace-PyTorch/blob/master/anchors.npy?raw=true' +} + + +class BlazeFaceDetector(FaceDetector): + def __init__(self, device, path_to_detector=None, path_to_anchor=None, verbose=False, + min_score_thresh=0.5, min_suppression_threshold=0.3): + super(BlazeFaceDetector, self).__init__(device, verbose) + + # Initialise the face detector + if path_to_detector is None: + model_weights = load_url(models_urls['blazeface_weights']) + model_anchors = np.load(load_file_from_url(models_urls['blazeface_anchors'])) + else: + model_weights = torch.load(path_to_detector) + model_anchors = np.load(path_to_anchor) + + self.face_detector = BlazeFace() + self.face_detector.load_state_dict(model_weights) + self.face_detector.load_anchors_from_npy(model_anchors, device) + + # Optionally change the thresholds: + self.face_detector.min_score_thresh = min_score_thresh + self.face_detector.min_suppression_threshold = min_suppression_threshold + + self.face_detector.to(device) + self.face_detector.eval() + + def detect_from_image(self, tensor_or_path): + image = self.tensor_or_path_to_ndarray(tensor_or_path) + + bboxlist = detect(self.face_detector, image, device=self.device)[0] + + return bboxlist + + def detect_from_batch(self, tensor): + bboxlists = batch_detect(self.face_detector, tensor, device=self.device) + return bboxlists + + @property + def reference_scale(self): + return 195 + + @property + def reference_x_shift(self): + return 0 + + @property + def reference_y_shift(self): + return 0 diff --git a/dagan/face-alignment/face_alignment/detection/blazeface/detect.py b/dagan/face-alignment/face_alignment/detection/blazeface/detect.py new file mode 100644 index 00000000..cd1366a6 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/blazeface/detect.py @@ -0,0 +1,72 @@ +import torch +import torch.nn.functional as F + +import cv2 +import numpy as np + +from .utils import * + + +def detect(net, img, device): + H, W, C = img.shape + orig_size = min(H, W) + img, (xshift, yshift) = resize_and_crop_image(img, 128) + preds = net.predict_on_image(img) + + if 0 == len(preds): + return [[]] + + shift = np.array([xshift, yshift] * 2) + scores = preds[:, -1:] + + # TODO: ugly + # reverses, x and y to adapt with face-alignment code + locs = np.concatenate((preds[:, 1:2], preds[:, 0:1], preds[:, 3:4], preds[:, 2:3]), axis=1) + return [np.concatenate((locs * orig_size + shift, scores), axis=1)] + + +def batch_detect(net, img_batch, device): + """ + Inputs: + - img_batch: a numpy array or tensor of shape (Batch size, Channels, Height, Width) + Outputs: + - list of 2-dim numpy arrays with shape (faces_on_this_image, 5): x1, y1, x2, y2, confidence + (x1, y1) - top left corner, (x2, y2) - bottom right corner + """ + B, C, H, W = img_batch.shape + orig_size = min(H, W) + + if isinstance(img_batch, torch.Tensor): + img_batch = img_batch.cpu().numpy() + + img_batch = img_batch.transpose((0, 2, 3, 1)) + + imgs, (xshift, yshift) = resize_and_crop_batch(img_batch, 128) + preds = net.predict_on_batch(imgs) + bboxlists = [] + for pred in preds: + shift = np.array([xshift, yshift] * 2) + scores = pred[:, -1:] + locs = np.concatenate((pred[:, 1:2], pred[:, 0:1], pred[:, 3:4], pred[:, 2:3]), axis=1) + bboxlists.append(np.concatenate((locs * orig_size + shift, scores), axis=1)) + + return bboxlists + + +def flip_detect(net, img, device): + img = cv2.flip(img, 1) + b = detect(net, img, device) + + bboxlist = np.zeros(b.shape) + bboxlist[:, 0] = img.shape[1] - b[:, 2] + bboxlist[:, 1] = b[:, 1] + bboxlist[:, 2] = img.shape[1] - b[:, 0] + bboxlist[:, 3] = b[:, 3] + bboxlist[:, 4] = b[:, 4] + return bboxlist + + +def pts_to_bb(pts): + min_x, min_y = np.min(pts, axis=0) + max_x, max_y = np.max(pts, axis=0) + return np.array([min_x, min_y, max_x, max_y]) diff --git a/dagan/face-alignment/face_alignment/detection/blazeface/net_blazeface.py b/dagan/face-alignment/face_alignment/detection/blazeface/net_blazeface.py new file mode 100644 index 00000000..2d2f6037 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/blazeface/net_blazeface.py @@ -0,0 +1,431 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BlazeBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1): + super(BlazeBlock, self).__init__() + + self.stride = stride + self.channel_pad = out_channels - in_channels + + # TFLite uses slightly different padding than PyTorch + # on the depthwise conv layer when the stride is 2. + if stride == 2: + self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride) + padding = 0 + else: + padding = (kernel_size - 1) // 2 + + self.convs = nn.Sequential( + nn.Conv2d(in_channels=in_channels, out_channels=in_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + groups=in_channels, bias=True), + nn.Conv2d(in_channels=in_channels, out_channels=out_channels, + kernel_size=1, stride=1, padding=0, bias=True), + ) + + self.act = nn.ReLU(inplace=True) + + def forward(self, x): + if self.stride == 2: + h = F.pad(x, (0, 2, 0, 2), "constant", 0) + x = self.max_pool(x) + else: + h = x + + if self.channel_pad > 0: + x = F.pad(x, (0, 0, 0, 0, 0, self.channel_pad), "constant", 0) + + return self.act(self.convs(h) + x) + + +class BlazeFace(nn.Module): + """The BlazeFace face detection model from MediaPipe. + + The version from MediaPipe is simpler than the one in the paper; + it does not use the "double" BlazeBlocks. + + Because we won't be training this model, it doesn't need to have + batchnorm layers. These have already been "folded" into the conv + weights by TFLite. + + The conversion to PyTorch is fairly straightforward, but there are + some small differences between TFLite and PyTorch in how they handle + padding on conv layers with stride 2. + + This version works on batches, while the MediaPipe version can only + handle a single image at a time. + + Based on code from https://github.com/tkat0/PyTorch_BlazeFace/ and + https://github.com/google/mediapipe/ + """ + + def __init__(self): + super(BlazeFace, self).__init__() + + # These are the settings from the MediaPipe example graph + # mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt + self.num_classes = 1 + self.num_anchors = 896 + self.num_coords = 16 + self.score_clipping_thresh = 100.0 + self.x_scale = 128.0 + self.y_scale = 128.0 + self.h_scale = 128.0 + self.w_scale = 128.0 + self.min_score_thresh = 0.75 + self.min_suppression_threshold = 0.3 + + self._define_layers() + + def _define_layers(self): + self.backbone1 = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=24, kernel_size=5, + stride=2, padding=0, bias=True), + nn.ReLU(inplace=True), + + BlazeBlock(24, 24), + BlazeBlock(24, 28), + BlazeBlock(28, 32, stride=2), + BlazeBlock(32, 36), + BlazeBlock(36, 42), + BlazeBlock(42, 48, stride=2), + BlazeBlock(48, 56), + BlazeBlock(56, 64), + BlazeBlock(64, 72), + BlazeBlock(72, 80), + BlazeBlock(80, 88), + ) + + self.backbone2 = nn.Sequential( + BlazeBlock(88, 96, stride=2), + BlazeBlock(96, 96), + BlazeBlock(96, 96), + BlazeBlock(96, 96), + BlazeBlock(96, 96), + ) + + self.classifier_8 = nn.Conv2d(88, 2, 1, bias=True) + self.classifier_16 = nn.Conv2d(96, 6, 1, bias=True) + + self.regressor_8 = nn.Conv2d(88, 32, 1, bias=True) + self.regressor_16 = nn.Conv2d(96, 96, 1, bias=True) + + def forward(self, x): + # TFLite uses slightly different padding on the first conv layer + # than PyTorch, so do it manually. + x = F.pad(x, (1, 2, 1, 2), "constant", 0) + + b = x.shape[0] # batch size, needed for reshaping later + + x = self.backbone1(x) # (b, 88, 16, 16) + h = self.backbone2(x) # (b, 96, 8, 8) + + # Note: Because PyTorch is NCHW but TFLite is NHWC, we need to + # permute the output from the conv layers before reshaping it. + + c1 = self.classifier_8(x) # (b, 2, 16, 16) + c1 = c1.permute(0, 2, 3, 1) # (b, 16, 16, 2) + c1 = c1.reshape(b, -1, 1) # (b, 512, 1) + + c2 = self.classifier_16(h) # (b, 6, 8, 8) + c2 = c2.permute(0, 2, 3, 1) # (b, 8, 8, 6) + c2 = c2.reshape(b, -1, 1) # (b, 384, 1) + + c = torch.cat((c1, c2), dim=1) # (b, 896, 1) + + r1 = self.regressor_8(x) # (b, 32, 16, 16) + r1 = r1.permute(0, 2, 3, 1) # (b, 16, 16, 32) + r1 = r1.reshape(b, -1, 16) # (b, 512, 16) + + r2 = self.regressor_16(h) # (b, 96, 8, 8) + r2 = r2.permute(0, 2, 3, 1) # (b, 8, 8, 96) + r2 = r2.reshape(b, -1, 16) # (b, 384, 16) + + r = torch.cat((r1, r2), dim=1) # (b, 896, 16) + return [r, c] + + def _device(self): + """Which device (CPU or GPU) is being used by this model?""" + return self.classifier_8.weight.device + + def load_weights(self, path): + self.load_state_dict(torch.load(path)) + self.eval() + + def load_anchors(self, path, device=None): + device = device or self._device() + self.anchors = torch.tensor( + np.load(path), dtype=torch.float32, device=device) + assert(self.anchors.ndimension() == 2) + assert(self.anchors.shape[0] == self.num_anchors) + assert(self.anchors.shape[1] == 4) + + def load_anchors_from_npy(self, arr, device=None): + device = device or self._device() + self.anchors = torch.tensor( + arr, dtype=torch.float32, device=device) + assert(self.anchors.ndimension() == 2) + assert(self.anchors.shape[0] == self.num_anchors) + assert(self.anchors.shape[1] == 4) + + def _preprocess(self, x): + """Converts the image pixels to the range [-1, 1].""" + return x.float() / 127.5 - 1.0 + + def predict_on_image(self, img): + """Makes a prediction on a single image. + + Arguments: + img: a NumPy array of shape (H, W, 3) or a PyTorch tensor of + shape (3, H, W). The image's height and width should be + 128 pixels. + + Returns: + A tensor with face detections. + """ + if isinstance(img, np.ndarray): + img = torch.from_numpy(img).permute((2, 0, 1)) + + return self.predict_on_batch(img.unsqueeze(0))[0] + + def predict_on_batch(self, x): + """Makes a prediction on a batch of images. + + Arguments: + x: a NumPy array of shape (b, H, W, 3) or a PyTorch tensor of + shape (b, 3, H, W). The height and width should be 128 pixels. + + Returns: + A list containing a tensor of face detections for each image in + the batch. If no faces are found for an image, returns a tensor + of shape (0, 17). + + Each face detection is a PyTorch tensor consisting of 17 numbers: + - ymin, xmin, ymax, xmax + - x,y-coordinates for the 6 keypoints + - confidence score + """ + if isinstance(x, np.ndarray): + x = torch.from_numpy(x).permute((0, 3, 1, 2)) + + assert x.shape[1] == 3 + assert x.shape[2] == 128 + assert x.shape[3] == 128 + + # 1. Preprocess the images into tensors: + x = x.to(self._device()) + x = self._preprocess(x) + + # 2. Run the neural network: + with torch.no_grad(): + out = self.__call__(x) + + # 3. Postprocess the raw predictions: + detections = self._tensors_to_detections(out[0], out[1], self.anchors) + + # 4. Non-maximum suppression to remove overlapping detections: + filtered_detections = [] + for i in range(len(detections)): + faces = self._weighted_non_max_suppression(detections[i]) + faces = torch.stack(faces) if len( + faces) > 0 else torch.zeros((0, 17)) + filtered_detections.append(faces) + + return filtered_detections + + def _tensors_to_detections(self, raw_box_tensor, raw_score_tensor, anchors): + """The output of the neural network is a tensor of shape (b, 896, 16) + containing the bounding box regressor predictions, as well as a tensor + of shape (b, 896, 1) with the classification confidences. + + This function converts these two "raw" tensors into proper detections. + Returns a list of (num_detections, 17) tensors, one for each image in + the batch. + + This is based on the source_self code from: + mediapipe/calculators/tflite/tflite_tensors_to_detections_calculator.cc + mediapipe/calculators/tflite/tflite_tensors_to_detections_calculator.proto + """ + assert raw_box_tensor.ndimension() == 3 + assert raw_box_tensor.shape[1] == self.num_anchors + assert raw_box_tensor.shape[2] == self.num_coords + + assert raw_score_tensor.ndimension() == 3 + assert raw_score_tensor.shape[1] == self.num_anchors + assert raw_score_tensor.shape[2] == self.num_classes + + assert raw_box_tensor.shape[0] == raw_score_tensor.shape[0] + + detection_boxes = self._decode_boxes(raw_box_tensor, anchors) + + thresh = self.score_clipping_thresh + raw_score_tensor = raw_score_tensor.clamp(-thresh, thresh) + detection_scores = raw_score_tensor.sigmoid().squeeze(dim=-1) + + # Note: we stripped off the last dimension from the scores tensor + # because there is only has one class. Now we can simply use a mask + # to filter out the boxes with too low confidence. + mask = detection_scores >= self.min_score_thresh + + # Because each image from the batch can have a different number of + # detections, process them one at a time using a loop. + output_detections = [] + for i in range(raw_box_tensor.shape[0]): + boxes = detection_boxes[i, mask[i]] + scores = detection_scores[i, mask[i]].unsqueeze(dim=-1) + output_detections.append(torch.cat((boxes, scores), dim=-1).to('cpu')) + + return output_detections + + def _decode_boxes(self, raw_boxes, anchors): + """Converts the predictions into actual coordinates using + the anchor boxes. Processes the entire batch at once. + """ + boxes = torch.zeros_like(raw_boxes) + + x_center = raw_boxes[..., 0] / self.x_scale * \ + anchors[:, 2] + anchors[:, 0] + y_center = raw_boxes[..., 1] / self.y_scale * \ + anchors[:, 3] + anchors[:, 1] + + w = raw_boxes[..., 2] / self.w_scale * anchors[:, 2] + h = raw_boxes[..., 3] / self.h_scale * anchors[:, 3] + + boxes[..., 0] = y_center - h / 2. # ymin + boxes[..., 1] = x_center - w / 2. # xmin + boxes[..., 2] = y_center + h / 2. # ymax + boxes[..., 3] = x_center + w / 2. # xmax + + for k in range(6): + offset = 4 + k * 2 + keypoint_x = raw_boxes[..., offset] / \ + self.x_scale * anchors[:, 2] + anchors[:, 0] + keypoint_y = raw_boxes[..., offset + 1] / \ + self.y_scale * anchors[:, 3] + anchors[:, 1] + boxes[..., offset] = keypoint_x + boxes[..., offset + 1] = keypoint_y + + return boxes + + def _weighted_non_max_suppression(self, detections): + """The alternative NMS method as mentioned in the BlazeFace paper: + + "We replace the suppression algorithm with a blending strategy that + estimates the regression parameters of a bounding box as a weighted + mean between the overlapping predictions." + + The original MediaPipe code assigns the score of the most confident + detection to the weighted detection, but we take the average score + of the overlapping detections. + + The input detections should be a Tensor of shape (count, 17). + + Returns a list of PyTorch tensors, one for each detected face. + + This is based on the source_self code from: + mediapipe/calculators/util/non_max_suppression_calculator.cc + mediapipe/calculators/util/non_max_suppression_calculator.proto + """ + if len(detections) == 0: + return [] + + output_detections = [] + + # Sort the detections from highest to lowest score. + remaining = torch.argsort(detections[:, 16], descending=True) + + while len(remaining) > 0: + detection = detections[remaining[0]] + + # Compute the overlap between the first box and the other + # remaining boxes. (Note that the other_boxes also include + # the first_box.) + first_box = detection[:4] + other_boxes = detections[remaining, :4] + ious = overlap_similarity(first_box, other_boxes) + + # If two detections don't overlap enough, they are considered + # to be from different faces. + mask = ious > self.min_suppression_threshold + overlapping = remaining[mask] + remaining = remaining[~mask] + + # Take an average of the coordinates from the overlapping + # detections, weighted by their confidence scores. + weighted_detection = detection.clone() + if len(overlapping) > 1: + coordinates = detections[overlapping, :16] + scores = detections[overlapping, 16:17] + total_score = scores.sum() + weighted = (coordinates * scores).sum(dim=0) / total_score + weighted_detection[:16] = weighted + weighted_detection[16] = total_score / len(overlapping) + + output_detections.append(weighted_detection) + + return output_detections + + +# IOU code from https://github.com/amdegroot/ssd.pytorch/blob/master/layers/box_utils.py + +def intersect(box_a, box_b): + """ We resize both tensors to [A,B,2] without new malloc: + [A,2] -> [A,1,2] -> [A,B,2] + [B,2] -> [1,B,2] -> [A,B,2] + Then we compute the area of intersect between box_a and box_b. + Args: + box_a: (tensor) bounding boxes, Shape: [A,4]. + box_b: (tensor) bounding boxes, Shape: [B,4]. + Return: + (tensor) intersection area, Shape: [A,B]. + """ + A = box_a.size(0) + B = box_b.size(0) + max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), + box_b[:, 2:].unsqueeze(0).expand(A, B, 2)) + min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), + box_b[:, :2].unsqueeze(0).expand(A, B, 2)) + inter = torch.clamp((max_xy - min_xy), min=0) + return inter[:, :, 0] * inter[:, :, 1] + + +def jaccard(box_a, box_b): + """Compute the jaccard overlap of two sets of boxes. The jaccard overlap + is simply the intersection over union of two boxes. Here we operate on + ground truth boxes and default boxes. + E.g.: + A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) + Args: + box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4] + box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4] + Return: + jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)] + """ + inter = intersect(box_a, box_b) + area_a = ((box_a[:, 2] - box_a[:, 0]) + * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B] + area_b = ((box_b[:, 2] - box_b[:, 0]) + * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B] + union = area_a + area_b - inter + return inter / union # [A,B] + + +def overlap_similarity(box, other_boxes): + """Computes the IOU between a bounding box and set of other boxes.""" + return jaccard(box.unsqueeze(0), other_boxes).squeeze(0) + + +def init_model(): + net = BlazeFace().to('cuda') + net.load_weights("BlazeFace/params/blazeface.pth") + net.load_anchors("BlazeFace/params/anchors.npy") + + # Optionally change the thresholds: + net.min_score_thresh = 0.75 + net.min_suppression_threshold = 0.3 + + return net diff --git a/dagan/face-alignment/face_alignment/detection/blazeface/utils.py b/dagan/face-alignment/face_alignment/detection/blazeface/utils.py new file mode 100644 index 00000000..7596a8cb --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/blazeface/utils.py @@ -0,0 +1,65 @@ +import cv2 +import numpy as np + + +def image_resize(image, width=None, height=None, inter=cv2.INTER_AREA): + # initialize the dimensions of the image to be resized and + # grab the image size + dim = None + (h, w) = image.shape[:2] + + # if both the width and height are None, then return the + # original image + if width is None and height is None: + return image + + # check to see if the width is None + if width is None: + # calculate the ratio of the height and construct the + # dimensions + r = height / float(h) + dim = (int(w * r), height) + + # otherwise, the height is None + else: + # calculate the ratio of the width and construct the + # dimensions + r = width / float(w) + dim = (width, int(h * r)) + + # resize the image + resized = cv2.resize(image, dim, interpolation=inter) + + # return the resized image + return resized + + +def resize_and_crop_image(image, dim): + if image.shape[0] > image.shape[1]: + img = image_resize(image, width=dim) + yshift, xshift = (image.shape[0] - image.shape[1]) // 2, 0 + y_start = (img.shape[0] - img.shape[1]) // 2 + y_end = y_start + dim + return img[y_start:y_end, :, :], (xshift, yshift) + else: + img = image_resize(image, height=dim) + yshift, xshift = 0, (image.shape[1] - image.shape[0]) // 2 + x_start = (img.shape[1] - img.shape[0]) // 2 + x_end = x_start + dim + return img[:, x_start:x_end, :], (xshift, yshift) + + +def resize_and_crop_batch(frames, dim): + """ + Center crop + resize to (dim x dim) + inputs: + - frames: list of images (numpy arrays) + - dim: output dimension size + """ + smframes = [] + xshift, yshift = 0, 0 + for i in range(len(frames)): + smframe, (xshift, yshift) = resize_and_crop_image(frames[i], dim) + smframes.append(smframe) + smframes = np.stack(smframes) + return smframes, (xshift, yshift) diff --git a/dagan/face-alignment/face_alignment/detection/core.py b/dagan/face-alignment/face_alignment/detection/core.py new file mode 100644 index 00000000..6ced06ba --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/core.py @@ -0,0 +1,150 @@ +import logging +import glob +from tqdm import tqdm +import numpy as np +import torch +from skimage import io + + +class FaceDetector(object): + """An abstract class representing a face detector. + + Any other face detection implementation must subclass it. All subclasses + must implement ``detect_from_image``, that return a list of detected + bounding boxes. Optionally, for speed considerations detect from path is + recommended. + """ + + def __init__(self, device, verbose): + self.device = device + self.verbose = verbose + + if verbose: + if 'cpu' in device: + logger = logging.getLogger(__name__) + logger.warning("Detection running on CPU, this may be potentially slow.") + + if 'cpu' not in device and 'cuda' not in device: + if verbose: + logger.error("Expected values for device are: {cpu, cuda} but got: %s", device) + raise ValueError + + def detect_from_image(self, tensor_or_path): + """Detects faces in a given image. + + This function detects the faces present in a provided BGR(usually) + image. The input can be either the image itself or the path to it. + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- the path + to an image or the image itself. + + Example:: + + >>> path_to_image = 'data/image_01.jpg' + ... detected_faces = detect_from_image(path_to_image) + [A list of bounding boxes (x1, y1, x2, y2)] + >>> image = cv2.imread(path_to_image) + ... detected_faces = detect_from_image(image) + [A list of bounding boxes (x1, y1, x2, y2)] + + """ + raise NotImplementedError + + def detect_from_batch(self, tensor): + """Detects faces in a given image. + + This function detects the faces present in a provided BGR(usually) + image. The input can be either the image itself or the path to it. + + Arguments: + tensor {torch.tensor} -- image batch tensor. + + Example:: + + >>> path_to_image = 'data/image_01.jpg' + ... detected_faces = detect_from_image(path_to_image) + [A list of bounding boxes (x1, y1, x2, y2)] + >>> image = cv2.imread(path_to_image) + ... detected_faces = detect_from_image(image) + [A list of bounding boxes (x1, y1, x2, y2)] + + """ + raise NotImplementedError + + def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True): + """Detects faces from all the images present in a given directory. + + Arguments: + path {string} -- a string containing a path that points to the folder containing the images + + Keyword Arguments: + extensions {list} -- list of string containing the extensions to be + consider in the following format: ``.extension_name`` (default: + {['.jpg', '.png']}) recursive {bool} -- option wherever to scan the + folder recursively (default: {False}) show_progress_bar {bool} -- + display a progressbar (default: {True}) + + Example: + >>> directory = 'data' + ... detected_faces = detect_from_directory(directory) + {A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]} + + """ + if self.verbose: + logger = logging.getLogger(__name__) + + if len(extensions) == 0: + if self.verbose: + logger.error("Expected at list one extension, but none was received.") + raise ValueError + + if self.verbose: + logger.info("Constructing the list of images.") + additional_pattern = '/**/*' if recursive else '/*' + files = [] + for extension in extensions: + files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive)) + + if self.verbose: + logger.info("Finished searching for images. %s images found", len(files)) + logger.info("Preparing to run the detection.") + + predictions = {} + for image_path in tqdm(files, disable=not show_progress_bar): + if self.verbose: + logger.info("Running the face detector on image: %s", image_path) + predictions[image_path] = self.detect_from_image(image_path) + + if self.verbose: + logger.info("The detector was successfully run on all %s images", len(files)) + + return predictions + + @property + def reference_scale(self): + raise NotImplementedError + + @property + def reference_x_shift(self): + raise NotImplementedError + + @property + def reference_y_shift(self): + raise NotImplementedError + + @staticmethod + def tensor_or_path_to_ndarray(tensor_or_path): + """Convert path (represented as a string) or torch.tensor to a numpy.ndarray + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself + """ + if isinstance(tensor_or_path, str): + return io.imread(tensor_or_path) + elif torch.is_tensor(tensor_or_path): + return tensor_or_path.cpu().numpy() + elif isinstance(tensor_or_path, np.ndarray): + return tensor_or_path + else: + raise TypeError diff --git a/dagan/face-alignment/face_alignment/detection/dlib/__init__.py b/dagan/face-alignment/face_alignment/detection/dlib/__init__.py new file mode 100644 index 00000000..d8e5ee5f --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/dlib/__init__.py @@ -0,0 +1 @@ +from .dlib_detector import DlibDetector as FaceDetector \ No newline at end of file diff --git a/dagan/face-alignment/face_alignment/detection/dlib/dlib_detector.py b/dagan/face-alignment/face_alignment/detection/dlib/dlib_detector.py new file mode 100644 index 00000000..0af33167 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/dlib/dlib_detector.py @@ -0,0 +1,48 @@ +import warnings +import cv2 +import dlib + +from ..core import FaceDetector +from ...utils import load_file_from_url + + +class DlibDetector(FaceDetector): + def __init__(self, device, path_to_detector=None, verbose=False): + super().__init__(device, verbose) + + warnings.warn('Warning: this detector is deprecated. Please use a different one, i.e.: S3FD.') + + # Initialise the face detector + if 'cuda' in device: + if path_to_detector is None: + path_to_detector = load_file_from_url( + "https://www.adrianbulat.com/downloads/dlib/mmod_human_face_detector.dat") + + self.face_detector = dlib.cnn_face_detection_model_v1(path_to_detector) + else: + self.face_detector = dlib.get_frontal_face_detector() + + def detect_from_image(self, tensor_or_path): + image = self.tensor_or_path_to_ndarray(tensor_or_path) + image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + detected_faces = self.face_detector(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)) + + if 'cuda' not in self.device: + detected_faces = [[d.left(), d.top(), d.right(), d.bottom()] for d in detected_faces] + else: + detected_faces = [[d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom()] for d in detected_faces] + + return detected_faces + + @property + def reference_scale(self): + return 195 + + @property + def reference_x_shift(self): + return 0 + + @property + def reference_y_shift(self): + return 0 diff --git a/dagan/face-alignment/face_alignment/detection/folder/__init__.py b/dagan/face-alignment/face_alignment/detection/folder/__init__.py new file mode 100644 index 00000000..0a9128ee --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/folder/__init__.py @@ -0,0 +1 @@ +from .folder_detector import FolderDetector as FaceDetector \ No newline at end of file diff --git a/dagan/face-alignment/face_alignment/detection/folder/folder_detector.py b/dagan/face-alignment/face_alignment/detection/folder/folder_detector.py new file mode 100644 index 00000000..add19fa4 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/folder/folder_detector.py @@ -0,0 +1,53 @@ +import os +import numpy as np +import torch + +from ..core import FaceDetector + + +class FolderDetector(FaceDetector): + '''This is a simple helper module that assumes the faces were detected already + (either previously or are provided as ground truth). + + The class expects to find the bounding boxes in the same format used by + the rest of face detectors, mainly ``list[(x1,y1,x2,y2),...]``. + For each image the detector will search for a file with the same name and with one of the + following extensions: .npy, .t7 or .pth + + ''' + + def __init__(self, device, path_to_detector=None, verbose=False): + super(FolderDetector, self).__init__(device, verbose) + + def detect_from_image(self, tensor_or_path): + # Only strings supported + if not isinstance(tensor_or_path, str): + raise ValueError + + base_name = os.path.splitext(tensor_or_path)[0] + + if os.path.isfile(base_name + '.npy'): + detected_faces = np.load(base_name + '.npy') + elif os.path.isfile(base_name + '.t7'): + detected_faces = torch.load(base_name + '.t7') + elif os.path.isfile(base_name + '.pth'): + detected_faces = torch.load(base_name + '.pth') + else: + raise FileNotFoundError + + if not isinstance(detected_faces, list): + raise TypeError + + return detected_faces + + @property + def reference_scale(self): + return 195 + + @property + def reference_x_shift(self): + return 0 + + @property + def reference_y_shift(self): + return 0 diff --git a/dagan/face-alignment/face_alignment/detection/sfd/__init__.py b/dagan/face-alignment/face_alignment/detection/sfd/__init__.py new file mode 100644 index 00000000..5a63ecd4 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/sfd/__init__.py @@ -0,0 +1 @@ +from .sfd_detector import SFDDetector as FaceDetector \ No newline at end of file diff --git a/dagan/face-alignment/face_alignment/detection/sfd/bbox.py b/dagan/face-alignment/face_alignment/detection/sfd/bbox.py new file mode 100644 index 00000000..3d266963 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/sfd/bbox.py @@ -0,0 +1,71 @@ +import math +import numpy as np + + +def nms(dets, thresh): + if 0 == len(dets): + return [] + x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4] + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]]) + xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]]) + + w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1) + ovr = w * h / (areas[i] + areas[order[1:]] - w * h) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + +def encode(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 4]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded boxes (tensor), Shape: [num_priors, 4] + """ + + # dist b/t match center and prior's center + g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, 2:]) + # match wh / prior wh + g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] + g_wh = np.log(g_wh) / variances[1] + + # return target for smooth_l1_loss + return np.concatenate([g_cxcy, g_wh], 1) # [num_priors,4] + + +def decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = np.concatenate(( + priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * np.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes diff --git a/dagan/face-alignment/face_alignment/detection/sfd/detect.py b/dagan/face-alignment/face_alignment/detection/sfd/detect.py new file mode 100644 index 00000000..143071f9 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/sfd/detect.py @@ -0,0 +1,87 @@ +import torch +import torch.nn.functional as F + +import cv2 +import numpy as np + +from .bbox import * + + +def detect(net, img, device): + img = img.transpose(2, 0, 1) + # Creates a batch of 1 + img = np.expand_dims(img, 0) + + img = torch.from_numpy(img.copy()).to(device, dtype=torch.float32) + + return batch_detect(net, img, device) + + +def batch_detect(net, img_batch, device): + """ + Inputs: + - img_batch: a torch.Tensor of shape (Batch size, Channels, Height, Width) + """ + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + batch_size = img_batch.size(0) + img_batch = img_batch.to(device, dtype=torch.float32) + + img_batch = img_batch.flip(-3) # RGB to BGR + img_batch = img_batch - torch.tensor([104.0, 117.0, 123.0], device=device).view(1, 3, 1, 1) + + with torch.no_grad(): + olist = net(img_batch) # patched uint8_t overflow error + + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + + olist = [oelem.data.cpu().numpy() for oelem in olist] + + bboxlists = get_predictions(olist, batch_size) + return bboxlists + + +def get_predictions(olist, batch_size): + bboxlists = [] + variances = [0.1, 0.2] + for j in range(batch_size): + bboxlist = [] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + stride = 2**(i + 2) # 4,8,16,32,64,128 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[j, 1, hindex, windex] + loc = oreg[j, :, hindex, windex].copy().reshape(1, 4) + priors = np.array([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]) + box = decode(loc, priors, variances) + x1, y1, x2, y2 = box[0] + bboxlist.append([x1, y1, x2, y2, score]) + + bboxlists.append(bboxlist) + + bboxlists = np.array(bboxlists) + return bboxlists + + +def flip_detect(net, img, device): + img = cv2.flip(img, 1) + b = detect(net, img, device) + + bboxlist = np.zeros(b.shape) + bboxlist[:, 0] = img.shape[1] - b[:, 2] + bboxlist[:, 1] = b[:, 1] + bboxlist[:, 2] = img.shape[1] - b[:, 0] + bboxlist[:, 3] = b[:, 3] + bboxlist[:, 4] = b[:, 4] + return bboxlist + + +def pts_to_bb(pts): + min_x, min_y = np.min(pts, axis=0) + max_x, max_y = np.max(pts, axis=0) + return np.array([min_x, min_y, max_x, max_y]) diff --git a/dagan/face-alignment/face_alignment/detection/sfd/net_s3fd.py b/dagan/face-alignment/face_alignment/detection/sfd/net_s3fd.py new file mode 100644 index 00000000..ecfa0f65 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/sfd/net_s3fd.py @@ -0,0 +1,127 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class L2Norm(nn.Module): + def __init__(self, n_channels, scale=1.0): + super(L2Norm, self).__init__() + self.n_channels = n_channels + self.scale = scale + self.eps = 1e-10 + self.weight = nn.Parameter(torch.empty(self.n_channels).fill_(self.scale)) + + def forward(self, x): + norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps + x = x / norm * self.weight.view(1, -1, 1, 1) + return x + + +class s3fd(nn.Module): + def __init__(self): + super(s3fd, self).__init__() + self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) + self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) + + self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) + self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) + + self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) + self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) + self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) + + self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1) + self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + + self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + + self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3) + self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0) + + self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0) + self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1) + + self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0) + self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) + + self.conv3_3_norm = L2Norm(256, scale=10) + self.conv4_3_norm = L2Norm(512, scale=8) + self.conv5_3_norm = L2Norm(512, scale=5) + + self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + + self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1) + self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1) + self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1) + self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + h = F.relu(self.conv1_1(x), inplace=True) + h = F.relu(self.conv1_2(h), inplace=True) + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv2_1(h), inplace=True) + h = F.relu(self.conv2_2(h), inplace=True) + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv3_1(h), inplace=True) + h = F.relu(self.conv3_2(h), inplace=True) + h = F.relu(self.conv3_3(h), inplace=True) + f3_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv4_1(h), inplace=True) + h = F.relu(self.conv4_2(h), inplace=True) + h = F.relu(self.conv4_3(h), inplace=True) + f4_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv5_1(h), inplace=True) + h = F.relu(self.conv5_2(h), inplace=True) + h = F.relu(self.conv5_3(h), inplace=True) + f5_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.fc6(h), inplace=True) + h = F.relu(self.fc7(h), inplace=True) + ffc7 = h + h = F.relu(self.conv6_1(h), inplace=True) + h = F.relu(self.conv6_2(h), inplace=True) + f6_2 = h + h = F.relu(self.conv7_1(h), inplace=True) + h = F.relu(self.conv7_2(h), inplace=True) + f7_2 = h + + f3_3 = self.conv3_3_norm(f3_3) + f4_3 = self.conv4_3_norm(f4_3) + f5_3 = self.conv5_3_norm(f5_3) + + cls1 = self.conv3_3_norm_mbox_conf(f3_3) + reg1 = self.conv3_3_norm_mbox_loc(f3_3) + cls2 = self.conv4_3_norm_mbox_conf(f4_3) + reg2 = self.conv4_3_norm_mbox_loc(f4_3) + cls3 = self.conv5_3_norm_mbox_conf(f5_3) + reg3 = self.conv5_3_norm_mbox_loc(f5_3) + cls4 = self.fc7_mbox_conf(ffc7) + reg4 = self.fc7_mbox_loc(ffc7) + cls5 = self.conv6_2_mbox_conf(f6_2) + reg5 = self.conv6_2_mbox_loc(f6_2) + cls6 = self.conv7_2_mbox_conf(f7_2) + reg6 = self.conv7_2_mbox_loc(f7_2) + + # max-out background label + chunk = torch.chunk(cls1, 4, 1) + bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2]) + cls1 = torch.cat([bmax, chunk[3]], dim=1) + + return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6] diff --git a/dagan/face-alignment/face_alignment/detection/sfd/sfd_detector.py b/dagan/face-alignment/face_alignment/detection/sfd/sfd_detector.py new file mode 100644 index 00000000..3799c435 --- /dev/null +++ b/dagan/face-alignment/face_alignment/detection/sfd/sfd_detector.py @@ -0,0 +1,71 @@ +import torch +from torch.utils.model_zoo import load_url + +from ..core import FaceDetector + +from .net_s3fd import s3fd +from .bbox import nms +from .detect import detect, batch_detect + +models_urls = { + 's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth', +} + + +class SFDDetector(FaceDetector): + '''SF3D Detector. + ''' + + def __init__(self, device, path_to_detector=None, verbose=False, filter_threshold=0.5): + super(SFDDetector, self).__init__(device, verbose) + + # Initialise the face detector + if path_to_detector is None: + model_weights = load_url(models_urls['s3fd']) + else: + model_weights = torch.load(path_to_detector) + + self.fiter_threshold = filter_threshold + self.face_detector = s3fd() + self.face_detector.load_state_dict(model_weights) + self.face_detector.to(device) + self.face_detector.eval() + + def _filter_bboxes(self, bboxlist): + if len(bboxlist) > 0: + keep = nms(bboxlist, 0.3) + bboxlist = bboxlist[keep, :] + bboxlist = [x for x in bboxlist if x[-1] > self.fiter_threshold] + + return bboxlist + + def detect_from_image(self, tensor_or_path): + image = self.tensor_or_path_to_ndarray(tensor_or_path) + + bboxlist = detect(self.face_detector, image, device=self.device)[0] + bboxlist = self._filter_bboxes(bboxlist) + + return bboxlist + + def detect_from_batch(self, tensor): + bboxlists = batch_detect(self.face_detector, tensor, device=self.device) + + new_bboxlists = [] + for i in range(bboxlists.shape[0]): + bboxlist = bboxlists[i] + bboxlist = self._filter_bboxes(bboxlist) + new_bboxlists.append(bboxlist) + + return new_bboxlists + + @property + def reference_scale(self): + return 195 + + @property + def reference_x_shift(self): + return 0 + + @property + def reference_y_shift(self): + return 0 diff --git a/dagan/face-alignment/face_alignment/utils.py b/dagan/face-alignment/face_alignment/utils.py new file mode 100644 index 00000000..2618770b --- /dev/null +++ b/dagan/face-alignment/face_alignment/utils.py @@ -0,0 +1,379 @@ +import os +import sys +import errno +import torch +import math +import numpy as np +import cv2 +from skimage import io +from skimage import color +from numba import jit + +from urllib.parse import urlparse +from torch.hub import download_url_to_file, HASH_REGEX +try: + from torch.hub import get_dir +except BaseException: + from torch.hub import _get_torch_home as get_dir + +gauss_kernel = None + + +def _gaussian( + size=3, sigma=0.25, amplitude=1, normalize=False, width=None, + height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5, + mean_vert=0.5): + # handle some defaults + if width is None: + width = size + if height is None: + height = size + if sigma_horz is None: + sigma_horz = sigma + if sigma_vert is None: + sigma_vert = sigma + center_x = mean_horz * width + 0.5 + center_y = mean_vert * height + 0.5 + gauss = np.empty((height, width), dtype=np.float32) + # generate kernel + for i in range(height): + for j in range(width): + gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / ( + sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0)) + if normalize: + gauss = gauss / np.sum(gauss) + return gauss + + +def draw_gaussian(image, point, sigma): + global gauss_kernel + # Check if the gaussian is inside + ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)] + br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)] + if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1): + return image + size = 6 * sigma + 1 + if gauss_kernel is None: + g = _gaussian(size) + gauss_kernel = g + else: + g = gauss_kernel + g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))] + g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))] + img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))] + img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))] + assert (g_x[0] > 0 and g_y[1] > 0) + image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1] + ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]] + image[image > 1] = 1 + return image + + +def transform(point, center, scale, resolution, invert=False): + """Generate and affine transformation matrix. + + Given a set of points, a center, a scale and a targer resolution, the + function generates and affine transformation matrix. If invert is ``True`` + it will produce the inverse transformation. + + Arguments: + point {torch.tensor} -- the input 2D point + center {torch.tensor or numpy.array} -- the center around which to perform the transformations + scale {float} -- the scale of the face/object + resolution {float} -- the output resolution + + Keyword Arguments: + invert {bool} -- define wherever the function should produce the direct or the + inverse transformation matrix (default: {False}) + """ + _pt = torch.ones(3) + _pt[0] = point[0] + _pt[1] = point[1] + + h = 200.0 * scale + t = torch.eye(3) + t[0, 0] = resolution / h + t[1, 1] = resolution / h + t[0, 2] = resolution * (-center[0] / h + 0.5) + t[1, 2] = resolution * (-center[1] / h + 0.5) + + if invert: + t = torch.inverse(t) + + new_point = (torch.matmul(t, _pt))[0:2] + + return new_point.int() + + +def crop(image, center, scale, resolution=256.0): + """Center crops an image or set of heatmaps + + Arguments: + image {numpy.array} -- an rgb image + center {numpy.array} -- the center of the object, usually the same as of the bounding box + scale {float} -- scale of the face + + Keyword Arguments: + resolution {float} -- the size of the output cropped image (default: {256.0}) + + Returns: + [type] -- [description] + """ # Crop around the center point + """ Crops the image around the center. Input is expected to be an np.ndarray """ + ul = transform([1, 1], center, scale, resolution, True) + br = transform([resolution, resolution], center, scale, resolution, True) + # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0) + if image.ndim > 2: + newDim = np.array([br[1] - ul[1], br[0] - ul[0], + image.shape[2]], dtype=np.int32) + newImg = np.zeros(newDim, dtype=np.uint8) + else: + newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int) + newImg = np.zeros(newDim, dtype=np.uint8) + ht = image.shape[0] + wd = image.shape[1] + newX = np.array( + [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32) + newY = np.array( + [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32) + oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32) + oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32) + newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1] + ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :] + newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)), + interpolation=cv2.INTER_LINEAR) + return newImg + + +@jit(nopython=True) +def transform_np(point, center, scale, resolution, invert=False): + """Generate and affine transformation matrix. + + Given a set of points, a center, a scale and a targer resolution, the + function generates and affine transformation matrix. If invert is ``True`` + it will produce the inverse transformation. + + Arguments: + point {numpy.array} -- the input 2D point + center {numpy.array} -- the center around which to perform the transformations + scale {float} -- the scale of the face/object + resolution {float} -- the output resolution + + Keyword Arguments: + invert {bool} -- define wherever the function should produce the direct or the + inverse transformation matrix (default: {False}) + """ + _pt = np.ones(3) + _pt[0] = point[0] + _pt[1] = point[1] + + h = 200.0 * scale + t = np.eye(3) + t[0, 0] = resolution / h + t[1, 1] = resolution / h + t[0, 2] = resolution * (-center[0] / h + 0.5) + t[1, 2] = resolution * (-center[1] / h + 0.5) + + if invert: + t = np.ascontiguousarray(np.linalg.pinv(t)) + + new_point = np.dot(t, _pt)[0:2] + + return new_point.astype(np.int32) + + +def get_preds_fromhm(hm, center=None, scale=None): + """Obtain (x,y) coordinates given a set of N heatmaps. If the center + and the scale is provided the function will return the points also in + the original coordinate frame. + + Arguments: + hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] + + Keyword Arguments: + center {torch.tensor} -- the center of the bounding box (default: {None}) + scale {float} -- face scale (default: {None}) + """ + B, C, H, W = hm.shape + hm_reshape = hm.reshape(B, C, H * W) + idx = np.argmax(hm_reshape, axis=-1) + scores = np.take_along_axis(hm_reshape, np.expand_dims(idx, axis=-1), axis=-1).squeeze(-1) + preds, preds_orig = _get_preds_fromhm(hm, idx, center, scale) + + return preds, preds_orig, scores + + +@jit(nopython=True) +def _get_preds_fromhm(hm, idx, center=None, scale=None): + """Obtain (x,y) coordinates given a set of N heatmaps and the + coresponding locations of the maximums. If the center + and the scale is provided the function will return the points also in + the original coordinate frame. + + Arguments: + hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] + + Keyword Arguments: + center {torch.tensor} -- the center of the bounding box (default: {None}) + scale {float} -- face scale (default: {None}) + """ + B, C, H, W = hm.shape + idx += 1 + preds = idx.repeat(2).reshape(B, C, 2).astype(np.float32) + preds[:, :, 0] = (preds[:, :, 0] - 1) % W + 1 + preds[:, :, 1] = np.floor((preds[:, :, 1] - 1) / H) + 1 + + for i in range(B): + for j in range(C): + hm_ = hm[i, j, :] + pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 + if pX > 0 and pX < 63 and pY > 0 and pY < 63: + diff = np.array( + [hm_[pY, pX + 1] - hm_[pY, pX - 1], + hm_[pY + 1, pX] - hm_[pY - 1, pX]]) + preds[i, j] += np.sign(diff) * 0.25 + + preds -= 0.5 + + preds_orig = np.zeros_like(preds) + if center is not None and scale is not None: + for i in range(B): + for j in range(C): + preds_orig[i, j] = transform_np( + preds[i, j], center, scale, H, True) + + return preds, preds_orig + + +def create_target_heatmap(target_landmarks, centers, scales): + heatmaps = np.zeros((target_landmarks.shape[0], 68, 64, 64), dtype=np.float32) + for i in range(heatmaps.shape[0]): + for p in range(68): + landmark_cropped_coor = transform(target_landmarks[i, p] + 1, centers[i], scales[i], 64, invert=False) + heatmaps[i, p] = draw_gaussian(heatmaps[i, p], landmark_cropped_coor + 1, 2) + return torch.tensor(heatmaps) + + +def create_bounding_box(target_landmarks, expansion_factor=0.0): + """ + gets a batch of landmarks and calculates a bounding box that includes all the landmarks per set of landmarks in + the batch + :param target_landmarks: batch of landmarks of dim (n x 68 x 2). Where n is the batch size + :param expansion_factor: expands the bounding box by this factor. For example, a `expansion_factor` of 0.2 leads + to 20% increase in width and height of the boxes + :return: a batch of bounding boxes of dim (n x 4) where the second dim is (x1,y1,x2,y2) + """ + # Calc bounding box + x_y_min, _ = target_landmarks.reshape(-1, 68, 2).min(dim=1) + x_y_max, _ = target_landmarks.reshape(-1, 68, 2).max(dim=1) + # expanding the bounding box + expansion_factor /= 2 + bb_expansion_x = (x_y_max[:, 0] - x_y_min[:, 0]) * expansion_factor + bb_expansion_y = (x_y_max[:, 1] - x_y_min[:, 1]) * expansion_factor + x_y_min[:, 0] -= bb_expansion_x + x_y_max[:, 0] += bb_expansion_x + x_y_min[:, 1] -= bb_expansion_y + x_y_max[:, 1] += bb_expansion_y + return torch.cat([x_y_min, x_y_max], dim=1) + + +def shuffle_lr(parts, pairs=None): + """Shuffle the points left-right according to the axis of symmetry + of the object. + + Arguments: + parts {torch.tensor} -- a 3D or 4D object containing the + heatmaps. + + Keyword Arguments: + pairs {list of integers} -- [order of the flipped points] (default: {None}) + """ + if pairs is None: + pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, + 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35, + 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41, + 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63, + 62, 61, 60, 67, 66, 65] + if parts.ndimension() == 3: + parts = parts[pairs, ...] + else: + parts = parts[:, pairs, ...] + + return parts + + +def flip(tensor, is_label=False): + """Flip an image or a set of heatmaps left-right + + Arguments: + tensor {numpy.array or torch.tensor} -- [the input image or heatmaps] + + Keyword Arguments: + is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False}) + """ + if not torch.is_tensor(tensor): + tensor = torch.from_numpy(tensor) + + if is_label: + tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1) + else: + tensor = tensor.flip(tensor.ndimension() - 1) + + return tensor + + +def get_image(image_or_path): + """Reads an image from file or array/tensor and converts it to RGB (H,W,3). + + Arguments: + tensor {Sstring, numpy.array or torch.tensor} -- [the input image or path to it] + """ + if isinstance(image_or_path, str): + try: + image = io.imread(image_or_path) + except IOError: + print("error opening file :: ", image_or_path) + return None + elif isinstance(image_or_path, torch.Tensor): + image = image_or_path.detach().cpu().numpy() + else: + image = image_or_path + + if image.ndim == 2: + image = color.gray2rgb(image) + elif image.ndim == 4: + image = image[..., :3] + + return image + + +# Pytorch load supports only pytorch models +def load_file_from_url(url, model_dir=None, progress=True, check_hash=False, file_name=None): + if model_dir is None: + hub_dir = get_dir() + model_dir = os.path.join(hub_dir, 'checkpoints') + + try: + os.makedirs(model_dir) + except OSError as e: + if e.errno == errno.EEXIST: + # Directory already exists, ignore. + pass + else: + # Unexpected OSError, re-raise. + raise + + parts = urlparse(url) + filename = os.path.basename(parts.path) + if file_name is not None: + filename = file_name + cached_file = os.path.join(model_dir, filename) + if not os.path.exists(cached_file): + sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file)) + hash_prefix = None + if check_hash: + r = HASH_REGEX.search(filename) # r is Optional[Match[str]] + hash_prefix = r.group(1) if r else None + download_url_to_file(url, cached_file, hash_prefix, progress=progress) + + return cached_file diff --git a/dagan/face-alignment/requirements.txt b/dagan/face-alignment/requirements.txt new file mode 100644 index 00000000..499d4248 --- /dev/null +++ b/dagan/face-alignment/requirements.txt @@ -0,0 +1,4 @@ +opencv-python +scipy>=0.17.0 +scikit-image +numba diff --git a/dagan/face-alignment/setup.cfg b/dagan/face-alignment/setup.cfg new file mode 100644 index 00000000..635ce833 --- /dev/null +++ b/dagan/face-alignment/setup.cfg @@ -0,0 +1,32 @@ +[bumpversion] +current_version = 1.3.4 +commit = True +tag = True + +[bumpversion:file:setup.py] +search = version='{current_version}' +replace = version='{new_version}' + +[bumpversion:file:face_alignment/__init__.py] +search = __version__ = '{current_version}' +replace = __version__ = '{new_version}' + +[metadata] +description_file = README.md + +[bdist_wheel] +universal = 1 + +[flake8] +exclude = + .github, + examples, + docs, + .tox, + bin, + dist, + tools, + *.egg-info, + __init__.py, + *.yml +max-line-length = 160 \ No newline at end of file diff --git a/dagan/face-alignment/setup.py b/dagan/face-alignment/setup.py new file mode 100644 index 00000000..48bf9e3f --- /dev/null +++ b/dagan/face-alignment/setup.py @@ -0,0 +1,85 @@ +import io +import os +from os import path +import re +from setuptools import setup, find_packages +# To use consisten encodings +from codecs import open + +# Function from: https://github.com/pytorch/vision/blob/master/setup.py + + +def read(*names, **kwargs): + with io.open( + os.path.join(os.path.dirname(__file__), *names), + encoding=kwargs.get("encoding", "utf8") + ) as fp: + return fp.read() + +# Function from: https://github.com/pytorch/vision/blob/master/setup.py + + +def find_version(*file_paths): + version_file = read(*file_paths) + version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", + version_file, re.M) + if version_match: + return version_match.group(1) + raise RuntimeError("Unable to find version string.") + +here = path.abspath(path.dirname(__file__)) + +# Get the long description from the README file +with open(path.join(here, 'README.md'), encoding='utf-8') as readme_file: + long_description = readme_file.read() + +VERSION = find_version('face_alignment', '__init__.py') + +requirements = [ + 'torch', + 'numpy', + 'scipy>=0.17', + 'scikit-image', + 'opencv-python', + 'tqdm', + 'numba', + 'enum34;python_version<"3.4"' +] + +setup( + name='face_alignment', + version=VERSION, + + description="Detector 2D or 3D face landmarks from Python", + long_description=long_description, + long_description_content_type="text/markdown", + + # Author details + author="Adrian Bulat", + author_email="adrian@adrianbulat.com", + url="https://github.com/1adrianb/face-alignment", + + # Package info + packages=find_packages(exclude=('test',)), + + python_requires='>=3', + install_requires=requirements, + license='BSD', + zip_safe=True, + + classifiers=[ + 'Development Status :: 5 - Production/Stable', + 'Operating System :: OS Independent', + 'License :: OSI Approved :: BSD License', + 'Natural Language :: English', + + # Supported python versions + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.3', + 'Programming Language :: Python :: 3.4', + 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + ], +) diff --git a/dagan/face-alignment/test/facealignment_test.py b/dagan/face-alignment/test/facealignment_test.py new file mode 100644 index 00000000..ce26640b --- /dev/null +++ b/dagan/face-alignment/test/facealignment_test.py @@ -0,0 +1,121 @@ +import unittest +import numpy as np +import face_alignment +import sys +import torch +sys.path.append('.') +from face_alignment.utils import get_image + + +class Tester(unittest.TestCase): + def setUp(self) -> None: + self.reference_data = [np.array([[137., 240., -85.907196], + [140., 264., -81.1443], + [143., 288., -76.25633], + [146., 306., -69.01708], + [152., 327., -53.775352], + [161., 342., -30.029667], + [170., 348., -2.792292], + [185., 354., 23.522688], + [212., 360., 38.664257], + [239., 357., 31.747217], + [263., 354., 12.192401], + [284., 348., -10.0569725], + [302., 333., -29.42916], + [314., 315., -41.675602], + [320., 297., -46.924263], + [326., 276., -50.33218], + [335., 252., -53.945686], + [152., 207., -7.6189857], + [164., 201., 6.1879144], + [176., 198., 16.991247], + [188., 198., 24.690582], + [200., 201., 29.248188], + [245., 204., 37.878166], + [257., 201., 37.420483], + [269., 201., 34.163113], + [284., 204., 28.480812], + [299., 216., 18.31863], + [221., 225., 37.93351], + [218., 237., 48.337395], + [215., 249., 60.502884], + [215., 261., 63.353687], + [203., 273., 40.186855], + [209., 276., 45.057003], + [218., 276., 48.56715], + [227., 276., 47.744766], + [233., 276., 45.01401], + [170., 228., 7.166072], + [179., 222., 17.168053], + [188., 222., 19.775822], + [200., 228., 19.06176], + [191., 231., 20.636724], + [179., 231., 16.125824], + [248., 231., 28.566122], + [257., 225., 33.024036], + [269., 225., 34.384735], + [278., 231., 27.014532], + [269., 234., 32.867023], + [257., 234., 33.34033], + [185., 306., 29.927242], + [194., 297., 42.611233], + [209., 291., 50.563396], + [215., 291., 52.831104], + [221., 291., 52.9225], + [236., 300., 48.32575], + [248., 309., 38.2375], + [236., 312., 48.377922], + [224., 315., 52.63793], + [212., 315., 52.330444], + [203., 315., 49.552994], + [194., 309., 42.64459], + [188., 303., 30.746407], + [206., 300., 46.514435], + [215., 300., 49.611156], + [224., 300., 49.058918], + [248., 309., 38.084103], + [224., 303., 49.817806], + [215., 303., 49.59815], + [206., 303., 47.13894]], dtype=np.float32)] + + def test_predict_points(self): + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device='cpu') + preds = fa.get_landmarks('test/assets/aflw-test.jpg') + self.assertEqual(len(preds), len(self.reference_data)) + for pred, reference in zip(preds, self.reference_data): + self.assertTrue(np.allclose(pred, reference)) + + def test_predict_batch_points(self): + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device='cpu') + + reference_data = self.reference_data + self.reference_data + reference_data.append([]) + image = get_image('test/assets/aflw-test.jpg') + batch = np.stack([image, image, np.zeros_like(image)]) + batch = torch.Tensor(batch.transpose(0, 3, 1, 2)) + + preds = fa.get_landmarks_from_batch(batch) + + self.assertEqual(len(preds), len(reference_data)) + for pred, reference in zip(preds, reference_data): + self.assertTrue(np.allclose(pred, reference)) + + def test_predict_points_from_dir(self): + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device='cpu') + + reference_data = { + 'test/assets/grass.jpg': None, + 'test/assets/aflw-test.jpg': self.reference_data} + + preds = fa.get_landmarks_from_directory('test/assests/') + + for k, points in preds.items(): + if isinstance(points, list): + for p, p_reference in zip(points, reference_data[k]): + self.assertTrue(np.allclose(p, p_reference)) + else: + self.assertEqual(points, reference_data[k]) + + +if __name__ == '__main__': + unittest.main() diff --git a/dagan/face-alignment/test/smoke_test.py b/dagan/face-alignment/test/smoke_test.py new file mode 100644 index 00000000..93a006d0 --- /dev/null +++ b/dagan/face-alignment/test/smoke_test.py @@ -0,0 +1,2 @@ +import torch +import face_alignment diff --git a/dagan/face-alignment/test/test_utils.py b/dagan/face-alignment/test/test_utils.py new file mode 100644 index 00000000..0eb0bb5f --- /dev/null +++ b/dagan/face-alignment/test/test_utils.py @@ -0,0 +1,50 @@ +import sys +sys.path.append('.') +import unittest +from face_alignment.utils import * +import numpy as np +import torch + + +class Tester(unittest.TestCase): + def test_flip_is_label(self): + # Generate the points + heatmaps = torch.from_numpy(np.random.randint(1, high=250, size=(68, 64, 64)).astype('float32')) + + flipped_heatmaps = flip(flip(heatmaps.clone(), is_label=True), is_label=True) + + assert np.allclose(heatmaps.numpy(), flipped_heatmaps.numpy()) + + def test_flip_is_image(self): + fake_image = torch.torch.rand(3, 256, 256) + fliped_fake_image = flip(flip(fake_image.clone())) + + assert np.allclose(fake_image.numpy(), fliped_fake_image.numpy()) + + def test_getpreds(self): + pts = np.random.randint(1, high=63, size=(68, 2)).astype('float32') + + heatmaps = np.zeros((68, 256, 256)) + for i in range(68): + if pts[i, 0] > 0: + heatmaps[i] = draw_gaussian(heatmaps[i], pts[i], 2) + heatmaps = np.expand_dims(heatmaps, axis=0) + + preds, _, _ = get_preds_fromhm(heatmaps) + + assert np.allclose(pts, preds, atol=5) + + def test_create_heatmaps(self): + reference_scale = 195 + target_landmarks = torch.randint(0, 255, (1, 68, 2)).type(torch.float) # simulated dataset + bb = create_bounding_box(target_landmarks) + centers = torch.stack([bb[:, 2] - (bb[:, 2] - bb[:, 0]) / 2.0, bb[:, 3] - (bb[:, 3] - bb[:, 1]) / 2.0], dim=1) + centers[:, 1] = centers[:, 1] - (bb[:, 3] - bb[:, 1]) * 0.12 # Not sure where 0.12 comes from + scales = (bb[:, 2] - bb[:, 0] + bb[:, 3] - bb[:, 1]) / reference_scale + heatmaps = create_target_heatmap(target_landmarks, centers, scales) + preds = get_preds_fromhm(heatmaps.numpy(), centers.squeeze().numpy(), scales.squeeze().numpy())[1] + + assert np.allclose(preds, target_landmarks, atol=5) + +if __name__ == '__main__': + unittest.main() diff --git a/dagan/face-alignment/tox.ini b/dagan/face-alignment/tox.ini new file mode 100644 index 00000000..b31398c2 --- /dev/null +++ b/dagan/face-alignment/tox.ini @@ -0,0 +1,3 @@ +[flake8] +max-line-length = 120 +ignore = E305,E402,E721,F401,F403,F405,F821,F841,F999,W503 \ No newline at end of file diff --git a/dagan/frames_dataset.py b/dagan/frames_dataset.py new file mode 100644 index 00000000..fa9c4bc5 --- /dev/null +++ b/dagan/frames_dataset.py @@ -0,0 +1,200 @@ +import os +from skimage import io, img_as_float32 +from skimage.color import gray2rgb +from sklearn.model_selection import train_test_split +from imageio import mimread + +import numpy as np +from torch.utils.data import Dataset +import pandas as pd +from augmentation import AllAugmentationTransform +import glob +def read_video(name, frame_shape): + """ + Read video which can be: + - an image of concatenated frames + - '.mp4' and'.gif' + - folder with videos + """ + + if os.path.isdir(name): + frames = sorted(os.listdir(name)) + num_frames = len(frames) + video_array = np.array( + [img_as_float32(io.imread(os.path.join(name, frames[idx]))) for idx in range(num_frames)]) + elif name.lower().endswith('.png') or name.lower().endswith('.jpg'): + image = io.imread(name) + + if len(image.shape) == 2 or image.shape[2] == 1: + image = gray2rgb(image) + + if image.shape[2] == 4: + image = image[..., :3] + + image = img_as_float32(image) + + video_array = np.moveaxis(image, 1, 0) + + video_array = video_array.reshape((-1,) + frame_shape) + video_array = np.moveaxis(video_array, 1, 2) + elif name.lower().endswith('.gif') or name.lower().endswith('.mp4') or name.lower().endswith('.mov'): + video = np.array(mimread(name)) + if len(video.shape) == 3: + video = np.array([gray2rgb(frame) for frame in video]) + if video.shape[-1] == 4: + video = video[..., :3] + video_array = img_as_float32(video) + else: + raise Exception("Unknown file extensions %s" % name) + + return video_array + + +class FramesDataset(Dataset): + """ + Dataset of videos, each video can be represented as: + - an image of concatenated frames + - '.mp4' or '.gif' + - folder with all frames + """ + + def __init__(self, root_dir, frame_shape=(256, 256, 3), id_sampling=False, is_train=True, + random_seed=0, pairs_list=None, augmentation_params=None): + self.root_dir = root_dir + self.videos = os.listdir(root_dir) + # self.videos = self.videos[5000] + self.frame_shape = tuple(frame_shape) + self.pairs_list = pairs_list + self.id_sampling = id_sampling + if os.path.exists(os.path.join(root_dir, 'train')): + assert os.path.exists(os.path.join(root_dir, 'test')) + print("Use predefined train-test split.") + if id_sampling: + train_videos = {os.path.basename(video).split('#')[0] for video in + os.listdir(os.path.join(root_dir, 'train'))} + train_videos = list(train_videos) + else: + train_videos = os.listdir(os.path.join(root_dir, 'train')) + test_videos = os.listdir(os.path.join(root_dir, 'test')) + self.root_dir = os.path.join(self.root_dir, 'train' if is_train else 'test') + else: + print("Use random train-test split.") + train_videos, test_videos = train_test_split(self.videos, random_state=random_seed, test_size=0.2) + + if is_train: + self.videos = train_videos + else: + self.videos = test_videos + + self.is_train = is_train + + if self.is_train: + self.transform = AllAugmentationTransform(**augmentation_params) + else: + self.transform = None + + def __len__(self): + return len(self.videos) + + def __getitem__(self, idx): + if self.is_train and self.id_sampling: + name = self.videos[idx] + path = np.random.choice(glob.glob(os.path.join(self.root_dir, name + '*.mp4'))) + else: + name = self.videos[idx] + path = os.path.join(self.root_dir, name) + + video_name = os.path.basename(path) + + if self.is_train and os.path.isdir(path): + frames = os.listdir(path) + num_frames = len(frames) + frame_idx = np.sort(np.random.choice(num_frames, replace=True, size=2)) + # video_array = [img_as_float32(io.imread(os.path.join(path, frames[idx].decode()))) for idx in frame_idx] + video_array = [] + for idx in frame_idx: + try: + video_array.append(img_as_float32(io.imread(os.path.join(path, frames[idx].decode())))) + except Exception as e: + print(e) + else: + video_array = read_video(path, frame_shape=self.frame_shape) + num_frames = len(video_array) + frame_idx = np.sort(np.random.choice(num_frames, replace=True, size=2)) if self.is_train else range( + num_frames) + video_array = video_array[frame_idx] + + if self.transform is not None: + video_array = self.transform(video_array) + + out = {} + if self.is_train: + source = np.array(video_array[0], dtype='float32') + driving = np.array(video_array[1], dtype='float32') + + out['driving'] = driving.transpose((2, 0, 1)) + out['source'] = source.transpose((2, 0, 1)) + else: + video = np.array(video_array, dtype='float32') + out['video'] = video.transpose((3, 0, 1, 2)) + + out['name'] = video_name + + return out + + +class DatasetRepeater(Dataset): + """ + Pass several times over the same dataset for better i/o performance + """ + + def __init__(self, dataset, num_repeats=100): + self.dataset = dataset + self.num_repeats = num_repeats + + def __len__(self): + return self.num_repeats * self.dataset.__len__() + + def __getitem__(self, idx): + return self.dataset[idx % self.dataset.__len__()] + + +class PairedDataset(Dataset): + """ + Dataset of pairs for animation. + """ + + def __init__(self, initial_dataset, number_of_pairs, seed=0): + self.initial_dataset = initial_dataset + pairs_list = self.initial_dataset.pairs_list + np.random.seed(seed) + + if pairs_list is None: + max_idx = min(number_of_pairs, len(initial_dataset)) + nx, ny = max_idx, max_idx + xy = np.mgrid[:nx, :ny].reshape(2, -1).T + number_of_pairs = min(xy.shape[0], number_of_pairs) + self.pairs = xy.take(np.random.choice(xy.shape[0], number_of_pairs, replace=False), axis=0) + else: + videos = self.initial_dataset.videos + name_to_index = {name: index for index, name in enumerate(videos)} + pairs = pd.read_csv(pairs_list) + pairs = pairs[np.logical_and(pairs['source'].isin(videos), pairs['driving'].isin(videos))] + number_of_pairs = min(pairs.shape[0], number_of_pairs) + self.pairs = [] + self.start_frames = [] + for ind in range(number_of_pairs): + self.pairs.append( + (name_to_index[pairs['driving'].iloc[ind]], name_to_index[pairs['source'].iloc[ind]])) + + def __len__(self): + return len(self.pairs) + + def __getitem__(self, idx): + pair = self.pairs[idx] + first = self.initial_dataset[pair[0]] + second = self.initial_dataset[pair[1]] + first = {'driving_' + key: value for key, value in first.items()} + second = {'source_' + key: value for key, value in second.items()} + + return {**first, **second} \ No newline at end of file diff --git a/dagan/generate.py b/dagan/generate.py new file mode 100644 index 00000000..c705e9a6 --- /dev/null +++ b/dagan/generate.py @@ -0,0 +1,244 @@ +import matplotlib + +matplotlib.use('Agg') +import os +import sys +import yaml +from argparse import ArgumentParser +from tqdm import tqdm +import modules.generator as GEN +import imageio +import numpy as np +from skimage.transform import resize +from skimage import img_as_ubyte +import torch +from sync_batchnorm import DataParallelWithCallback +import depth +from modules.keypoint_detector import KPDetector +from animate import normalize_kp +from collections import OrderedDict +import time + + +if sys.version_info[0] < 3: + raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7") + + +def load_checkpoints(config_path, checkpoint_path, cpu=False): + with open(config_path) as f: + config = yaml.load(f, Loader=yaml.FullLoader) + if opt.kp_num != -1: + config['model_params']['common_params']['num_kp'] = opt.kp_num + generator = getattr(GEN, opt.generator)(**config['model_params']['generator_params'], + **config['model_params']['common_params']) + if not cpu: + generator.cuda() + config['model_params']['common_params']['num_channels'] = 4 + kp_detector = KPDetector(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + if not cpu: + kp_detector.cuda() + if cpu: + checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) + else: + checkpoint = torch.load(checkpoint_path, map_location="cuda:0") + + ckp_generator = OrderedDict((k.replace('module.', ''), v) for k, v in checkpoint['generator'].items()) + generator.load_state_dict(ckp_generator) + ckp_kp_detector = OrderedDict((k.replace('module.', ''), v) for k, v in checkpoint['kp_detector'].items()) + kp_detector.load_state_dict(ckp_kp_detector) + + if not cpu: + generator = DataParallelWithCallback(generator) + kp_detector = DataParallelWithCallback(kp_detector) + + generator.eval() + kp_detector.eval() + + return generator, kp_detector + + +def make_animation(source_image, driving_image, generator, kp_detector, depth_encoder, depth_decoder, + relative=True, adapt_movement_scale=True, cpu=False): + predictions = [] + with torch.no_grad(): + source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) + driving = torch.tensor(driving_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) + + if not cpu: + source = source.cuda() + driving = driving.cuda() + + # Depth estimation (for both source and driving) + outputs = depth_decoder(depth_encoder(source)) + depth_source = outputs[("disp", 0)] + + outputs = depth_decoder(depth_encoder(driving)) + depth_driving = outputs[("disp", 0)] + + source_kp = torch.cat((source, depth_source), 1) + driving_kp = torch.cat((driving, depth_driving), 1) + + kp_source = kp_detector(source_kp) + kp_driving_initial = kp_detector(driving_kp) + + kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving_initial, + kp_driving_initial=kp_driving_initial, use_relative_movement=relative, + use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale) + out = generator(source, kp_source=kp_source, kp_driving=kp_norm, source_depth=depth_source, + driving_depth=depth_driving) + + predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) + + return predictions + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--config", required=True, help="path to config") + parser.add_argument("--checkpoint", default='vox-cpk.pth.tar', help="path to checkpoint to restore") + parser.add_argument("--source_dir", required=True, help="directory containing source images") + parser.add_argument("--driving_dir", required=True, help="directory containing driving images") + parser.add_argument("--save_folder", default='results/', help="path to output folder") + parser.add_argument("--relative", dest="relative", action="store_true", + help="use relative or absolute keypoint coordinates") + parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true", + help="adapt movement scale based on convex hull of keypoints") + parser.add_argument("--generator", type=str, required=True) + parser.add_argument("--kp_num", type=int, required=True) + parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.") + + parser.set_defaults(relative=False) + parser.set_defaults(adapt_scale=False) + + opt = parser.parse_args() + + print("开始加载模型...") + with tqdm(total=4, desc="模型初始化", dynamic_ncols=True, leave=False) as pbar: + # 加载深度模型 + depth_encoder = depth.ResnetEncoder(18, False) + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)) + pbar.update(1) + + # 加载编码器权重 + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth', + map_location='cpu' if opt.cpu else 'cuda') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() + if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_encoder.eval() + pbar.update(1) + + # 加载解码器权重 + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth', + map_location='cpu' if opt.cpu else 'cuda') + depth_decoder.load_state_dict(loaded_dict_dec) + depth_decoder.eval() + + if not opt.cpu: + depth_encoder.cuda() + depth_decoder.cuda() + pbar.update(1) + + # 加载生成器和关键点检测器 + generator, kp_detector = load_checkpoints(config_path=opt.config, + checkpoint_path=opt.checkpoint, + cpu=opt.cpu) + pbar.update(1) + + if not os.path.exists(opt.save_folder): + os.makedirs(opt.save_folder) + print(f"创建输出目录: {opt.save_folder}") + + # 获取源目录中的文件列表 + source_files = sorted([f for f in os.listdir(opt.source_dir) + if f.endswith(('.png', '.jpg', '.jpeg'))]) + total_files = len(source_files) + + print(f"\n开始处理图像，共找到 {total_files} 个文件...") + + # 处理统计 + success_count = 0 + failed_count = 0 + start_time = time.time() + + # 显示处理配置 + print("\n处理配置：") + print(f"- 使用相对坐标: {'是' if opt.relative else '否'}") + print(f"- 自适应缩放: {'是' if opt.adapt_scale else '否'}") + print(f"- 运行模式: {'CPU' if opt.cpu else 'CUDA'}") + print(f"- 关键点数量: {opt.kp_num}") + print("-" * 50) + + # 主处理循环 + with tqdm(total=total_files, desc="处理进度", dynamic_ncols=True, leave=False) as pbar: + for idx, filename in enumerate(source_files, 1): + source_path = os.path.join(opt.source_dir, filename) + driving_path = os.path.join(opt.driving_dir, filename) + + # 显示当前处理的文件信息 + pbar.set_description(f"处理: {filename}") + + if not os.path.isfile(driving_path): + failed_count += 1 + pbar.write(f"[错误] 驱动图像不存在: {driving_path}") + pbar.update(1) + continue + + try: + process_start = time.time() + + # 读取图像 + source_image = imageio.imread(source_path) + driving_image = imageio.imread(driving_path) + + # 调整大小 + source_image = resize(source_image, (256, 256))[..., :3] + driving_image = resize(driving_image, (256, 256))[..., :3] + + # 生成动画 + predictions = make_animation(source_image, driving_image, + generator, kp_detector, + depth_encoder, depth_decoder, + relative=opt.relative, + adapt_movement_scale=opt.adapt_scale, + cpu=opt.cpu) + + # 保存生成的图像 + output_path = os.path.join(opt.save_folder, filename) + imageio.imwrite(output_path, img_as_ubyte(predictions[0])) + + success_count += 1 + process_time = time.time() - process_start + avg_time = (time.time() - start_time) / idx + eta = avg_time * (total_files - idx) + + # 更新进度条信息 + pbar.set_postfix({ + '当前耗时': f'{process_time:.1f}s' + }) + + except Exception as e: + failed_count += 1 + pbar.write(f"[错误] 处理 {filename} 失败: {str(e)}") + + pbar.update(1) + + # 最终统计 + total_time = time.time() - start_time + success_rate = (success_count / total_files) * 100 + + print("\n" + "=" * 50) + print("处理完成！最终统计：") + print("=" * 50) + print(f"总文件数: {total_files}") + print(f"成功处理: {success_count} ({success_rate:.1f}%)") + print(f"处理失败: {failed_count}") + print(f"总耗时: {total_time:.1f} 秒") + print(f"平均每张耗时: {total_time / total_files:.1f} 秒") + print(f"生成的图像已保存至: {os.path.abspath(opt.save_folder)}") + + if failed_count > 0: + print(f"\n警告：有 {failed_count} 个文件处理失败，请检查日志信息。") + else: + print("\n所有文件处理成功！") \ No newline at end of file diff --git a/dagan/generate_img.py b/dagan/generate_img.py new file mode 100644 index 00000000..cdb7489d --- /dev/null +++ b/dagan/generate_img.py @@ -0,0 +1,194 @@ +import matplotlib + +matplotlib.use('Agg') +import os +import sys +import yaml +import pandas as pd +from argparse import ArgumentParser +from tqdm import tqdm +import modules.generator as GEN +import imageio +import numpy as np +from skimage.transform import resize +from skimage import img_as_ubyte +import torch +from sync_batchnorm import DataParallelWithCallback +import depth +from modules.keypoint_detector import KPDetector +from animate import normalize_kp +from collections import OrderedDict + +if sys.version_info[0] < 3: + raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7") + + +def load_checkpoints(config_path, checkpoint_path, cpu=False): + with open(config_path) as f: + config = yaml.load(f, Loader=yaml.FullLoader) + if opt.kp_num != -1: + config['model_params']['common_params']['num_kp'] = opt.kp_num + generator = getattr(GEN, opt.generator)(**config['model_params']['generator_params'], + **config['model_params']['common_params']) + if not cpu: + generator.cuda() + config['model_params']['common_params']['num_channels'] = 4 + kp_detector = KPDetector(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + if not cpu: + kp_detector.cuda() + if cpu: + checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) + else: + checkpoint = torch.load(checkpoint_path, map_location="cuda:0") + + ckp_generator = OrderedDict((k.replace('module.', ''), v) for k, v in checkpoint['generator'].items()) + generator.load_state_dict(ckp_generator) + ckp_kp_detector = OrderedDict((k.replace('module.', ''), v) for k, v in checkpoint['kp_detector'].items()) + kp_detector.load_state_dict(ckp_kp_detector) + + if not cpu: + generator = DataParallelWithCallback(generator) + kp_detector = DataParallelWithCallback(kp_detector) + + generator.eval() + kp_detector.eval() + + return generator, kp_detector + + +def make_animation(source_image, driving_image, generator, kp_detector, depth_encoder, depth_decoder, + relative=True, adapt_movement_scale=True, cpu=False): + predictions = [] + with torch.no_grad(): + source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) + driving = torch.tensor(driving_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) + + if not cpu: + source = source.cuda() + driving = driving.cuda() + + # Depth estimation (for both source and driving) + outputs = depth_decoder(depth_encoder(source)) + depth_source = outputs[("disp", 0)] + + outputs = depth_decoder(depth_encoder(driving)) + depth_driving = outputs[("disp", 0)] + + source_kp = torch.cat((source, depth_source), 1) + driving_kp = torch.cat((driving, depth_driving), 1) + + kp_source = kp_detector(source_kp) + kp_driving_initial = kp_detector(driving_kp) + + kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving_initial, + kp_driving_initial=kp_driving_initial, use_relative_movement=relative, + use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale) + out = generator(source, kp_source=kp_source, kp_driving=kp_norm, source_depth=depth_source, + driving_depth=depth_driving) + + predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) + + return predictions + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--config", required=True, help="path to config") + parser.add_argument("--checkpoint", default='vox-cpk.pth.tar', help="path to checkpoint to restore") + parser.add_argument("--csv", required=True, help="path to CSV file containing image pairs") + parser.add_argument("--prefix", required=True, help="prefix to add to image paths") + parser.add_argument("--save_folder", default='results/', help="path to output folder") + parser.add_argument("--relative", dest="relative", action="store_true", + help="use relative or absolute keypoint coordinates") + parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true", + help="adapt movement scale based on convex hull of keypoints") + parser.add_argument("--generator", type=str, required=True) + parser.add_argument("--kp_num", type=int, required=True) + parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.") + + parser.set_defaults(relative=False) + parser.set_defaults(adapt_scale=False) + + opt = parser.parse_args() + + # 加载深度模型 + depth_encoder = depth.ResnetEncoder(18, False) + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)) + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth', map_location='cpu' if opt.cpu else 'cuda') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth', map_location='cpu' if opt.cpu else 'cuda') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + if not opt.cpu: + depth_encoder.cuda() + depth_decoder.cuda() + + # 加载生成器和关键点检测器 + generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) + + # 创建输出文件夹（如果不存在） + if not os.path.exists(opt.save_folder): + os.makedirs(opt.save_folder) + + # 读取CSV文件 + df = pd.read_csv(opt.csv) + + # 初始化新的一列用于存储生成图像的路径 + generated_images = [] + + # 遍历每一对源图像和驱动图像 + for index, row in tqdm(df.iterrows(), total=df.shape[0], desc="Processing image pairs"): + source_rel = row['source'] + driving_rel = row['driving'] + + # 添加前缀 + source_path = os.path.join(opt.prefix, source_rel) + driving_path = os.path.join(opt.prefix, driving_rel) + + # 检查文件是否存在 + if not os.path.isfile(source_path): + print(f"源图像不存在: {source_path}, 跳过此对") + generated_images.append(None) + continue + if not os.path.isfile(driving_path): + print(f"驱动图像不存在: {driving_path}, 跳过此对") + generated_images.append(None) + continue + + # 读取源图像和驱动图像 + source_image = imageio.imread(source_path) + driving_image = imageio.imread(driving_path) + + # 调整大小到256x256 + source_image = resize(source_image, (256, 256))[..., :3] + driving_image = resize(driving_image, (256, 256))[..., :3] + + try: + # 生成动画图像（这里只生成一帧） + predictions = make_animation(source_image, driving_image, generator, kp_detector, + depth_encoder, depth_decoder, + relative=opt.relative, + adapt_movement_scale=opt.adapt_scale, + cpu=opt.cpu) + + # 生成唯一的输出文件名，例如：generated_00001.png + output_filename = f"generated_{index:05d}.png" + output_path = os.path.join(opt.save_folder, output_filename) + + # 保存生成的图像 + imageio.imwrite(output_path, img_as_ubyte(predictions[0])) + generated_images.append(output_path) + except Exception as e: + print(f"处理第{index}对图像时出错: {e}") + generated_images.append(None) + + # 将生成图像的路径添加到DataFrame + df['generated_image'] = generated_images + + # 保存更新后的CSV文件 + updated_csv_path = os.path.join(opt.save_folder, 'self_reenactment_pairs_with_generated.csv') + df.to_csv(updated_csv_path, index=False) + print(f"所有生成的图像路径已保存到 {updated_csv_path}") diff --git a/dagan/generate_video.py b/dagan/generate_video.py new file mode 100644 index 00000000..fd2364f9 --- /dev/null +++ b/dagan/generate_video.py @@ -0,0 +1,203 @@ + +import pandas as pd +import numpy as np +import glob +import matplotlib +matplotlib.use('Agg') +import os +import yaml +from argparse import ArgumentParser +from tqdm import tqdm +import modules.generator as GEN +import imageio +from skimage.transform import resize +from skimage import img_as_ubyte +import torch +from sync_batchnorm import DataParallelWithCallback +import depth +from modules.keypoint_detector import KPDetector +from animate import normalize_kp +from collections import OrderedDict + + +def load_checkpoints(config_path, checkpoint_path, cpu=False): + with open(config_path) as f: + config = yaml.load(f, Loader=yaml.SafeLoader) + if opt.kp_num != -1: + config['model_params']['common_params']['num_kp'] = opt.kp_num + generator = getattr(GEN, opt.generator)(**config['model_params']['generator_params'], + **config['model_params']['common_params']) + if not cpu: + generator.cuda() + + config['model_params']['common_params']['num_channels'] = 4 + kp_detector = KPDetector(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + if not cpu: + kp_detector.cuda() + + if cpu: + checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) + else: + checkpoint = torch.load(checkpoint_path, map_location="cuda:0") + + ckp_generator = OrderedDict((k.replace('module.', ''), v) for k, v in checkpoint['generator'].items()) + generator.load_state_dict(ckp_generator) + ckp_kp_detector = OrderedDict((k.replace('module.', ''), v) for k, v in checkpoint['kp_detector'].items()) + kp_detector.load_state_dict(ckp_kp_detector) + + if not cpu: + generator = DataParallelWithCallback(generator) + kp_detector = DataParallelWithCallback(kp_detector) + + generator.eval() + kp_detector.eval() + + return generator, kp_detector + + +def load_image_sequence(folder_path): + # Get all PNG files in the folder + image_files = sorted(glob.glob(os.path.join(folder_path, '*.png'))) + if not image_files: + raise ValueError(f"No PNG files found in {folder_path}") + + # Read all images + images = [] + for img_path in image_files: + img = imageio.imread(img_path) + img = resize(img, (256, 256))[..., :3] + images.append(img) + + return images + + +def make_animation(source_image, driving_video, generator, kp_detector, relative=True, + adapt_movement_scale=True, cpu=False): + with torch.no_grad(): + predictions = [] + source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) + driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3) + if not cpu: + source = source.cuda() + driving = driving.cuda() + + outputs = depth_decoder(depth_encoder(source)) + depth_source = outputs[("disp", 0)] + + outputs = depth_decoder(depth_encoder(driving[:, :, 0])) + depth_driving = outputs[("disp", 0)] + + source_kp = torch.cat((source, depth_source), 1) + driving_kp = torch.cat((driving[:, :, 0], depth_driving), 1) + + kp_source = kp_detector(source_kp) + kp_driving_initial = kp_detector(driving_kp) + + for frame_idx in tqdm(range(driving.shape[2])): + driving_frame = driving[:, :, frame_idx] + + if not cpu: + driving_frame = driving_frame.cuda() + + outputs = depth_decoder(depth_encoder(driving_frame)) + depth_map = outputs[("disp", 0)] + + frame = torch.cat((driving_frame, depth_map), 1) + kp_driving = kp_detector(frame) + + kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving, + kp_driving_initial=kp_driving_initial, + use_relative_movement=relative, + use_relative_jacobian=relative, + adapt_movement_scale=adapt_movement_scale) + + out = generator(source, kp_source=kp_source, kp_driving=kp_norm, + source_depth=depth_source, driving_depth=depth_map) + + predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), + [0, 2, 3, 1])[0]) + + return predictions + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--config", required=True, help="path to config") + parser.add_argument("--checkpoint", default='vox-cpk.pth.tar', help="path to checkpoint") + parser.add_argument("--csv_path", required=True, help="path to input CSV file") + parser.add_argument("--output_dir", default='animate', help="output directory") + parser.add_argument("--relative", dest="relative", action="store_true") + parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true") + parser.add_argument("--generator", type=str, required=True) + parser.add_argument("--kp_num", type=int, required=True) + parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode") + + parser.set_defaults(relative=False) + parser.set_defaults(adapt_scale=False) + + opt = parser.parse_args() + + # Create output directory if it doesn't exist + os.makedirs(opt.output_dir, exist_ok=True) + + # Initialize depth models + depth_encoder = depth.ResnetEncoder(18, False) + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)) + + # Load depth model weights + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() + if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + + depth_encoder.eval() + depth_decoder.eval() + + if not opt.cpu: + depth_encoder.cuda() + depth_decoder.cuda() + + # Load generator and keypoint detector + generator, kp_detector = load_checkpoints(config_path=opt.config, + checkpoint_path=opt.checkpoint, + cpu=opt.cpu) + + # Read CSV file + df = pd.read_csv(opt.csv_path) + + # Process each row in the CSV + for idx, row in tqdm(df.iterrows(), total=len(df), desc="Processing pairs"): + try: + # Generate output filename + source_id = os.path.basename(os.path.dirname(row['source_frame'])) + driving_id = os.path.basename(row['driving_video']) + output_filename = f"{source_id}_to_{driving_id}.mp4" + output_path = os.path.join(opt.output_dir, output_filename) + + # Skip if output already exists + if os.path.exists(output_path): + print(f"Skipping existing file: {output_path}") + continue + + # Load source image + source_image = imageio.imread(row['source_frame']) + source_image = resize(source_image, (256, 256))[..., :3] + + # Load driving video frames + driving_frames = load_image_sequence(row['driving_video']) + + # Generate animation + predictions = make_animation(source_image, driving_frames, generator, kp_detector, + relative=opt.relative, adapt_movement_scale=opt.adapt_scale, + cpu=opt.cpu) + + # Save video + imageio.mimsave(output_path, [img_as_ubyte(p) for p in predictions], fps=25) + print(f"Saved animation to: {output_path}") + + except Exception as e: + print(f"Error processing pair {idx}: {str(e)}") + continue \ No newline at end of file diff --git a/dagan/generated_img/evaluation_results_cross_299.txt b/dagan/generated_img/evaluation_results_cross_299.txt new file mode 100644 index 00000000..a980099a --- /dev/null +++ b/dagan/generated_img/evaluation_results_cross_299.txt @@ -0,0 +1,8 @@ +niqe: 2.6827 +psnr: 15.3882 +ssim: 0.6554 +fid: 0.0067 +lse_c: 0.3903 +lse_d: 0.4044 + +PRMSE: 2.961677881449758, AUCON : 0.7558749369638144, CSIM: 0.757752229254314 \ No newline at end of file diff --git a/dagan/generated_img/evaluation_results_cross_364.txt b/dagan/generated_img/evaluation_results_cross_364.txt new file mode 100644 index 00000000..fc4644e9 --- /dev/null +++ b/dagan/generated_img/evaluation_results_cross_364.txt @@ -0,0 +1,11 @@ +niqe: 2.9154 +psnr: 15.4496 +ssim: 0.6546 +fid: 0.0064 +lse_c: 0.3900 +lse_d: 0.3976 +aucon: 0.4923 +csim: 0.5561 +prmse: 0.1936 + + PRMSE: 12.562801158620402, AUCON : 0.6398890569840439, CSIM: 0.32219273304259594 diff --git a/dagan/generated_img/evaluation_results_cross_form.txt b/dagan/generated_img/evaluation_results_cross_form.txt new file mode 100644 index 00000000..67967555 --- /dev/null +++ b/dagan/generated_img/evaluation_results_cross_form.txt @@ -0,0 +1,9 @@ +niqe: 2.7291 +psnr: 15.3151 +ssim: 0.6526 +fid: 0.0067 +lse_c: 0.3846 +lse_d: 0.4047 + +100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2001/2001 [58:16<00:00, 1.75s/it] + PRMSE: 2.6556226359385744, AUCON : 0.7693450881608215, CSIM: 0.7508928756710113 diff --git a/dagan/generated_img/evaluation_results_self_299.txt b/dagan/generated_img/evaluation_results_self_299.txt new file mode 100644 index 00000000..46c98c03 --- /dev/null +++ b/dagan/generated_img/evaluation_results_self_299.txt @@ -0,0 +1,8 @@ +niqe: 4.5944 +psnr: 24.4211 +ssim: 0.8684 +fid: 0.0006 +lse_c: 0.2509 +lse_d: 0.1080 + + PRMSE: 1.601216509155132, AUCON : 0.8347432024164978, CSIM: 0.8902153855259736 diff --git a/dagan/generated_img/evaluation_results_self_364.txt b/dagan/generated_img/evaluation_results_self_364.txt new file mode 100644 index 00000000..8b33a087 --- /dev/null +++ b/dagan/generated_img/evaluation_results_self_364.txt @@ -0,0 +1,8 @@ +niqe: 4.8714 +psnr: 24.7391 +ssim: 0.8722 +fid: 0.0004 +lse_c: 0.2489 +lse_d: 0.1020 + + PRMSE: 1.601216509155132, AUCON : 0.8347432024164978, CSIM: 0.8902153855259736 diff --git a/dagan/generated_img/evaluation_results_self_form.txt b/dagan/generated_img/evaluation_results_self_form.txt new file mode 100644 index 00000000..6c873001 --- /dev/null +++ b/dagan/generated_img/evaluation_results_self_form.txt @@ -0,0 +1,8 @@ +niqe: 4.7003 +psnr: 24.7610 +ssim: 0.8738 +fid: 0.0005 +lse_c: 0.2412 +lse_d: 0.1010 + + PRMSE: 1.3962652412197722, AUCON : 0.8390428211582677, CSIM: 0.8948365081693228 \ No newline at end of file diff --git a/dagan/input_audio_text/driving.mp4 b/dagan/input_audio_text/driving.mp4 new file mode 100644 index 00000000..8f9ce09a Binary files /dev/null and b/dagan/input_audio_text/driving.mp4 differ diff --git a/dagan/input_image/src.png b/dagan/input_image/src.png new file mode 100644 index 00000000..5b875f7f Binary files /dev/null and b/dagan/input_image/src.png differ diff --git a/dagan/kill_port.py b/dagan/kill_port.py new file mode 100644 index 00000000..5d05ca9c --- /dev/null +++ b/dagan/kill_port.py @@ -0,0 +1,28 @@ +import os +import sys +import signal +import pdb +def kill_process(*pids): + for pid in pids: + a = os.kill(pid, signal.SIGKILL) + print('已杀死pid为%s的进程,　返回值是:%s' % (pid, a)) + +def get_pid(*ports): + #其中\"为转义" + pids = [] + print(ports) + for port in ports: + msg = os.popen('lsof -i:{}'.format(port)).read() + msg = msg.split('\n')[1:-1] + for m in msg: + m = m.replace(' ', ' ') + m = m.replace(' ', ' ') + tokens = m.split(' ') + pids.append(int(tokens[1])) + return pids + +if __name__ == "__main__": + # 杀死占用端口号的ps进程 + ports = sys.argv[1:] + kill_process(*get_pid(*ports)) + diff --git a/dagan/logger.py b/dagan/logger.py new file mode 100644 index 00000000..c430f673 --- /dev/null +++ b/dagan/logger.py @@ -0,0 +1,217 @@ +import numpy as np +import torch +import torch.nn.functional as F +import imageio + +import os +from skimage.draw import ellipse +import pdb +import matplotlib.pyplot as plt +import collections + +class Logger: + def __init__(self, log_dir, checkpoint_freq=100, visualizer_params=None, zfill_num=8, log_file_name='log.txt'): + + self.loss_list = [] + self.cpk_dir = log_dir + self.visualizations_dir = os.path.join(log_dir, 'train-vis') + if not os.path.exists(self.visualizations_dir): + os.makedirs(self.visualizations_dir) + self.log_file = open(os.path.join(log_dir, log_file_name), 'a') + self.zfill_num = zfill_num + self.visualizer = Visualizer(**visualizer_params) + self.checkpoint_freq = checkpoint_freq + self.epoch = 0 + self.best_loss = float('inf') + self.names = None + + def log_scores(self, loss_names): + loss_mean = np.array(self.loss_list).mean(axis=0) + + loss_string = "; ".join(["%s - %.5f" % (name, value) for name, value in zip(loss_names, loss_mean)]) + loss_string = str(self.epoch).zfill(self.zfill_num) + ") " + loss_string + + print(loss_string, file=self.log_file) + self.loss_list = [] + self.log_file.flush() + + def visualize_rec(self, inp, out): + image = self.visualizer.visualize(inp['driving'], inp['source_self'], out) + imageio.imsave(os.path.join(self.visualizations_dir, "%s-rec.png" % str(self.epoch).zfill(self.zfill_num)), image) + + def save_cpk(self, emergent=False): + cpk = {k: v.state_dict() for k, v in self.models.items()} + cpk['epoch'] = self.epoch + cpk_path = os.path.join(self.cpk_dir, '%s-checkpoint.pth.tar' % str(self.epoch).zfill(self.zfill_num)) + if not (os.path.exists(cpk_path) and emergent): + torch.save(cpk, cpk_path) + + @staticmethod + def load_cpk(checkpoint_path, generator=None, discriminator=None, kp_detector=None, + optimizer_generator=None, optimizer_discriminator=None, optimizer_kp_detector=None): + num_gpu = torch.cuda.device_count() + if num_gpu == 1: + checkpoint = torch.load(checkpoint_path,map_location='cuda:0') + else: + checkpoint = torch.load(checkpoint_path,map_location='cpu') + if generator is not None: + ckp_generator = collections.OrderedDict((k.replace('.module.','.'),v) for k,v in checkpoint['generator'].items()) + generator.load_state_dict(ckp_generator) + if kp_detector is not None: + ckp_kp_detector = collections.OrderedDict((k.replace('.module.','.'),v) for k,v in checkpoint['kp_detector'].items()) + kp_detector.load_state_dict(ckp_kp_detector) + if discriminator is not None: + try: + ckp_discriminator = collections.OrderedDict((k.replace('.module.','.'),v) for k,v in checkpoint['discriminator'].items()) + discriminator.load_state_dict(ckp_discriminator) + except: + print ('No discriminator in the state-dict. Dicriminator will be randomly initialized') + if optimizer_generator is not None: + ckp_optimizer_generator = collections.OrderedDict((k.replace('.module.','.'),v) for k,v in checkpoint['optimizer_generator'].items()) + optimizer_generator.load_state_dict(ckp_optimizer_generator) + if optimizer_discriminator is not None: + try: + ckp_optimizer_discriminator = collections.OrderedDict((k.replace('.module.','.'),v) for k,v in checkpoint['optimizer_discriminator'].items()) + optimizer_discriminator.load_state_dict(ckp_optimizer_discriminator) + except RuntimeError as e: + print ('No discriminator optimizer in the state-dict. Optimizer will be not initialized') + if optimizer_kp_detector is not None: + ckp_optimizer_kp_detector = collections.OrderedDict((k.replace('.module.','.'),v) for k,v in checkpoint['optimizer_kp_detector'].items()) + optimizer_kp_detector.load_state_dict(ckp_optimizer_kp_detector) + + return checkpoint['epoch'] + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + if 'models' in self.__dict__: + self.save_cpk() + self.log_file.close() + + def log_iter(self, losses): + losses = collections.OrderedDict(losses.items()) + if self.names is None: + self.names = list(losses.keys()) + self.loss_list.append(list(losses.values())) + + def log_epoch(self, epoch, models, inp, out): + self.epoch = epoch + self.models = models + if (self.epoch + 1) % self.checkpoint_freq == 0: + self.save_cpk() + self.log_scores(self.names) + # self.visualize_rec(inp, out) + + +class Visualizer: + def __init__(self, kp_size=5, draw_border=False, colormap='gist_rainbow'): + self.kp_size = kp_size + self.draw_border = draw_border + self.colormap = plt.get_cmap(colormap) + + def draw_image_with_kp(self, image, kp_array): + image = np.copy(image) + spatial_size = np.array(image.shape[:2][::-1])[np.newaxis] + kp_array = spatial_size * (kp_array + 1) / 2 + num_kp = kp_array.shape[0] + for kp_ind, kp in enumerate(kp_array): + rr, cc = ellipse(kp[1], kp[0], self.kp_size,self.kp_size, shape=image.shape[:2]) + image[rr, cc] = np.array(self.colormap(kp_ind / num_kp))[:3] + return image + + def create_image_column_with_kp(self, images, kp): + image_array = np.array([self.draw_image_with_kp(v, k) for v, k in zip(images, kp)]) + return self.create_image_column(image_array) + + def create_image_column(self, images): + if self.draw_border: + images = np.copy(images) + images[:, :, [0, -1]] = (1, 1, 1) + images[:, :, [0, -1]] = (1, 1, 1) + return np.concatenate(list(images), axis=0) + + def create_image_grid(self, *args): + out = [] + for arg in args: + if type(arg) == tuple: + out.append(self.create_image_column_with_kp(arg[0], arg[1])) + else: + out.append(self.create_image_column(arg)) + return np.concatenate(out, axis=1) + + def visualize(self, driving, source, out): + images = [] + + # Source image with keypoints + source = source.data.cpu() + kp_source = out['kp_source']['value'].data.cpu().numpy() + source = np.transpose(source, [0, 2, 3, 1]) + images.append((source, kp_source)) + + # Equivariance visualization + if 'transformed_frame' in out: + transformed = out['transformed_frame'].data.cpu().numpy() + transformed = np.transpose(transformed, [0, 2, 3, 1]) + transformed_kp = out['transformed_kp']['value'].data.cpu().numpy() + images.append((transformed, transformed_kp)) + + # Driving image with keypoints + kp_driving = out['kp_driving']['value'].data.cpu().numpy() + driving = driving.data.cpu().numpy() + driving = np.transpose(driving, [0, 2, 3, 1]) + images.append((driving, kp_driving)) + + # Deformed image + if 'deformed' in out: + deformed = out['deformed'].data.cpu().numpy() + deformed = np.transpose(deformed, [0, 2, 3, 1]) + images.append(deformed) + + # Result with and without keypoints + prediction = out['prediction'].data.cpu().numpy() + prediction = np.transpose(prediction, [0, 2, 3, 1]) + if 'kp_norm' in out: + kp_norm = out['kp_norm']['value'].data.cpu().numpy() + images.append((prediction, kp_norm)) + images.append(prediction) + + + ## Occlusion map + if 'occlusion_map' in out: + occlusion_map = out['occlusion_map'].data.cpu().repeat(1, 3, 1, 1) + occlusion_map = F.interpolate(occlusion_map, size=source.shape[1:3]).numpy() + occlusion_map = np.transpose(occlusion_map, [0, 2, 3, 1]) + images.append(occlusion_map) + + # Deformed images according to each individual transform + if 'sparse_deformed' in out: + full_mask = [] + for i in range(out['sparse_deformed'].shape[1]): + image = out['sparse_deformed'][:, i].data.cpu() + image = F.interpolate(image, size=source.shape[1:3]) + mask = out['mask'][:, i:(i+1)].data.cpu().repeat(1, 3, 1, 1) + mask = F.interpolate(mask, size=source.shape[1:3]) + image = np.transpose(image.numpy(), (0, 2, 3, 1)) + mask = np.transpose(mask.numpy(), (0, 2, 3, 1)) + + if i != 0: + color = np.array(self.colormap((i - 1) / (out['sparse_deformed'].shape[1] - 1)))[:3] + else: + color = np.array((0, 0, 0)) + + color = color.reshape((1, 1, 1, 3)) + + images.append(image) + if i != 0: + images.append(mask * color) + else: + images.append(mask) + + full_mask.append(mask * color) + + images.append(sum(full_mask)) + + image = self.create_image_grid(*images) + image = (255 * image).astype(np.uint8) + return image diff --git a/dagan/modules/AdaIN.py b/dagan/modules/AdaIN.py new file mode 100644 index 00000000..dac8be74 --- /dev/null +++ b/dagan/modules/AdaIN.py @@ -0,0 +1,61 @@ +import torch + +def calc_mean_std(feat, eps=1e-5): + # eps is a small value added to the variance to avoid divide-by-zero. + size = feat.size() + assert (len(size) == 4) + N, C = size[:2] + feat_var = feat.view(N, C, -1).var(dim=2) + eps + feat_std = feat_var.sqrt().view(N, C, 1, 1) + feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1) + return feat_mean, feat_std + +def adaptive_instance_normalization(content_feat, style_feat): + assert (content_feat.size()[:2] == style_feat.size()[:2]) + size = content_feat.size() + style_mean, style_std = calc_mean_std(style_feat) + content_mean, content_std = calc_mean_std(content_feat) + normalized_feat = (content_feat - content_mean.expand( + size)) / content_std.expand(size) + + return normalized_feat * style_std.expand(size) + style_mean.expand(size) + +def _calc_feat_flatten_mean_std(feat): + # takes 3D feat (C, H, W), return mean and std of array within channels + assert (feat.size()[0] == 3) + assert (isinstance(feat, torch.FloatTensor)) + feat_flatten = feat.view(3, -1) + mean = feat_flatten.mean(dim=-1, keepdim=True) + std = feat_flatten.std(dim=-1, keepdim=True) + return feat_flatten, mean, std + +def _mat_sqrt(x): + U, D, V = torch.svd(x) + return torch.mm(torch.mm(U, D.pow(0.5).diag()), V.t()) + +def coral(source, target): + # assume both source and target are 3D array (C, H, W) + # Note: flatten -> f + source_f, source_f_mean, source_f_std = _calc_feat_flatten_mean_std(source) + source_f_norm = (source_f - source_f_mean.expand_as( + source_f)) / source_f_std.expand_as(source_f) + source_f_cov_eye = \ + torch.mm(source_f_norm, source_f_norm.t()) + torch.eye(3) + + target_f, target_f_mean, target_f_std = _calc_feat_flatten_mean_std(target) + target_f_norm = (target_f - target_f_mean.expand_as( + target_f)) / target_f_std.expand_as(target_f) + target_f_cov_eye = \ + torch.mm(target_f_norm, target_f_norm.t()) + torch.eye(3) + + source_f_norm_transfer = torch.mm( + _mat_sqrt(target_f_cov_eye), + torch.mm(torch.inverse(_mat_sqrt(source_f_cov_eye)), + source_f_norm) + ) + + source_f_transfer = source_f_norm_transfer * \ + target_f_std.expand_as(source_f_norm) + \ + target_f_mean.expand_as(source_f_norm) + + return source_f_transfer.view(source.size()) \ No newline at end of file diff --git a/dagan/modules/dense_motion.py b/dagan/modules/dense_motion.py new file mode 100644 index 00000000..0a427423 --- /dev/null +++ b/dagan/modules/dense_motion.py @@ -0,0 +1,112 @@ +from torch import nn +import torch.nn.functional as F +import torch +from modules.util import Hourglass, AntiAliasInterpolation2d, make_coordinate_grid, kp2gaussian +import pdb +from modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d + + +class DenseMotionNetwork(nn.Module): + """ + Module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving + """ + + def __init__(self, block_expansion, num_blocks, max_features, num_kp, num_channels, estimate_occlusion_map=False, + scale_factor=1, kp_variance=0.01): + super(DenseMotionNetwork, self).__init__() + self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp + 1) * (num_channels + 1), + max_features=max_features, num_blocks=num_blocks) + + self.mask = nn.Conv2d(self.hourglass.out_filters, num_kp + 1, kernel_size=(7, 7), padding=(3, 3)) + + if estimate_occlusion_map: + self.occlusion = nn.Conv2d(self.hourglass.out_filters, 1, kernel_size=(7, 7), padding=(3, 3)) + else: + self.occlusion = None + + self.num_kp = num_kp + self.scale_factor = scale_factor + self.kp_variance = kp_variance + + if self.scale_factor != 1: + self.down = AntiAliasInterpolation2d(num_channels, self.scale_factor) + + def create_heatmap_representations(self, source_image, kp_driving, kp_source): + """ + Eq 6. in the paper H_k(z) + """ + spatial_size = source_image.shape[2:] + gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=self.kp_variance) + gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=self.kp_variance) + heatmap = gaussian_driving - gaussian_source + #adding background feature + zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1]).type(heatmap.type()) + heatmap = torch.cat([zeros, heatmap], dim=1) + heatmap = heatmap.unsqueeze(2) + return heatmap + + def create_sparse_motions(self, source_image, kp_driving, kp_source): + """ + Eq 4. in the paper T_{s<-d}(z) + """ + bs, _, h, w = source_image.shape + identity_grid = make_coordinate_grid((h, w), type=kp_source['value'].type()) + identity_grid = identity_grid.view(1, 1, h, w, 2) + coordinate_grid = identity_grid - kp_driving['value'].view(bs, self.num_kp, 1, 1, 2) + if 'jacobian' in kp_driving: + jacobian = torch.matmul(kp_source['jacobian'], torch.inverse(kp_driving['jacobian'])) + jacobian = jacobian.unsqueeze(-3).unsqueeze(-3) + jacobian = jacobian.repeat(1, 1, h, w, 1, 1) + coordinate_grid = torch.matmul(jacobian, coordinate_grid.unsqueeze(-1)) + coordinate_grid = coordinate_grid.squeeze(-1) + + driving_to_source = coordinate_grid + kp_source['value'].view(bs, self.num_kp, 1, 1, 2) + + #adding background feature + identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1) + sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1) #bs, num_kp+1,w,h,2 + return sparse_motions + + def create_deformed_source_image(self, source_image, sparse_motions): + """ + Eq 7. in the paper \hat{T}_{s<-d}(z) + """ + bs, _, h, w = source_image.shape + source_repeat = source_image.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp + 1, 1, 1, 1, 1) + source_repeat = source_repeat.view(bs * (self.num_kp + 1), -1, h, w) + sparse_motions = sparse_motions.view((bs * (self.num_kp + 1), h, w, -1)) + sparse_deformed = F.grid_sample(source_repeat, sparse_motions) + sparse_deformed = sparse_deformed.view((bs, self.num_kp + 1, -1, h, w)) + return sparse_deformed + + def forward(self, source_image, kp_driving, kp_source): + if self.scale_factor != 1: + source_image = self.down(source_image) + bs, _, h, w = source_image.shape + out_dict = dict() + heatmap_representation = self.create_heatmap_representations(source_image, kp_driving, kp_source) + sparse_motion = self.create_sparse_motions(source_image, kp_driving, kp_source) + deformed_source = self.create_deformed_source_image(source_image, sparse_motion) + out_dict['sparse_deformed'] = deformed_source + + input = torch.cat([heatmap_representation, deformed_source], dim=2) + input = input.view(bs, -1, h, w) + + prediction = self.hourglass(input) + + mask = self.mask(prediction) + mask = F.softmax(mask, dim=1) + out_dict['mask'] = mask + mask = mask.unsqueeze(2) + sparse_motion = sparse_motion.permute(0, 1, 4, 2, 3) + deformation = (sparse_motion * mask).sum(dim=1) + deformation = deformation.permute(0, 2, 3, 1) + + out_dict['deformation'] = deformation + + # Sec. 3.2 in the paper + if self.occlusion: + occlusion_map = torch.sigmoid(self.occlusion(prediction)) + out_dict['occlusion_map'] = occlusion_map + + return out_dict diff --git a/dagan/modules/discriminator.py b/dagan/modules/discriminator.py new file mode 100644 index 00000000..2f9fdfdb --- /dev/null +++ b/dagan/modules/discriminator.py @@ -0,0 +1,95 @@ +from torch import nn +import torch.nn.functional as F +from modules.util import kp2gaussian +import torch +import pdb + +class DownBlock2d(nn.Module): + """ + Simple block for processing video (encoder). + """ + + def __init__(self, in_features, out_features, norm=False, kernel_size=4, pool=False, sn=False): + super(DownBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size) + + if sn: + self.conv = nn.utils.spectral_norm(self.conv) + + if norm: + self.norm = nn.InstanceNorm2d(out_features, affine=True) + else: + self.norm = None + self.pool = pool + + def forward(self, x): + out = x + out = self.conv(out) + if self.norm: + out = self.norm(out) + out = F.leaky_relu(out, 0.2) + if self.pool: + out = F.avg_pool2d(out, (2, 2)) + return out + + +class Discriminator(nn.Module): + """ + Discriminator similar to Pix2Pix + """ + + def __init__(self, num_channels=3, block_expansion=64, num_blocks=4, max_features=512, + sn=False, use_kp=False, num_kp=10, kp_variance=0.01, **kwargs): + super(Discriminator, self).__init__() + + down_blocks = [] + for i in range(num_blocks): + down_blocks.append( + DownBlock2d(num_channels + num_kp * use_kp if i == 0 else min(max_features, block_expansion * (2 ** i)), + min(max_features, block_expansion * (2 ** (i + 1))), + norm=(i != 0), kernel_size=4, pool=(i != num_blocks - 1), sn=sn)) + self.down_blocks = nn.ModuleList(down_blocks) + self.conv = nn.Conv2d(self.down_blocks[-1].conv.out_channels, out_channels=1, kernel_size=1) + if sn: + self.conv = nn.utils.spectral_norm(self.conv) + self.use_kp = use_kp + self.kp_variance = kp_variance + + def forward(self, x, kp=None): + feature_maps = [] + out = x + if self.use_kp: + heatmap = kp2gaussian(kp, x.shape[2:], self.kp_variance) + out = torch.cat([out, heatmap], dim=1) + # print(out.shape) + for down_block in self.down_blocks: + feature_maps.append(down_block(out)) + out = feature_maps[-1] + # print(out.shape) + prediction_map = self.conv(out) + + return feature_maps, prediction_map + + +class MultiScaleDiscriminator(nn.Module): + """ + Multi-scale (scale) discriminator + """ + + def __init__(self, scales=(), **kwargs): + super(MultiScaleDiscriminator, self).__init__() + self.scales = scales + discs = {} + for scale in scales: + discs[str(scale).replace('.', '-')] = Discriminator(**kwargs) + self.discs = nn.ModuleDict(discs) + + def forward(self, x, kp=None): + out_dict = {} + for scale, disc in self.discs.items(): + scale = str(scale).replace('-', '.') + key = 'prediction_' + scale + feature_maps, prediction_map = disc(x[key], kp) + out_dict['feature_maps_' + scale] = feature_maps + out_dict['prediction_map_' + scale] = prediction_map + return out_dict diff --git a/dagan/modules/dynamic_conv.py b/dagan/modules/dynamic_conv.py new file mode 100644 index 00000000..29c1032b --- /dev/null +++ b/dagan/modules/dynamic_conv.py @@ -0,0 +1,382 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import pdb + +class attention1d(nn.Module): + def __init__(self, in_planes, ratios, K, temperature, init_weight=True): + super(attention1d, self).__init__() + assert temperature%3==1 + self.avgpool = nn.AdaptiveAvgPool1d(1) + if in_planes!=3: + hidden_planes = int(in_planes*ratios)+1 + else: + hidden_planes = K + self.fc1 = nn.Conv1d(in_planes, hidden_planes, 1, bias=False) + # self.bn = nn.BatchNorm2d(hidden_planes) + self.fc2 = nn.Conv1d(hidden_planes, K, 1, bias=True) + self.temperature = temperature + if init_weight: + self._initialize_weights() + + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv1d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + if isinstance(m ,nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def updata_temperature(self): + if self.temperature!=1: + self.temperature -=3 + print('Change temperature to:', str(self.temperature)) + + + def forward(self, x): + x = self.avgpool(x) + x = self.fc1(x) + x = F.relu(x) + x = self.fc2(x).view(x.size(0), -1) + return F.softmax(x/self.temperature, 1) + + +class Dynamic_conv1d(nn.Module): + def __init__(self, in_planes, out_planes, kernel_size, ratio=0.25, stride=1, padding=0, dilation=1, groups=1, bias=True, K=4,temperature=34, init_weight=True): + super(Dynamic_conv1d, self).__init__() + assert in_planes%groups==0 + self.in_planes = in_planes + self.out_planes = out_planes + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.bias = bias + self.K = K + self.attention = attention1d(in_planes, ratio, K, temperature) + + self.weight = nn.Parameter(torch.randn(K, out_planes, in_planes//groups, kernel_size), requires_grad=True) + if bias: + self.bias = nn.Parameter(torch.Tensor(K, out_planes)) + else: + self.bias = None + if init_weight: + self._initialize_weights() + + #TODO 初始化 + def _initialize_weights(self): + for i in range(self.K): + nn.init.kaiming_uniform_(self.weight[i]) + + + def update_temperature(self): + self.attention.updata_temperature() + + def forward(self, x):#将batch视作维度变量，进行组卷积，因为组卷积的权重是不同的，动态卷积的权重也是不同的 + softmax_attention = self.attention(x) + batch_size, in_planes, height = x.size() + x = x.view(1, -1, height, )# 变化成一个维度进行组卷积 + weight = self.weight.view(self.K, -1) + + # 动态卷积的权重的生成， 生成的是batch_size个卷积参数（每个参数不同） + aggregate_weight = torch.mm(softmax_attention, weight).view(-1, self.in_planes, self.kernel_size,) + if self.bias is not None: + aggregate_bias = torch.mm(softmax_attention, self.bias).view(-1) + output = F.conv1d(x, weight=aggregate_weight, bias=aggregate_bias, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups*batch_size) + else: + output = F.conv1d(x, weight=aggregate_weight, bias=None, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups * batch_size) + + output = output.view(batch_size, self.out_planes, output.size(-1)) + return output + + + +class attention2d(nn.Module): + def __init__(self, in_planes, ratios, K, temperature, init_weight=True): + super(attention2d, self).__init__() + assert temperature%3==1 + self.avgpool = nn.AdaptiveAvgPool2d(1) + if in_planes!=3: + hidden_planes = int(in_planes*ratios)+1 + else: + hidden_planes = K + self.fc1 = nn.Conv2d(in_planes, hidden_planes, 1, bias=False) + # self.bn = nn.BatchNorm2d(hidden_planes) + self.fc2 = nn.Conv2d(hidden_planes, K, 1, bias=True) + self.temperature = temperature + if init_weight: + self._initialize_weights() + + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + if isinstance(m ,nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def updata_temperature(self): + if self.temperature!=1: + self.temperature -=3 + print('Change temperature to:', str(self.temperature)) + + + def forward(self, x): + x = self.avgpool(x) + x = self.fc1(x) + x = F.relu(x) + x = self.fc2(x).view(x.size(0), -1) + return F.softmax(x/self.temperature, 1) + + +class Dynamic_deepwise_conv2d(nn.Module): + def __init__(self, in_planes, out_planes, kernel_size, ratio=0.25, stride=1, padding=0, dilation=1, groups=1, bias=True, K=4,temperature=34, init_weight=True): + super(Dynamic_deepwise_conv2d, self).__init__() + assert in_planes%groups==0 + self.in_planes = in_planes + self.out_planes = out_planes + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.bias = bias + self.K = K + self.attention = attention2d(in_planes, ratio, K, temperature) + + self.weight = nn.Parameter(torch.randn(K, out_planes, in_planes//groups, kernel_size, kernel_size), requires_grad=True) + if bias: + self.bias = nn.Parameter(torch.Tensor(K, out_planes)) + else: + self.bias = None + if init_weight: + self._initialize_weights() + + #TODO 初始化 + def _initialize_weights(self): + for i in range(self.K): + nn.init.kaiming_uniform_(self.weight[i]) + + + def update_temperature(self): + self.attention.updata_temperature() + + def forward(self, x, y):#将batch视作维度变量，进行组卷积，因为组卷积的权重是不同的，动态卷积的权重也是不同的 + softmax_attention = self.attention(x) + batch_size, in_planes, height, width = x.size() + y = y.view(1, -1, height, width)# 变化成一个维度进行组卷积 + weight = self.weight.view(self.K, -1) + + # 动态卷积的权重的生成， 生成的是batch_size个卷积参数（每个参数不同） + aggregate_weight = torch.mm(softmax_attention, weight).view(-1, 1, self.kernel_size, self.kernel_size) + if self.bias is not None: + aggregate_bias = torch.mm(softmax_attention, self.bias).view(-1) + output = F.conv2d(y, weight=aggregate_weight, bias=aggregate_bias, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups*batch_size) + else: + output = F.conv2d(y, weight=aggregate_weight, bias=None, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups * batch_size) + + output = output.view(batch_size, self.out_planes, output.size(-2), output.size(-1)) + return output + +class Dynamic_conv2d(nn.Module): + def __init__(self, in_planes, out_planes, kernel_size, ratio=0.25, stride=1, padding=0, dilation=1, groups=1, bias=True, K=4,temperature=34, init_weight=True): + super(Dynamic_conv2d, self).__init__() + assert in_planes%groups==0 + self.in_planes = in_planes + self.out_planes = out_planes + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.bias = bias + self.K = K + self.attention = attention2d(in_planes, ratio, K, temperature) + + self.weight = nn.Parameter(torch.randn(K, out_planes, in_planes//groups, kernel_size, kernel_size), requires_grad=True) + if bias: + self.bias = nn.Parameter(torch.Tensor(K, out_planes)) + else: + self.bias = None + if init_weight: + self._initialize_weights() + + #TODO 初始化 + def _initialize_weights(self): + for i in range(self.K): + nn.init.kaiming_uniform_(self.weight[i]) + + + def update_temperature(self): + self.attention.updata_temperature() + + def forward(self, x,y):#将batch视作维度变量，进行组卷积，因为组卷积的权重是不同的，动态卷积的权重也是不同的 + softmax_attention = self.attention(x) + batch_size, in_planes, height, width = x.size() + y = y.view(1, -1, height, width)# 变化成一个维度进行组卷积 + weight = self.weight.view(self.K, -1) + + # 动态卷积的权重的生成， 生成的是batch_size个卷积参数（每个参数不同） + aggregate_weight = torch.mm(softmax_attention, weight).view(-1, self.in_planes, self.kernel_size, self.kernel_size) + if self.bias is not None: + aggregate_bias = torch.mm(softmax_attention, self.bias).view(-1) + output = F.conv2d(y, weight=aggregate_weight, bias=aggregate_bias, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups*batch_size) + else: + output = F.conv2d(y, weight=aggregate_weight, bias=None, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups * batch_size) + + output = output.view(batch_size, self.out_planes, output.size(-2), output.size(-1)) + return output + + +class attention3d(nn.Module): + def __init__(self, in_planes, ratios, K, temperature): + super(attention3d, self).__init__() + assert temperature%3==1 + self.avgpool = nn.AdaptiveAvgPool3d(1) + if in_planes != 3: + hidden_planes = int(in_planes * ratios)+1 + else: + hidden_planes = K + self.fc1 = nn.Conv3d(in_planes, hidden_planes, 1, bias=False) + self.fc2 = nn.Conv3d(hidden_planes, K, 1, bias=False) + self.temperature = temperature + + def updata_temperature(self): + if self.temperature!=1: + self.temperature -=3 + print('Change temperature to:', str(self.temperature)) + + def forward(self, x): + x = self.avgpool(x) + x = self.fc1(x) + x = F.relu(x) + x = self.fc2(x).view(x.size(0), -1) + return F.softmax(x / self.temperature, 1) + +class Dynamic_conv3d(nn.Module): + def __init__(self, in_planes, out_planes, kernel_size, ratio=0.25, stride=1, padding=0, dilation=1, groups=1, bias=True, K=4, temperature=34): + super(Dynamic_conv3d, self).__init__() + assert in_planes%groups==0 + self.in_planes = in_planes + self.out_planes = out_planes + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.bias = bias + self.K = K + self.attention = attention3d(in_planes, ratio, K, temperature) + + self.weight = nn.Parameter(torch.randn(K, out_planes, in_planes//groups, kernel_size, kernel_size, kernel_size), requires_grad=True) + if bias: + self.bias = nn.Parameter(torch.Tensor(K, out_planes)) + else: + self.bias = None + + + #TODO 初始化 + # nn.init.kaiming_uniform_(self.weight, ) + + def update_temperature(self): + self.attention.updata_temperature() + + def forward(self, x):#将batch视作维度变量，进行组卷积，因为组卷积的权重是不同的，动态卷积的权重也是不同的 + softmax_attention = self.attention(x) + batch_size, in_planes, depth, height, width = x.size() + x = x.view(1, -1, depth, height, width)# 变化成一个维度进行组卷积 + weight = self.weight.view(self.K, -1) + + # 动态卷积的权重的生成， 生成的是batch_size个卷积参数（每个参数不同） + aggregate_weight = torch.mm(softmax_attention, weight).view(-1, self.in_planes, self.kernel_size, self.kernel_size, self.kernel_size) + if self.bias is not None: + aggregate_bias = torch.mm(softmax_attention, self.bias).view(-1) + output = F.conv3d(x, weight=aggregate_weight, bias=aggregate_bias, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups*batch_size) + else: + output = F.conv3d(x, weight=aggregate_weight, bias=None, stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=self.groups * batch_size) + + output = output.view(batch_size, self.out_planes, output.size(-3), output.size(-2), output.size(-1)) + return output + + + + +if __name__ == '__main__': + x = torch.randn(12, 256, 64, 64) + y = torch.randn(12, 256, 64, 64) + + model = Dynamic_conv2d(in_planes=256, out_planes=256, kernel_size=3, ratio=0.25, padding=1,groups=1) + x = x.to('cuda:0') + y = y.to('cuda:0') + model.to('cuda') + # model.attention.cuda() + print(model(x,y).shape) + # nn.Conv3d() + # print(model(x).shape) + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # model.update_temperature() + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + # print(model(x).shape) + diff --git a/dagan/modules/generator.py b/dagan/modules/generator.py new file mode 100644 index 00000000..5492af65 --- /dev/null +++ b/dagan/modules/generator.py @@ -0,0 +1,324 @@ +import torch +from torch import nn +import torch.nn.functional as F +from modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d,SPADEResnetBlock +from modules.dense_motion import * +import pdb +from modules.AdaIN import calc_mean_std,adaptive_instance_normalization +from modules.dynamic_conv import Dynamic_conv2d +class SPADEGenerator(nn.Module): + def __init__(self): + super().__init__() + ic = 256 + cc = 4 + oc = 64 + norm_G = 'spadespectralinstance' + label_nc = 3 + cc + + self.compress = nn.Conv2d(ic, cc, 3, padding=1) + self.fc = nn.Conv2d(ic, 2 * ic, 3, padding=1) + + self.G_middle_0 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + self.G_middle_1 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + self.G_middle_2 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + # self.G_middle_3 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + # self.G_middle_4 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + # self.G_middle_5 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + + self.up_0 = SPADEResnetBlock(2 * ic, ic, norm_G, label_nc) + self.up_1 = SPADEResnetBlock(ic, oc, norm_G, label_nc) + self.conv_img = nn.Conv2d(oc, 3, 3, padding=1) + self.up = nn.Upsample(scale_factor=2) + + def forward(self, feature, image): + cp = self.compress(feature) + seg = torch.cat((F.interpolate(cp, size=(image.shape[2], image.shape[3])), image), dim=1) # 7, 256, 256 + + x = feature # 256, 64, 64 + x = self.fc(x) # 512, 64, 64 + x = self.G_middle_0(x, seg) + x = self.G_middle_1(x, seg) + x = self.G_middle_2(x, seg) + # x = self.G_middle_3(x, seg) + # x = self.G_middle_4(x, seg) + # x = self.G_middle_5(x, seg) + x = self.up(x) # 256, 128, 128 + x = self.up_0(x, seg) + x = self.up(x) # 64, 256, 256 + x = self.up_1(x, seg) + x = self.conv_img(F.leaky_relu(x, 2e-1)) + # x = torch.tanh(x) + x = F.sigmoid(x) + + return x + +class DepthAwareAttention(nn.Module): + """ depth-aware attention Layer""" + def __init__(self,in_dim,activation): + super(DepthAwareAttention,self).__init__() + self.chanel_in = in_dim + self.activation = activation + + self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1) + self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1) + self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1) + self.gamma = nn.Parameter(torch.zeros(1)) + + self.softmax = nn.Softmax(dim=-1) # + def forward(self,source,feat): + """ + inputs : + source : input feature maps( B X C X W X H) 256,64,64 + driving : input feature maps( B X C X W X H) 256,64,64 + returns : + out : self attention value + input feature + attention: B X N X N (N is Width*Height) + """ + m_batchsize,C,width ,height = source.size() + proj_query = self.activation(self.query_conv(source)).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N) [bz,32,64,64] + proj_key = self.activation(self.key_conv(feat)).view(m_batchsize,-1,width*height) # B X C x (*W*H) + energy = torch.bmm(proj_query,proj_key) # transpose check + attention = self.softmax(energy) # BX (N) X (N) + proj_value = self.activation(self.value_conv(feat)).view(m_batchsize,-1,width*height) # B X C X N + + out = torch.bmm(proj_value,attention.permute(0,2,1) ) + out = out.view(m_batchsize,C,width,height) + out = self.gamma*out + feat + + return out,attention + +#### main #### +class DepthAwareGenerator(nn.Module): + """ + Generator that given source image and and keypoints try to transform image according to movement trajectories + induced by keypoints. Generator follows Johnson architecture. + """ + + def __init__(self, num_channels, num_kp, block_expansion, max_features, num_down_blocks, + num_bottleneck_blocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False): + super(DepthAwareGenerator, self).__init__() + + if dense_motion_params is not None: + self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, num_channels=num_channels, + estimate_occlusion_map=estimate_occlusion_map, + **dense_motion_params) + else: + self.dense_motion_network = None + + self.first = SameBlock2d(num_channels, block_expansion, kernel_size=(7, 7), padding=(3, 3)) + down_blocks = [] + for i in range(num_down_blocks): + in_features = min(max_features, block_expansion * (2 ** i)) + out_features = min(max_features, block_expansion * (2 ** (i + 1))) + down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.down_blocks = nn.ModuleList(down_blocks) + + #source depth + self.src_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3)) + src_down_blocks = [] + for i in range(num_down_blocks): + in_features = min(max_features, block_expansion * (2 ** i)) + out_features = min(max_features, block_expansion * (2 ** (i + 1))) + src_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.src_down_blocks = nn.ModuleList(src_down_blocks) + + # #driving depth + # self.dst_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3)) + # dst_down_blocks = [] + # for i in range(num_down_blocks): + # in_features = min(max_features, block_expansion * (2 ** i)) + # out_features = min(max_features, block_expansion * (2 ** (i + 1))) + # dst_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + # self.dst_down_blocks = nn.ModuleList(dst_down_blocks) + + self.AttnModule = DepthAwareAttention(out_features,nn.ReLU()) + + up_blocks = [] + for i in range(num_down_blocks): + in_features = min(max_features, block_expansion * (2 ** (num_down_blocks - i))) + out_features = min(max_features, block_expansion * (2 ** (num_down_blocks - i - 1))) + up_blocks.append(UpBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.up_blocks = nn.ModuleList(up_blocks) + + self.bottleneck = torch.nn.Sequential() + in_features = min(max_features, block_expansion * (2 ** num_down_blocks)) + for i in range(num_bottleneck_blocks): + self.bottleneck.add_module('r' + str(i), ResBlock2d(in_features, kernel_size=(3, 3), padding=(1, 1))) + + self.final = nn.Conv2d(block_expansion, num_channels, kernel_size=(7, 7), padding=(3, 3)) + self.estimate_occlusion_map = estimate_occlusion_map + self.num_channels = num_channels + + def deform_input(self, inp, deformation): + _, h_old, w_old, _ = deformation.shape + _, _, h, w = inp.shape + if h_old != h or w_old != w: + deformation = deformation.permute(0, 3, 1, 2) + deformation = F.interpolate(deformation, size=(h, w), mode='bilinear') + deformation = deformation.permute(0, 2, 3, 1) + return F.grid_sample(inp, deformation) + + def forward(self, source_image, kp_driving, kp_source, source_depth, driving_depth): + # Encoding (downsampling) part + out = self.first(source_image) + for i in range(len(self.down_blocks)): + out = self.down_blocks[i](out) + + src_out = self.src_first(source_depth) + for i in range(len(self.src_down_blocks)): + src_out = self.src_down_blocks[i](src_out) + + # dst_out = self.dst_first(driving_depth) + # for i in range(len(self.down_blocks)): + # dst_out = self.dst_down_blocks[i](dst_out) + + # Transforming feature representation according to deformation and occlusion + output_dict = {} + if self.dense_motion_network is not None: + dense_motion = self.dense_motion_network(source_image=source_image, kp_driving=kp_driving, + kp_source=kp_source) + output_dict['mask'] = dense_motion['mask'] + output_dict['sparse_deformed'] = dense_motion['sparse_deformed'] + + if 'occlusion_map' in dense_motion: + occlusion_map = dense_motion['occlusion_map'] + output_dict['occlusion_map'] = occlusion_map + else: + occlusion_map = None + deformation = dense_motion['deformation'] + out = self.deform_input(out, deformation) + + if occlusion_map is not None: + if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]: + occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear') + out = out * occlusion_map + out,attention = self.AttnModule(src_out,out) + + output_dict["deformed"] = self.deform_input(source_image, deformation) + output_dict["attention"] = attention + + # Decoding part + out = self.bottleneck(out) + for i in range(len(self.up_blocks)): + out = self.up_blocks[i](out) + out = self.final(out) + out = F.sigmoid(out) + + output_dict["prediction"] = out + + return output_dict + +class SPADEDepthAwareGenerator(nn.Module): + """ + Generator that given source image and and keypoints try to transform image according to movement trajectories + induced by keypoints. Generator follows Johnson architecture. + """ + + def __init__(self, num_channels, num_kp, block_expansion, max_features, num_down_blocks, + num_bottleneck_blocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False): + super(SPADEDepthAwareGenerator, self).__init__() + + if dense_motion_params is not None: + self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, num_channels=num_channels, + estimate_occlusion_map=estimate_occlusion_map, + **dense_motion_params) + else: + self.dense_motion_network = None + + self.first = SameBlock2d(num_channels, block_expansion, kernel_size=(7, 7), padding=(3, 3)) + down_blocks = [] + for i in range(num_down_blocks): + in_features = min(max_features, block_expansion * (2 ** i)) + out_features = min(max_features, block_expansion * (2 ** (i + 1))) + down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.down_blocks = nn.ModuleList(down_blocks) + + #source depth + self.src_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3)) + src_down_blocks = [] + for i in range(num_down_blocks): + in_features = min(max_features, block_expansion * (2 ** i)) + out_features = min(max_features, block_expansion * (2 ** (i + 1))) + src_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.src_down_blocks = nn.ModuleList(src_down_blocks) + + # #driving depth + # self.dst_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3)) + # dst_down_blocks = [] + # for i in range(num_down_blocks): + # in_features = min(max_features, block_expansion * (2 ** i)) + # out_features = min(max_features, block_expansion * (2 ** (i + 1))) + # dst_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + # self.dst_down_blocks = nn.ModuleList(dst_down_blocks) + + self.AttnModule = DepthAwareAttention(out_features,nn.ReLU()) + self.decoder = SPADEGenerator() + + self.estimate_occlusion_map = estimate_occlusion_map + self.num_channels = num_channels + + def deform_input(self, inp, deformation): + _, h_old, w_old, _ = deformation.shape + _, _, h, w = inp.shape + if h_old != h or w_old != w: + deformation = deformation.permute(0, 3, 1, 2) + deformation = F.interpolate(deformation, size=(h, w), mode='bilinear') + deformation = deformation.permute(0, 2, 3, 1) + return F.grid_sample(inp, deformation) + + def forward(self, source_image, kp_driving, kp_source, source_depth, driving_depth): + # Encoding (downsampling) part + out = self.first(source_image) + for i in range(len(self.down_blocks)): + out = self.down_blocks[i](out) + + src_out = self.src_first(source_depth) + for i in range(len(self.src_down_blocks)): + src_out = self.src_down_blocks[i](src_out) + + # dst_out = self.dst_first(driving_depth) + # for i in range(len(self.down_blocks)): + # dst_out = self.dst_down_blocks[i](dst_out) + + # Transforming feature representation according to deformation and occlusion + output_dict = {} + if self.dense_motion_network is not None: + dense_motion = self.dense_motion_network(source_image=source_image, kp_driving=kp_driving, + kp_source=kp_source) + output_dict['mask'] = dense_motion['mask'] + output_dict['sparse_deformed'] = dense_motion['sparse_deformed'] + + if 'occlusion_map' in dense_motion: + occlusion_map = dense_motion['occlusion_map'] + output_dict['occlusion_map'] = occlusion_map + else: + occlusion_map = None + deformation = dense_motion['deformation'] + out = self.deform_input(out, deformation) + + if occlusion_map is not None: + if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]: + occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear') + out = out * occlusion_map + + out,attention = self.AttnModule(src_out,out) + + deformed_image = self.deform_input(source_image, deformation) + output_dict["deformed"] = deformed_image + output_dict["attention"] = attention + + if occlusion_map is not None: + if deformed_image.shape[2] != occlusion_map.shape[2] or deformed_image.shape[3] != occlusion_map.shape[3]: + occlusion_map = F.interpolate(occlusion_map, size=deformed_image.shape[2:], mode='bilinear') + deformed_image = deformed_image * occlusion_map + + out = self.decoder(out, deformed_image) + + # # Decoding part + # out = self.bottleneck(out) + # for i in range(len(self.up_blocks)): + # out = self.up_blocks[i](out) + # out = self.final(out) + # out = F.sigmoid(out) + output_dict["prediction"] = out + return output_dict diff --git a/dagan/modules/keypoint_detector.py b/dagan/modules/keypoint_detector.py new file mode 100644 index 00000000..b3906919 --- /dev/null +++ b/dagan/modules/keypoint_detector.py @@ -0,0 +1,75 @@ +from torch import nn +import torch +import torch.nn.functional as F +from modules.util import Hourglass, make_coordinate_grid, AntiAliasInterpolation2d,Hourglass_2branch +import pdb + +class KPDetector(nn.Module): + """ + Detecting a keypoints. Return keypoint position and jacobian near each keypoint. + """ + + def __init__(self, block_expansion, num_kp, num_channels, max_features, + num_blocks, temperature, estimate_jacobian=False, scale_factor=1, + single_jacobian_map=False, pad=0): + super(KPDetector, self).__init__() + self.predictor = Hourglass(block_expansion, in_features=num_channels, + max_features=max_features, num_blocks=num_blocks) + + self.kp = nn.Conv2d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=(7, 7), + padding=pad) + + if estimate_jacobian: + self.num_jacobian_maps = 1 if single_jacobian_map else num_kp + self.jacobian = nn.Conv2d(in_channels=self.predictor.out_filters, + out_channels=4 * self.num_jacobian_maps, kernel_size=(7, 7), padding=pad) + self.jacobian.weight.data.zero_() + self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 1] * self.num_jacobian_maps, dtype=torch.float)) + else: + self.jacobian = None + + self.temperature = temperature + self.scale_factor = scale_factor + if self.scale_factor != 1: + self.down = AntiAliasInterpolation2d(num_channels, self.scale_factor) + + def gaussian2kp(self, heatmap): + """ + Extract the mean and from a heatmap + """ + shape = heatmap.shape + heatmap = heatmap.unsqueeze(-1) + grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0) + value = (heatmap * grid).sum(dim=(2, 3)) + kp = {'value': value} + + return kp + + def forward(self, x): + if self.scale_factor != 1: + x = self.down(x) + feature_map = self.predictor(x) #x bz,4,64,64 + prediction = self.kp(feature_map) + + final_shape = prediction.shape + heatmap = prediction.view(final_shape[0], final_shape[1], -1) + heatmap = F.softmax(heatmap / self.temperature, dim=2) + heatmap = heatmap.view(*final_shape) + + out = self.gaussian2kp(heatmap) + + if self.jacobian is not None: + jacobian_map = self.jacobian(feature_map) + # pdb.set_trace() + jacobian_map = jacobian_map.reshape(final_shape[0], self.num_jacobian_maps, 4, final_shape[2], + final_shape[3]) + heatmap = heatmap.unsqueeze(2) + + jacobian = heatmap * jacobian_map + jacobian = jacobian.view(final_shape[0], final_shape[1], 4, -1) + jacobian = jacobian.sum(dim=-1) + jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 2, 2) + out['jacobian'] = jacobian + + return out + diff --git a/dagan/modules/model.py b/dagan/modules/model.py new file mode 100644 index 00000000..6074d93d --- /dev/null +++ b/dagan/modules/model.py @@ -0,0 +1,362 @@ +from torch import nn +import torch +import torch.nn.functional as F +from modules.util import AntiAliasInterpolation2d, make_coordinate_grid +from torchvision import models +import numpy as np +from torch.autograd import grad +import pdb +import depth + +class Vgg19(torch.nn.Module): + """ + Vgg19 network for perceptual loss. See Sec 3.3. + """ + def __init__(self, requires_grad=False): + super(Vgg19, self).__init__() + vgg_pretrained_features = models.vgg19(pretrained=True).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + for x in range(2): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(2, 7): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(7, 12): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(12, 21): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(21, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + + self.mean = torch.nn.Parameter(data=torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))), + requires_grad=False) + self.std = torch.nn.Parameter(data=torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))), + requires_grad=False) + + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + X = (X - self.mean) / self.std + h_relu1 = self.slice1(X) + h_relu2 = self.slice2(h_relu1) + h_relu3 = self.slice3(h_relu2) + h_relu4 = self.slice4(h_relu3) + h_relu5 = self.slice5(h_relu4) + out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] + return out + + +class ImagePyramide(torch.nn.Module): + """ + Create image pyramide for computing pyramide perceptual loss. See Sec 3.3 + """ + def __init__(self, scales, num_channels): + super(ImagePyramide, self).__init__() + downs = {} + for scale in scales: + downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale) + self.downs = nn.ModuleDict(downs) + + def forward(self, x): + out_dict = {} + for scale, down_module in self.downs.items(): + out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x) + return out_dict + + +class Transform: + """ + Random tps transformation for equivariance constraints. See Sec 3.3 + """ + def __init__(self, bs, **kwargs): + noise = torch.normal(mean=0, std=kwargs['sigma_affine'] * torch.ones([bs, 2, 3])) + self.theta = noise + torch.eye(2, 3).view(1, 2, 3) + self.bs = bs + + if ('sigma_tps' in kwargs) and ('points_tps' in kwargs): + self.tps = True + self.control_points = make_coordinate_grid((kwargs['points_tps'], kwargs['points_tps']), type=noise.type()) + self.control_points = self.control_points.unsqueeze(0) + self.control_params = torch.normal(mean=0, + std=kwargs['sigma_tps'] * torch.ones([bs, 1, kwargs['points_tps'] ** 2])) + else: + self.tps = False + + def transform_frame(self, frame): + grid = make_coordinate_grid(frame.shape[2:], type=frame.type()).unsqueeze(0) + grid = grid.view(1, frame.shape[2] * frame.shape[3], 2) + grid = self.warp_coordinates(grid).view(self.bs, frame.shape[2], frame.shape[3], 2) + return F.grid_sample(frame, grid, padding_mode="reflection") + + def warp_coordinates(self, coordinates): + theta = self.theta.type(coordinates.type()) + theta = theta.unsqueeze(1) + transformed = torch.matmul(theta[:, :, :, :2], coordinates.unsqueeze(-1)) + theta[:, :, :, 2:] + transformed = transformed.squeeze(-1) + + if self.tps: + control_points = self.control_points.type(coordinates.type()) + control_params = self.control_params.type(coordinates.type()) + distances = coordinates.view(coordinates.shape[0], -1, 1, 2) - control_points.view(1, 1, -1, 2) + distances = torch.abs(distances).sum(-1) + + result = distances ** 2 + result = result * torch.log(distances + 1e-6) + result = result * control_params + result = result.sum(dim=2).view(self.bs, coordinates.shape[1], 1) + transformed = transformed + result + + return transformed + + def jacobian(self, coordinates): + new_coordinates = self.warp_coordinates(coordinates) + grad_x = grad(new_coordinates[..., 0].sum(), coordinates, create_graph=True) + grad_y = grad(new_coordinates[..., 1].sum(), coordinates, create_graph=True) + jacobian = torch.cat([grad_x[0].unsqueeze(-2), grad_y[0].unsqueeze(-2)], dim=-2) + return jacobian + + +def detach_kp(kp): + return {key: value.detach() for key, value in kp.items()} + + +class GeneratorFullModel(torch.nn.Module): + """ + Merge all generator related updates into single model for better multi-gpu usage + """ + + def __init__(self, kp_extractor, generator, discriminator, train_params,opt): + super(GeneratorFullModel, self).__init__() + self.kp_extractor = kp_extractor + self.generator = generator + self.discriminator = discriminator + self.train_params = train_params + self.scales = train_params['scales'] + self.disc_scales = self.discriminator.module.scales + self.pyramid = ImagePyramide(self.scales, generator.module.num_channels) + if torch.cuda.is_available(): + self.pyramid = self.pyramid.cuda() + self.opt = opt + self.loss_weights = train_params['loss_weights'] + + if sum(self.loss_weights['perceptual']) != 0: + self.vgg = Vgg19() + if torch.cuda.is_available(): + self.vgg = self.vgg.cuda() + self.depth_encoder = depth.ResnetEncoder(50, False).cuda() + self.depth_decoder = depth.DepthDecoder(num_ch_enc=self.depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/depth_face_model_Voxceleb2_10w/encoder.pth',map_location='cpu') + loaded_dict_dec = torch.load('depth/models/depth_face_model_Voxceleb2_10w/depth.pth',map_location='cpu') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in self.depth_encoder.state_dict()} + self.depth_encoder.load_state_dict(filtered_dict_enc) + self.depth_decoder.load_state_dict(loaded_dict_dec) + self.set_requires_grad(self.depth_encoder, False) + self.set_requires_grad(self.depth_decoder, False) + self.depth_decoder.eval() + self.depth_encoder.eval() + def set_requires_grad(self, nets, requires_grad=False): + """Set requies_grad=Fasle for all the networks to avoid unnecessary computations + Parameters: + nets (network list) -- a list of networks + requires_grad (bool) -- whether the networks require gradients or not + """ + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad + def forward(self, x): + depth_source = None + depth_driving = None + outputs = self.depth_decoder(self.depth_encoder(x['source'])) + depth_source = outputs[("disp", 0)] + outputs = self.depth_decoder(self.depth_encoder(x['driving'])) + depth_driving = outputs[("disp", 0)] + + if self.opt.use_depth: + kp_source = self.kp_extractor(depth_source) + kp_driving = self.kp_extractor(depth_driving) + elif self.opt.rgbd: + source = torch.cat((x['source'],depth_source),1) + driving = torch.cat((x['driving'],depth_driving),1) + kp_source = self.kp_extractor(source) + kp_driving = self.kp_extractor(driving) + else: + kp_source = self.kp_extractor(x['source']) + kp_driving = self.kp_extractor(x['driving']) + generated = self.generator(x['source'], kp_source=kp_source, kp_driving=kp_driving, source_depth = depth_source, driving_depth = depth_driving) + generated.update({'kp_source': kp_source, 'kp_driving': kp_driving}) + loss_values = {} + pyramide_real = self.pyramid(x['driving']) + pyramide_generated = self.pyramid(generated['prediction']) + if sum(self.loss_weights['perceptual']) != 0: + value_total = 0 + for scale in self.scales: + x_vgg = self.vgg(pyramide_generated['prediction_' + str(scale)]) + y_vgg = self.vgg(pyramide_real['prediction_' + str(scale)]) + + for i, weight in enumerate(self.loss_weights['perceptual']): + value = torch.abs(x_vgg[i] - y_vgg[i].detach()).mean() + value_total += self.loss_weights['perceptual'][i] * value + loss_values['perceptual'] = value_total + + if self.loss_weights['generator_gan'] != 0: + + discriminator_maps_generated = self.discriminator(pyramide_generated, kp=detach_kp(kp_driving)) + + discriminator_maps_real = self.discriminator(pyramide_real, kp=detach_kp(kp_driving)) + value_total = 0 + for scale in self.disc_scales: + key = 'prediction_map_%s' % scale + value = ((1 - discriminator_maps_generated[key]) ** 2).mean() + value_total += self.loss_weights['generator_gan'] * value + loss_values['gen_gan'] = value_total + + if sum(self.loss_weights['feature_matching']) != 0: + value_total = 0 + for scale in self.disc_scales: + key = 'feature_maps_%s' % scale + for i, (a, b) in enumerate(zip(discriminator_maps_real[key], discriminator_maps_generated[key])): + if self.loss_weights['feature_matching'][i] == 0: + continue + value = torch.abs(a - b).mean() + value_total += self.loss_weights['feature_matching'][i] * value + loss_values['feature_matching'] = value_total + + if (self.loss_weights['equivariance_value'] + self.loss_weights['equivariance_jacobian']) != 0: + transform = Transform(x['driving'].shape[0], **self.train_params['transform_params']) + transformed_frame = transform.transform_frame(x['driving']) + if self.opt.use_depth: + outputs = self.depth_decoder(self.depth_encoder(transformed_frame)) + depth_transform = outputs[("disp", 0)] + transformed_kp = self.kp_extractor(depth_transform) + elif self.opt.rgbd: + outputs = self.depth_decoder(self.depth_encoder(transformed_frame)) + depth_transform = outputs[("disp", 0)] + transform_img = torch.cat((transformed_frame,depth_transform),1) + transformed_kp = self.kp_extractor(transform_img) + else: + transformed_kp = self.kp_extractor(transformed_frame) + + generated['transformed_frame'] = transformed_frame + generated['transformed_kp'] = transformed_kp + + ## Value loss part + if self.loss_weights['equivariance_value'] != 0: + value = torch.abs(kp_driving['value'] - transform.warp_coordinates(transformed_kp['value'])).mean() + loss_values['equivariance_value'] = self.loss_weights['equivariance_value'] * value + + ## jacobian loss part + if self.loss_weights['equivariance_jacobian'] != 0: + jacobian_transformed = torch.matmul(transform.jacobian(transformed_kp['value']), + transformed_kp['jacobian']) + + normed_driving = torch.inverse(kp_driving['jacobian']) + normed_transformed = jacobian_transformed + value = torch.matmul(normed_driving, normed_transformed) + + eye = torch.eye(2).view(1, 1, 2, 2).type(value.type()) + + value = torch.abs(eye - value).mean() + loss_values['equivariance_jacobian'] = self.loss_weights['equivariance_jacobian'] * value + + + if self.loss_weights['kp_distance']: + bz,num_kp,kp_dim = kp_source['value'].shape + sk = kp_source['value'].unsqueeze(2)-kp_source['value'].unsqueeze(1) + dk = kp_driving['value'].unsqueeze(2)-kp_driving['value'].unsqueeze(1) + source_dist_loss = (-torch.sign((torch.sqrt((sk*sk).sum(-1)+1e-8)+torch.eye(num_kp).cuda()*0.2)-0.2)+1).mean() + driving_dist_loss = (-torch.sign((torch.sqrt((dk*dk).sum(-1)+1e-8)+torch.eye(num_kp).cuda()*0.2)-0.2)+1).mean() + # driving_dist_loss = (torch.sign(1-(torch.sqrt((dk*dk).sum(-1)+1e-8)+torch.eye(num_kp).cuda()))+1).mean() + value_total = self.loss_weights['kp_distance']*(source_dist_loss+driving_dist_loss) + loss_values['kp_distance'] = value_total + if self.loss_weights['kp_prior']: + bz,num_kp,kp_dim = kp_source['value'].shape + sk = kp_source['value'].unsqueeze(2)-kp_source['value'].unsqueeze(1) + dk = kp_driving['value'].unsqueeze(2)-kp_driving['value'].unsqueeze(1) + dis_loss = torch.relu(0.1-torch.sqrt((sk*sk).sum(-1)+1e-8))+torch.relu(0.1-torch.sqrt((dk*dk).sum(-1)+1e-8)) + bs,nk,_=kp_source['value'].shape + scoor_depth = F.grid_sample(depth_source,kp_source['value'].view(bs,1,nk,-1)) + dcoor_depth = F.grid_sample(depth_driving,kp_driving['value'].view(bs,1,nk,-1)) + sd_loss = torch.abs(scoor_depth.mean(-1,keepdim=True) - kp_source['value'].view(bs,1,nk,-1)).mean() + dd_loss = torch.abs(dcoor_depth.mean(-1,keepdim=True) - kp_driving['value'].view(bs,1,nk,-1)).mean() + value_total = self.loss_weights['kp_distance']*(dis_loss+sd_loss+dd_loss) + loss_values['kp_distance'] = value_total + + + if self.loss_weights['kp_scale']: + bz,num_kp,kp_dim = kp_source['value'].shape + if self.opt.rgbd: + outputs = self.depth_decoder(self.depth_encoder(generated['prediction'])) + depth_pred = outputs[("disp", 0)] + pred = torch.cat((generated['prediction'],depth_pred),1) + kp_pred = self.kp_extractor(pred) + elif self.opt.use_depth: + outputs = self.depth_decoder(self.depth_encoder(generated['prediction'])) + depth_pred = outputs[("disp", 0)] + kp_pred = self.kp_extractor(depth_pred) + else: + kp_pred = self.kp_extractor(generated['prediction']) + + pred_mean = kp_pred['value'].mean(1,keepdim=True) + driving_mean = kp_driving['value'].mean(1,keepdim=True) + pk = kp_source['value']-pred_mean + dk = kp_driving['value']- driving_mean + pred_dist_loss = torch.sqrt((pk*pk).sum(-1)+1e-8) + driving_dist_loss = torch.sqrt((dk*dk).sum(-1)+1e-8) + scale_vec = driving_dist_loss/pred_dist_loss + bz,n = scale_vec.shape + value = torch.abs(scale_vec[:,:n-1]-scale_vec[:,1:]).mean() + value_total = self.loss_weights['kp_scale']*value + loss_values['kp_scale'] = value_total + if self.loss_weights['depth_constraint']: + bz,num_kp,kp_dim = kp_source['value'].shape + outputs = self.depth_decoder(self.depth_encoder(generated['prediction'])) + depth_pred = outputs[("disp", 0)] + value_total = self.loss_weights['depth_constraint']*torch.abs(depth_driving-depth_pred).mean() + loss_values['depth_constraint'] = value_total + return loss_values, generated + + + +class DiscriminatorFullModel(torch.nn.Module): + """ + Merge all discriminator related updates into single model for better multi-gpu usage + """ + + def __init__(self, kp_extractor, generator, discriminator, train_params): + super(DiscriminatorFullModel, self).__init__() + self.kp_extractor = kp_extractor + self.generator = generator + self.discriminator = discriminator + self.train_params = train_params + self.scales = self.discriminator.module.scales + self.pyramid = ImagePyramide(self.scales, generator.module.num_channels) + if torch.cuda.is_available(): + self.pyramid = self.pyramid.cuda() + + self.loss_weights = train_params['loss_weights'] + + def forward(self, x, generated): + pyramide_real = self.pyramid(x['driving']) + pyramide_generated = self.pyramid(generated['prediction'].detach()) + + kp_driving = generated['kp_driving'] + discriminator_maps_generated = self.discriminator(pyramide_generated, kp=detach_kp(kp_driving)) + discriminator_maps_real = self.discriminator(pyramide_real, kp=detach_kp(kp_driving)) + + loss_values = {} + value_total = 0 + for scale in self.scales: + key = 'prediction_map_%s' % scale + value = (1 - discriminator_maps_real[key]) ** 2 + discriminator_maps_generated[key] ** 2 + value_total += self.loss_weights['discriminator_gan'] * value.mean() + loss_values['disc_gan'] = value_total + + return loss_values diff --git a/dagan/modules/model_dataparallel.py b/dagan/modules/model_dataparallel.py new file mode 100644 index 00000000..46e85fb6 --- /dev/null +++ b/dagan/modules/model_dataparallel.py @@ -0,0 +1,362 @@ +from torch import nn +import torch +import torch.nn.functional as F +from modules.util import AntiAliasInterpolation2d, make_coordinate_grid +from torchvision import models +import numpy as np +from torch.autograd import grad +import pdb +import depth + +class Vgg19(torch.nn.Module): + """ + Vgg19 network for perceptual loss. See Sec 3.3. + """ + def __init__(self, requires_grad=False): + super(Vgg19, self).__init__() + vgg_pretrained_features = models.vgg19(pretrained=True).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + for x in range(2): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(2, 7): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(7, 12): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(12, 21): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(21, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + + self.mean = torch.nn.Parameter(data=torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))), + requires_grad=False) + self.std = torch.nn.Parameter(data=torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))), + requires_grad=False) + + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + X = (X - self.mean) / self.std + h_relu1 = self.slice1(X) + h_relu2 = self.slice2(h_relu1) + h_relu3 = self.slice3(h_relu2) + h_relu4 = self.slice4(h_relu3) + h_relu5 = self.slice5(h_relu4) + out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] + return out + + +class ImagePyramide(torch.nn.Module): + """ + Create image pyramide for computing pyramide perceptual loss. See Sec 3.3 + """ + def __init__(self, scales, num_channels): + super(ImagePyramide, self).__init__() + downs = {} + for scale in scales: + downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale) + self.downs = nn.ModuleDict(downs) + + def forward(self, x): + out_dict = {} + for scale, down_module in self.downs.items(): + out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x) + return out_dict + + +class Transform: + """ + Random tps transformation for equivariance constraints. See Sec 3.3 + """ + def __init__(self, bs, **kwargs): + noise = torch.normal(mean=0, std=kwargs['sigma_affine'] * torch.ones([bs, 2, 3])) + self.theta = noise + torch.eye(2, 3).view(1, 2, 3) + self.bs = bs + + if ('sigma_tps' in kwargs) and ('points_tps' in kwargs): + self.tps = True + self.control_points = make_coordinate_grid((kwargs['points_tps'], kwargs['points_tps']), type=noise.type()) + self.control_points = self.control_points.unsqueeze(0) + self.control_params = torch.normal(mean=0, + std=kwargs['sigma_tps'] * torch.ones([bs, 1, kwargs['points_tps'] ** 2])) + else: + self.tps = False + + def transform_frame(self, frame): + grid = make_coordinate_grid(frame.shape[2:], type=frame.type()).unsqueeze(0) + grid = grid.view(1, frame.shape[2] * frame.shape[3], 2) + grid = self.warp_coordinates(grid).view(self.bs, frame.shape[2], frame.shape[3], 2) + return F.grid_sample(frame, grid, padding_mode="reflection") + + def warp_coordinates(self, coordinates): + theta = self.theta.type(coordinates.type()) + theta = theta.unsqueeze(1) + transformed = torch.matmul(theta[:, :, :, :2], coordinates.unsqueeze(-1)) + theta[:, :, :, 2:] + transformed = transformed.squeeze(-1) + + if self.tps: + control_points = self.control_points.type(coordinates.type()) + control_params = self.control_params.type(coordinates.type()) + distances = coordinates.view(coordinates.shape[0], -1, 1, 2) - control_points.view(1, 1, -1, 2) + distances = torch.abs(distances).sum(-1) + + result = distances ** 2 + result = result * torch.log(distances + 1e-6) + result = result * control_params + result = result.sum(dim=2).view(self.bs, coordinates.shape[1], 1) + transformed = transformed + result + + return transformed + + def jacobian(self, coordinates): + new_coordinates = self.warp_coordinates(coordinates) + grad_x = grad(new_coordinates[..., 0].sum(), coordinates, create_graph=True) + grad_y = grad(new_coordinates[..., 1].sum(), coordinates, create_graph=True) + jacobian = torch.cat([grad_x[0].unsqueeze(-2), grad_y[0].unsqueeze(-2)], dim=-2) + return jacobian + + +def detach_kp(kp): + return {key: value.detach() for key, value in kp.items()} + + +class GeneratorFullModel(torch.nn.Module): + """ + Merge all generator related updates into single model for better multi-gpu usage + """ + + def __init__(self, kp_extractor, generator, discriminator, train_params,opt): + super(GeneratorFullModel, self).__init__() + self.kp_extractor = kp_extractor + self.generator = generator + self.discriminator = discriminator + self.train_params = train_params + self.scales = train_params['scales'] + self.disc_scales = self.discriminator.scales + self.pyramid = ImagePyramide(self.scales, generator.num_channels) + if torch.cuda.is_available(): + self.pyramid = self.pyramid.cuda() + self.opt = opt + self.loss_weights = train_params['loss_weights'] + + if sum(self.loss_weights['perceptual']) != 0: + self.vgg = Vgg19() + if torch.cuda.is_available(): + self.vgg = self.vgg.cuda() + self.depth_encoder = depth.ResnetEncoder(50, False).cuda() + self.depth_decoder = depth.DepthDecoder(num_ch_enc=self.depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/depth_face_model_Voxceleb2_10w/encoder.pth',map_location='cpu') + loaded_dict_dec = torch.load('depth/models/depth_face_model_Voxceleb2_10w/depth.pth',map_location='cpu') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in self.depth_encoder.state_dict()} + self.depth_encoder.load_state_dict(filtered_dict_enc) + self.depth_decoder.load_state_dict(loaded_dict_dec) + self.set_requires_grad(self.depth_encoder, False) + self.set_requires_grad(self.depth_decoder, False) + self.depth_decoder.eval() + self.depth_encoder.eval() + def set_requires_grad(self, nets, requires_grad=False): + """Set requies_grad=Fasle for all the networks to avoid unnecessary computations + Parameters: + nets (network list) -- a list of networks + requires_grad (bool) -- whether the networks require gradients or not + """ + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad + def forward(self, x): + depth_source = None + depth_driving = None + outputs = self.depth_decoder(self.depth_encoder(x['source'])) + depth_source = outputs[("disp", 0)] + outputs = self.depth_decoder(self.depth_encoder(x['driving'])) + depth_driving = outputs[("disp", 0)] + + if self.opt.use_depth: + kp_source = self.kp_extractor(depth_source) + kp_driving = self.kp_extractor(depth_driving) + elif self.opt.rgbd: + source = torch.cat((x['source'],depth_source),1) + driving = torch.cat((x['driving'],depth_driving),1) + kp_source = self.kp_extractor(source) + kp_driving = self.kp_extractor(driving) + else: + kp_source = self.kp_extractor(x['source']) + kp_driving = self.kp_extractor(x['driving']) + generated = self.generator(x['source'], kp_source=kp_source, kp_driving=kp_driving, source_depth = depth_source, driving_depth = depth_driving) + generated.update({'kp_source': kp_source, 'kp_driving': kp_driving}) + loss_values = {} + pyramide_real = self.pyramid(x['driving']) + pyramide_generated = self.pyramid(generated['prediction']) + if sum(self.loss_weights['perceptual']) != 0: + value_total = 0 + for scale in self.scales: + x_vgg = self.vgg(pyramide_generated['prediction_' + str(scale)]) + y_vgg = self.vgg(pyramide_real['prediction_' + str(scale)]) + + for i, weight in enumerate(self.loss_weights['perceptual']): + value = torch.abs(x_vgg[i] - y_vgg[i].detach()).mean() + value_total += self.loss_weights['perceptual'][i] * value + loss_values['perceptual'] = value_total + + if self.loss_weights['generator_gan'] != 0: + + discriminator_maps_generated = self.discriminator(pyramide_generated, kp=detach_kp(kp_driving)) + + discriminator_maps_real = self.discriminator(pyramide_real, kp=detach_kp(kp_driving)) + value_total = 0 + for scale in self.disc_scales: + key = 'prediction_map_%s' % scale + value = ((1 - discriminator_maps_generated[key]) ** 2).mean() + value_total += self.loss_weights['generator_gan'] * value + loss_values['gen_gan'] = value_total + + if sum(self.loss_weights['feature_matching']) != 0: + value_total = 0 + for scale in self.disc_scales: + key = 'feature_maps_%s' % scale + for i, (a, b) in enumerate(zip(discriminator_maps_real[key], discriminator_maps_generated[key])): + if self.loss_weights['feature_matching'][i] == 0: + continue + value = torch.abs(a - b).mean() + value_total += self.loss_weights['feature_matching'][i] * value + loss_values['feature_matching'] = value_total + + if (self.loss_weights['equivariance_value'] + self.loss_weights['equivariance_jacobian']) != 0: + transform = Transform(x['driving'].shape[0], **self.train_params['transform_params']) + transformed_frame = transform.transform_frame(x['driving']) + if self.opt.use_depth: + outputs = self.depth_decoder(self.depth_encoder(transformed_frame)) + depth_transform = outputs[("disp", 0)] + transformed_kp = self.kp_extractor(depth_transform) + elif self.opt.rgbd: + outputs = self.depth_decoder(self.depth_encoder(transformed_frame)) + depth_transform = outputs[("disp", 0)] + transform_img = torch.cat((transformed_frame,depth_transform),1) + transformed_kp = self.kp_extractor(transform_img) + else: + transformed_kp = self.kp_extractor(transformed_frame) + + generated['transformed_frame'] = transformed_frame + generated['transformed_kp'] = transformed_kp + + ## Value loss part + if self.loss_weights['equivariance_value'] != 0: + value = torch.abs(kp_driving['value'] - transform.warp_coordinates(transformed_kp['value'])).mean() + loss_values['equivariance_value'] = self.loss_weights['equivariance_value'] * value + + ## jacobian loss part + if self.loss_weights['equivariance_jacobian'] != 0: + jacobian_transformed = torch.matmul(transform.jacobian(transformed_kp['value']), + transformed_kp['jacobian']) + + normed_driving = torch.inverse(kp_driving['jacobian']) + normed_transformed = jacobian_transformed + value = torch.matmul(normed_driving, normed_transformed) + + eye = torch.eye(2).view(1, 1, 2, 2).type(value.type()) + + value = torch.abs(eye - value).mean() + loss_values['equivariance_jacobian'] = self.loss_weights['equivariance_jacobian'] * value + + + if self.loss_weights['kp_distance']: + bz,num_kp,kp_dim = kp_source['value'].shape + sk = kp_source['value'].unsqueeze(2)-kp_source['value'].unsqueeze(1) + dk = kp_driving['value'].unsqueeze(2)-kp_driving['value'].unsqueeze(1) + source_dist_loss = (-torch.sign((torch.sqrt((sk*sk).sum(-1)+1e-8)+torch.eye(num_kp).cuda()*0.2)-0.2)+1).mean() + driving_dist_loss = (-torch.sign((torch.sqrt((dk*dk).sum(-1)+1e-8)+torch.eye(num_kp).cuda()*0.2)-0.2)+1).mean() + # driving_dist_loss = (torch.sign(1-(torch.sqrt((dk*dk).sum(-1)+1e-8)+torch.eye(num_kp).cuda()))+1).mean() + value_total = self.loss_weights['kp_distance']*(source_dist_loss+driving_dist_loss) + loss_values['kp_distance'] = value_total + if self.loss_weights['kp_prior']: + bz,num_kp,kp_dim = kp_source['value'].shape + sk = kp_source['value'].unsqueeze(2)-kp_source['value'].unsqueeze(1) + dk = kp_driving['value'].unsqueeze(2)-kp_driving['value'].unsqueeze(1) + dis_loss = torch.relu(0.1-torch.sqrt((sk*sk).sum(-1)+1e-8))+torch.relu(0.1-torch.sqrt((dk*dk).sum(-1)+1e-8)) + bs,nk,_=kp_source['value'].shape + scoor_depth = F.grid_sample(depth_source,kp_source['value'].view(bs,1,nk,-1)) + dcoor_depth = F.grid_sample(depth_driving,kp_driving['value'].view(bs,1,nk,-1)) + sd_loss = torch.abs(scoor_depth.mean(-1,keepdim=True) - kp_source['value'].view(bs,1,nk,-1)).mean() + dd_loss = torch.abs(dcoor_depth.mean(-1,keepdim=True) - kp_driving['value'].view(bs,1,nk,-1)).mean() + value_total = self.loss_weights['kp_distance']*(dis_loss+sd_loss+dd_loss) + loss_values['kp_distance'] = value_total + + + if self.loss_weights['kp_scale']: + bz,num_kp,kp_dim = kp_source['value'].shape + if self.opt.rgbd: + outputs = self.depth_decoder(self.depth_encoder(generated['prediction'])) + depth_pred = outputs[("disp", 0)] + pred = torch.cat((generated['prediction'],depth_pred),1) + kp_pred = self.kp_extractor(pred) + elif self.opt.use_depth: + outputs = self.depth_decoder(self.depth_encoder(generated['prediction'])) + depth_pred = outputs[("disp", 0)] + kp_pred = self.kp_extractor(depth_pred) + else: + kp_pred = self.kp_extractor(generated['prediction']) + + pred_mean = kp_pred['value'].mean(1,keepdim=True) + driving_mean = kp_driving['value'].mean(1,keepdim=True) + pk = kp_source['value']-pred_mean + dk = kp_driving['value']- driving_mean + pred_dist_loss = torch.sqrt((pk*pk).sum(-1)+1e-8) + driving_dist_loss = torch.sqrt((dk*dk).sum(-1)+1e-8) + scale_vec = driving_dist_loss/pred_dist_loss + bz,n = scale_vec.shape + value = torch.abs(scale_vec[:,:n-1]-scale_vec[:,1:]).mean() + value_total = self.loss_weights['kp_scale']*value + loss_values['kp_scale'] = value_total + if self.loss_weights['depth_constraint']: + bz,num_kp,kp_dim = kp_source['value'].shape + outputs = self.depth_decoder(self.depth_encoder(generated['prediction'])) + depth_pred = outputs[("disp", 0)] + value_total = self.loss_weights['depth_constraint']*torch.abs(depth_driving-depth_pred).mean() + loss_values['depth_constraint'] = value_total + return loss_values, generated + + + +class DiscriminatorFullModel(torch.nn.Module): + """ + Merge all discriminator related updates into single model for better multi-gpu usage + """ + + def __init__(self, kp_extractor, generator, discriminator, train_params): + super(DiscriminatorFullModel, self).__init__() + self.kp_extractor = kp_extractor + self.generator = generator + self.discriminator = discriminator + self.train_params = train_params + self.scales = self.discriminator.scales + self.pyramid = ImagePyramide(self.scales, generator.num_channels) + if torch.cuda.is_available(): + self.pyramid = self.pyramid.cuda() + + self.loss_weights = train_params['loss_weights'] + + def forward(self, x, generated): + pyramide_real = self.pyramid(x['driving']) + pyramide_generated = self.pyramid(generated['prediction'].detach()) + + kp_driving = generated['kp_driving'] + discriminator_maps_generated = self.discriminator(pyramide_generated, kp=detach_kp(kp_driving)) + discriminator_maps_real = self.discriminator(pyramide_real, kp=detach_kp(kp_driving)) + + loss_values = {} + value_total = 0 + for scale in self.scales: + key = 'prediction_map_%s' % scale + value = (1 - discriminator_maps_real[key]) ** 2 + discriminator_maps_generated[key] ** 2 + value_total += self.loss_weights['discriminator_gan'] * value.mean() + loss_values['disc_gan'] = value_total + + return loss_values diff --git a/dagan/modules/util.py b/dagan/modules/util.py new file mode 100644 index 00000000..765c4f15 --- /dev/null +++ b/dagan/modules/util.py @@ -0,0 +1,399 @@ +from torch import nn + +import torch.nn.functional as F +import torch + +from sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d +import pdb +import torch.nn.utils.spectral_norm as spectral_norm +def kp2gaussian(kp, spatial_size, kp_variance): + """ + Transform a keypoint into gaussian like representation + """ + mean = kp['value'] + + coordinate_grid = make_coordinate_grid(spatial_size, mean.type()) + number_of_leading_dimensions = len(mean.shape) - 1 + shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape + coordinate_grid = coordinate_grid.view(*shape) + repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1) + coordinate_grid = coordinate_grid.repeat(*repeats) + + # Preprocess kp shape + shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 2) + mean = mean.view(*shape) + + mean_sub = (coordinate_grid - mean) + + out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance) + + return out + + +def make_coordinate_grid(spatial_size, type): + """ + Create a meshgrid [-1,1] x [-1,1] of given spatial_size. + """ + h, w = spatial_size + x = torch.arange(w).type(type) + y = torch.arange(h).type(type) + + x = (2 * (x / (w - 1)) - 1) + y = (2 * (y / (h - 1)) - 1) + + yy = y.view(-1, 1).repeat(1, w) + xx = x.view(1, -1).repeat(h, 1) + + meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2) + + return meshed + + +class ResBlock2d(nn.Module): + """ + Res block, preserve spatial resolution. + """ + + def __init__(self, in_features, kernel_size, padding): + super(ResBlock2d, self).__init__() + self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.norm1 = BatchNorm2d(in_features, affine=True) + self.norm2 = BatchNorm2d(in_features, affine=True) + + def forward(self, x): + out = self.norm1(x) + out = F.relu(out) + out = self.conv1(out) + out = self.norm2(out) + out = F.relu(out) + out = self.conv2(out) + out += x + return out + + +class UpBlock2d(nn.Module): + """ + Upsampling block for use in decoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(UpBlock2d, self).__init__() + + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = BatchNorm2d(out_features, affine=True) + + def forward(self, x): + out = F.interpolate(x, scale_factor=2) + out = self.conv(out) + out = self.norm(out) + out = F.relu(out) + return out + + +class DownBlock2d(nn.Module): + """ + Downsampling block for use in encoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(DownBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = BatchNorm2d(out_features, affine=True) + self.pool = nn.AvgPool2d(kernel_size=(2, 2)) + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = F.relu(out) + out = self.pool(out) + return out + + +class SameBlock2d(nn.Module): + """ + Simple block, preserve spatial resolution. + """ + + def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1): + super(SameBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, + kernel_size=kernel_size, padding=padding, groups=groups) + self.norm = BatchNorm2d(out_features, affine=True) + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = F.relu(out) + return out + + +class Encoder(nn.Module): + """ + Hourglass Encoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Encoder, self).__init__() + + down_blocks = [] + for i in range(num_blocks): + down_blocks.append(DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)), + min(max_features, block_expansion * (2 ** (i + 1))), + kernel_size=3, padding=1)) + self.down_blocks = nn.ModuleList(down_blocks) + + def forward(self, x): + outs = [x] + for down_block in self.down_blocks: + outs.append(down_block(outs[-1])) + return outs + + +class Decoder(nn.Module): + """ + Hourglass Decoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Decoder, self).__init__() + + up_blocks = [] + + for i in range(num_blocks)[::-1]: + in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1))) + out_filters = min(max_features, block_expansion * (2 ** i)) + up_blocks.append(UpBlock2d(in_filters, out_filters, kernel_size=3, padding=1)) + + self.up_blocks = nn.ModuleList(up_blocks) + self.out_filters = block_expansion + in_features + + def forward(self, x): + out = x.pop() + for up_block in self.up_blocks: + out = up_block(out) + skip = x.pop() + out = torch.cat([out, skip], dim=1) + return out + + +class Decoder_w_emb(nn.Module): + """ + Hourglass Decoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Decoder_w_emb, self).__init__() + + up_blocks = [] + + for i in range(num_blocks)[::-1]: + in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1))) + out_filters = min(max_features, block_expansion * (2 ** i)) + up_blocks.append(UpBlock2d(in_filters, out_filters, kernel_size=3, padding=1)) + + self.up_blocks = nn.ModuleList(up_blocks) + self.out_filters = block_expansion + in_features + + def forward(self, x): + feats = [] + out = x.pop() + feats.append(out) + for ind,up_block in enumerate(self.up_blocks): + out = up_block(out) + skip = x.pop() + feats.append(skip) + out = torch.cat([out, skip], dim=1) + return out,feats + +class Decoder_2branch(nn.Module): + """ + Hourglass Decoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Decoder_2branch, self).__init__() + up_blocks = [] + for i in range(num_blocks)[::-1]: + in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1))) + out_filters = min(max_features, block_expansion * (2 ** i)) + up_blocks.append(UpBlock2d(in_filters, out_filters, kernel_size=3, padding=1)) + + self.up_blocks = nn.ModuleList(up_blocks) + self.out_filters = block_expansion + in_features + + def forward(self, x): + # out = x.pop() + num_feat = len(x) + out=x[-1] + for i in range(len(self.up_blocks)): + out = self.up_blocks[i](out) + skip = x[-(i+1+1)] + out = torch.cat([out, skip], dim=1) + return out + + + +class Hourglass(nn.Module): + """ + Hourglass architecture. + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Hourglass, self).__init__() + self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features) + self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features) + self.out_filters = self.decoder.out_filters + def forward(self, x): + return self.decoder(self.encoder(x)) + +class Hourglass_2branch(nn.Module): + """ + Hourglass architecture. + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Hourglass_2branch, self).__init__() + self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features) + self.decoder_kp = Decoder_2branch(block_expansion, in_features, num_blocks, max_features) + self.decoder_mask = Decoder_2branch(block_expansion, in_features, num_blocks, max_features) + + self.out_filters = self.decoder_kp.out_filters + def forward(self, x): + embd= self.encoder(x) + kp_feat = self.decoder_kp(embd) + mask_feat = self.decoder_mask(embd) + return kp_feat,mask_feat + + +class Hourglass_w_emb(nn.Module): + """ + Hourglass architecture. + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Hourglass_w_emb, self).__init__() + self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features) + self.decoder = Decoder_w_emb(block_expansion, in_features, num_blocks, max_features) + self.out_filters = self.decoder.out_filters + + def forward(self, x): + embs = self.encoder(x) + result,feats = self.decoder(embs) + return feats,result +class AntiAliasInterpolation2d(nn.Module): + """ + Band-limited downsampling, for better preservation of the input signal. + """ + def __init__(self, channels, scale): + super(AntiAliasInterpolation2d, self).__init__() + sigma = (1 / scale - 1) / 2 + kernel_size = 2 * round(sigma * 4) + 1 + self.ka = kernel_size // 2 + self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka + + kernel_size = [kernel_size, kernel_size] + sigma = [sigma, sigma] + # The gaussian kernel is the product of the + # gaussian function of each dimension. + kernel = 1 + meshgrids = torch.meshgrid( + [ + torch.arange(size, dtype=torch.float32) + for size in kernel_size + ] + ) + for size, std, mgrid in zip(kernel_size, sigma, meshgrids): + mean = (size - 1) / 2 + kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2)) + + # Make sure sum of values in gaussian kernel equals 1. + kernel = kernel / torch.sum(kernel) + # Reshape to depthwise convolutional weight + kernel = kernel.view(1, 1, *kernel.size()) + kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) + + self.register_buffer('weight', kernel) + self.groups = channels + self.scale = scale + inv_scale = 1 / scale + self.int_inv_scale = int(inv_scale) + + def forward(self, input): + if self.scale == 1.0: + return input + + out = F.pad(input, (self.ka, self.kb, self.ka, self.kb)) + out = F.conv2d(out, weight=self.weight, groups=self.groups) + out = out[:, :, ::self.int_inv_scale, ::self.int_inv_scale] + + return out + + +class SPADE(nn.Module): + def __init__(self, norm_nc, label_nc): + super().__init__() + + self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False) + nhidden = 128 + + self.mlp_shared = nn.Sequential( + nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1), + nn.ReLU()) + self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) + self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) + + def forward(self, x, segmap): + normalized = self.param_free_norm(x) + segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest') + actv = self.mlp_shared(segmap) + gamma = self.mlp_gamma(actv) + beta = self.mlp_beta(actv) + out = normalized * (1 + gamma) + beta + return out + + +class SPADEResnetBlock(nn.Module): + def __init__(self, fin, fout, norm_G, label_nc, use_se=False, dilation=1): + super().__init__() + # Attributes + self.learned_shortcut = (fin != fout) + fmiddle = min(fin, fout) + self.use_se = use_se + # create conv layers + self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=dilation, dilation=dilation) + self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=dilation, dilation=dilation) + if self.learned_shortcut: + self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) + # apply spectral norm if specified + if 'spectral' in norm_G: + self.conv_0 = spectral_norm(self.conv_0) + self.conv_1 = spectral_norm(self.conv_1) + if self.learned_shortcut: + self.conv_s = spectral_norm(self.conv_s) + # define normalization layers + self.norm_0 = SPADE(fin, label_nc) + self.norm_1 = SPADE(fmiddle, label_nc) + if self.learned_shortcut: + self.norm_s = SPADE(fin, label_nc) + + def forward(self, x, seg1): + x_s = self.shortcut(x, seg1) + dx = self.conv_0(self.actvn(self.norm_0(x, seg1))) + dx = self.conv_1(self.actvn(self.norm_1(dx, seg1))) + out = x_s + dx + return out + + def shortcut(self, x, seg1): + if self.learned_shortcut: + x_s = self.conv_s(self.norm_s(x, seg1)) + else: + x_s = x + return x_s + + def actvn(self, x): + return F.leaky_relu(x, 2e-1) \ No newline at end of file diff --git a/dagan/output_dir/result.mp4 b/dagan/output_dir/result.mp4 new file mode 100644 index 00000000..6f5448fa Binary files /dev/null and b/dagan/output_dir/result.mp4 differ diff --git a/dagan/reconstruction.py b/dagan/reconstruction.py new file mode 100644 index 00000000..6333bb69 --- /dev/null +++ b/dagan/reconstruction.py @@ -0,0 +1,89 @@ +import os +from tqdm import tqdm +import torch +from torch.utils.data import DataLoader +from logger import Logger, Visualizer +import numpy as np +import imageio +from sync_batchnorm import DataParallelWithCallback +import depth + + +def reconstruction(config, generator, kp_detector, checkpoint, log_dir, dataset): + png_dir = os.path.join(log_dir, 'reconstruction/png') + log_dir = os.path.join(log_dir, 'reconstruction') + + if checkpoint is not None: + Logger.load_cpk(checkpoint, generator=generator, kp_detector=kp_detector) + else: + raise AttributeError("Checkpoint should be specified for mode='reconstruction'.") + dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1) + + if not os.path.exists(log_dir): + os.makedirs(log_dir) + + if not os.path.exists(png_dir): + os.makedirs(png_dir) + + loss_list = [] + if torch.cuda.is_available(): + generator = DataParallelWithCallback(generator) + kp_detector = DataParallelWithCallback(kp_detector) + + depth_encoder = depth.ResnetEncoder(18, False).cuda() + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_decoder.eval() + depth_encoder.eval() + + generator.eval() + kp_detector.eval() + + for it, x in tqdm(enumerate(dataloader)): + if config['reconstruction_params']['num_videos'] is not None: + if it > config['reconstruction_params']['num_videos']: + break + with torch.no_grad(): + predictions = [] + visualizations = [] + if torch.cuda.is_available(): + x['video'] = x['video'].cuda() + + outputs = depth_decoder(depth_encoder(x['video'][:, :, 0])) + depth_source = outputs[("disp", 0)] + source_rgbd = torch.cat((x['video'][:, :, 0],depth_source),1) + + kp_source = kp_detector(source_rgbd) + + for frame_idx in range(x['video'].shape[2]): + source = x['video'][:, :, 0] + + driving = x['video'][:, :, frame_idx] + outputs = depth_decoder(depth_encoder(driving)) + depth_driving = outputs[("disp", 0)] + driving_rgbd = torch.cat((driving,depth_driving),1) + + kp_driving = kp_detector(driving_rgbd) + out = generator(source, kp_source=kp_source, kp_driving=kp_driving) + out['kp_source'] = kp_source + out['kp_driving'] = kp_driving + del out['sparse_deformed'] + predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) + + visualization = Visualizer(**config['visualizer_params']).visualize(source=source, + driving=driving, out=out) + visualizations.append(visualization) + + loss_list.append(torch.abs(out['prediction'] - driving).mean().cpu().numpy()) + + predictions = np.concatenate(predictions, axis=1) + imageio.imsave(os.path.join(png_dir, x['name'][0] + '.png'), (255 * predictions).astype(np.uint8)) + + image_name = x['name'][0] + config['reconstruction_params']['format'] + imageio.mimsave(os.path.join(log_dir, image_name), visualizations) + + print("Reconstruction loss: %s" % np.mean(loss_list)) \ No newline at end of file diff --git a/dagan/requirements.txt b/dagan/requirements.txt new file mode 100644 index 00000000..646d0216 --- /dev/null +++ b/dagan/requirements.txt @@ -0,0 +1,35 @@ +absl-py==1.0.0 +certifi==2021.10.8 +cycler==0.11.0 +fonttools==4.33.2 +grpcio==1.44.0 +imageio==2.17.0 +importlib-metadata==4.11.3 +joblib==1.1.0 +kiwisolver==1.4.2 +lpips +Markdown==3.3.6 +matplotlib==3.5.1 +networkx==2.6.3 +numpy==1.21.6 +packaging==21.3 +pandas==1.3.5 +Pillow==9.1.0 +protobuf==3.20.1 +pyparsing==3.0.8 +python-dateutil==2.8.2 +pytz==2022.1 +PyWavelets==1.3.0 +PyYAML==5.4.1 +scikit-image==0.16.2 +scikit-learn==1.0.2 +scipy==1.7.3 +six==1.16.0 +sklearn==0.0 +tensorboard==1.15.0 +threadpoolctl==3.1.0 +tifffile==2021.11.2 +tqdm==4.64.0 +typing_extensions==4.2.0 +Werkzeug==2.1.1 +zipp==3.8.0 diff --git a/dagan/result.mp4 b/dagan/result.mp4 new file mode 100644 index 00000000..75ea55d9 Binary files /dev/null and b/dagan/result.mp4 differ diff --git a/dagan/result_img/.ipynb_checkpoints/generated_image-checkpoint.png b/dagan/result_img/.ipynb_checkpoints/generated_image-checkpoint.png new file mode 100644 index 00000000..5c84c5b5 Binary files /dev/null and b/dagan/result_img/.ipynb_checkpoints/generated_image-checkpoint.png differ diff --git a/dagan/result_img/generated_image.png b/dagan/result_img/generated_image.png new file mode 100644 index 00000000..5c84c5b5 Binary files /dev/null and b/dagan/result_img/generated_image.png differ diff --git a/dagan/run.py b/dagan/run.py new file mode 100644 index 00000000..3673a3a6 --- /dev/null +++ b/dagan/run.py @@ -0,0 +1,125 @@ +import matplotlib + +matplotlib.use('Agg') + +import os, sys +import yaml +from argparse import ArgumentParser +from shutil import copy + +from frames_dataset import FramesDataset +# from modules.generator import OcclusionAwareGenerator +import modules.generator as generator +from modules.discriminator import MultiScaleDiscriminator +# from modules.keypoint_detector import KPDetector +import modules.keypoint_detector as KPD +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel as DDP + +import torch +from torch.utils.tensorboard import SummaryWriter +from train import train +# from reconstruction import reconstruction + + +if __name__ == "__main__": + + if sys.version_info[0] < 3: + raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7") + + parser = ArgumentParser() + parser.add_argument("--config", required=True, help="path to config") + parser.add_argument("--mode", default="train", choices=["train", "reconstruction", "animate"]) + parser.add_argument("--log_dir", default='log', help="path to log into") + parser.add_argument("--checkpoint", default=None, help="path to checkpoint to restore") + parser.add_argument("--device_ids", default="0", type=lambda x: list(map(int, x.split(','))), + help="Names of the devices comma separated.") + parser.add_argument("--verbose", dest="verbose", action="store_true", help="Print model architecture") + parser.add_argument("--local_rank", type=int) + parser.add_argument("--use_depth",action='store_true',help='depth mode') + parser.add_argument("--rgbd",action='store_true',help='rgbd mode') + parser.add_argument("--kp_prior",action='store_true',help='use kp_prior in final objective function') + + # alter model + parser.add_argument("--generator",required=True,help='the type of genertor') + parser.add_argument("--kp_detector",default='KPDetector',type=str,help='the type of KPDetector') + parser.add_argument("--GFM",default='GeneratorFullModel',help='the type of GeneratorFullModel') + + parser.add_argument("--batchsize",type=int, default=-1,help='user defined batchsize') + parser.add_argument("--kp_num",type=int, default=-1,help='user defined keypoint number') + parser.add_argument("--kp_distance",type=int, default=10,help='the weight of kp_distance loss') + parser.add_argument("--depth_constraint",type=int, default=0,help='the weight of depth_constraint loss') + + parser.add_argument("--name",type=str,help='user defined model saved name') + + parser.set_defaults(verbose=False) + opt = parser.parse_args() + with open(opt.config) as f: + config = yaml.load(f) + + if opt.checkpoint is not None: + log_dir = os.path.join(*os.path.split(opt.checkpoint)[:-1]) + else: + log_dir = os.path.join(opt.log_dir, os.path.basename(opt.config).split('.')[0]) + log_dir += opt.name + + + print("Training...") + + dist.init_process_group(backend='nccl', init_method='env://') + torch.cuda.set_device(opt.local_rank) + device=torch.device("cuda",opt.local_rank) + config['train_params']['loss_weights']['depth_constraint'] = opt.depth_constraint + config['train_params']['loss_weights']['kp_distance'] = opt.kp_distance + if opt.kp_prior: + config['train_params']['loss_weights']['kp_distance'] = 0 + config['train_params']['loss_weights']['kp_prior'] = 10 + if opt.batchsize != -1: + config['train_params']['batch_size'] = opt.batchsize + if opt.kp_num != -1: + config['model_params']['common_params']['num_kp'] = opt.kp_num + # create generator + generator = getattr(generator, opt.generator)(**config['model_params']['generator_params'], + **config['model_params']['common_params']) + generator.to(device) + if opt.verbose: + print(generator) + generator= torch.nn.SyncBatchNorm.convert_sync_batchnorm(generator) + + # create discriminator + discriminator = MultiScaleDiscriminator(**config['model_params']['discriminator_params'], + **config['model_params']['common_params']) + + discriminator.to(device) + if opt.verbose: + print(discriminator) + discriminator= torch.nn.SyncBatchNorm.convert_sync_batchnorm(discriminator) + + # create kp_detector + if opt.use_depth: + config['model_params']['common_params']['num_channels'] = 1 + if opt.rgbd: + config['model_params']['common_params']['num_channels'] = 4 + + kp_detector = getattr(KPD, opt.kp_detector)(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + kp_detector.to(device) + if opt.verbose: + print(kp_detector) + kp_detector= torch.nn.SyncBatchNorm.convert_sync_batchnorm(kp_detector) + + kp_detector = DDP(kp_detector,device_ids=[opt.local_rank],broadcast_buffers=False) + discriminator = DDP(discriminator,device_ids=[opt.local_rank],broadcast_buffers=False) + generator = DDP(generator,device_ids=[opt.local_rank],broadcast_buffers=False) + + dataset = FramesDataset(is_train=(opt.mode == 'train'), **config['dataset_params']) + if not os.path.exists(log_dir): + os.makedirs(log_dir) + if not os.path.exists(os.path.join(log_dir, os.path.basename(opt.config))): + copy(opt.config, log_dir) + + if not os.path.exists(os.path.join(log_dir,'log')): + os.makedirs(os.path.join(log_dir,'log')) + writer = SummaryWriter(os.path.join(log_dir,'log')) + if opt.mode == 'train': + train(config, generator, discriminator, kp_detector, opt.checkpoint, log_dir, dataset, opt.local_rank,device,opt,writer) \ No newline at end of file diff --git a/dagan/run_dataparallel.py b/dagan/run_dataparallel.py new file mode 100644 index 00000000..3a7a0d8d --- /dev/null +++ b/dagan/run_dataparallel.py @@ -0,0 +1,109 @@ + +import os, sys +import yaml +from argparse import ArgumentParser +from shutil import copy + +from frames_dataset import FramesDataset +import modules.generator as generator +from modules.discriminator import MultiScaleDiscriminator +import modules.keypoint_detector as KPD + +import torch +from torch.utils.tensorboard import SummaryWriter +from train_dataparallel import train +# from reconstruction import reconstruction + +if __name__ == "__main__": + + if sys.version_info[0] < 3: + raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7") + + parser = ArgumentParser() + parser.add_argument("--config", required=True, help="path to config") + parser.add_argument("--mode", default="train", choices=["train", "reconstruction", "animate"]) + parser.add_argument("--log_dir", default='log', help="path to log into") + parser.add_argument("--checkpoint", default=None, help="path to checkpoint to restore") + parser.add_argument("--device_ids", default="0", type=lambda x: list(map(int, x.split(','))), + help="Names of the devices comma separated.") + parser.add_argument("--verbose", dest="verbose", action="store_true", help="Print model architecture") + parser.add_argument("--use_depth",action='store_true',help='depth mode') + parser.add_argument("--rgbd",action='store_true',help='rgbd mode') + parser.add_argument("--kp_prior",action='store_true',help='use kp_prior in final objective function') + + # alter model + parser.add_argument("--generator",required=True,help='the type of genertor') + parser.add_argument("--kp_detector",default='KPDetector',type=str,help='the type of KPDetector') + parser.add_argument("--GFM",default='GeneratorFullModel',help='the type of GeneratorFullModel') + + parser.add_argument("--batchsize",type=int, default=-1,help='user defined batchsize') + parser.add_argument("--kp_num",type=int, default=-1,help='user defined keypoint number') + parser.add_argument("--kp_distance",type=int, default=10,help='the weight of kp_distance loss') + parser.add_argument("--depth_constraint",type=int, default=0,help='the weight of depth_constraint loss') + + parser.add_argument("--name",type=str,help='user defined model saved name') + + parser.set_defaults(verbose=False) + opt = parser.parse_args() + with open(opt.config) as f: + config = yaml.load(f) + + if opt.checkpoint is not None: + log_dir = os.path.join(*os.path.split(opt.checkpoint)[:-1]) + else: + log_dir = os.path.join(opt.log_dir, os.path.basename(opt.config).split('.')[0]) + log_dir += opt.name + + + print("Training...") + + config['train_params']['loss_weights']['depth_constraint'] = opt.depth_constraint + config['train_params']['loss_weights']['kp_distance'] = opt.kp_distance + if opt.kp_prior: + config['train_params']['loss_weights']['kp_distance'] = 0 + config['train_params']['loss_weights']['kp_prior'] = 10 + if opt.batchsize != -1: + config['train_params']['batch_size'] = opt.batchsize + if opt.kp_num != -1: + config['model_params']['common_params']['num_kp'] = opt.kp_num + # create generator + generator = getattr(generator, opt.generator)(**config['model_params']['generator_params'], + **config['model_params']['common_params']) + if torch.cuda.is_available(): + generator.to(opt.device_ids[0]) + if opt.verbose: + print(generator) + + # create discriminator + discriminator = MultiScaleDiscriminator(**config['model_params']['discriminator_params'], + **config['model_params']['common_params']) + + if torch.cuda.is_available(): + discriminator.to(opt.device_ids[0]) + if opt.verbose: + print(discriminator) + + # create kp_detector + if opt.use_depth: + config['model_params']['common_params']['num_channels'] = 1 + if opt.rgbd: + config['model_params']['common_params']['num_channels'] = 4 + + kp_detector = getattr(KPD, opt.kp_detector)(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + if torch.cuda.is_available(): + kp_detector.to(opt.device_ids[0]) + if opt.verbose: + print(kp_detector) + + dataset = FramesDataset(is_train=(opt.mode == 'train'), **config['dataset_params']) + if not os.path.exists(log_dir): + os.makedirs(log_dir) + if not os.path.exists(os.path.join(log_dir, os.path.basename(opt.config))): + copy(opt.config, log_dir) + + if not os.path.exists(os.path.join(log_dir,'log')): + os.makedirs(os.path.join(log_dir,'log')) + writer = SummaryWriter(os.path.join(log_dir,'log')) + if opt.mode == 'train': + train(config, generator, discriminator, kp_detector, opt.checkpoint, log_dir, dataset, opt.device_ids, opt,writer) \ No newline at end of file diff --git a/dagan/sync_batchnorm/__init__.py b/dagan/sync_batchnorm/__init__.py new file mode 100644 index 00000000..bc8709d9 --- /dev/null +++ b/dagan/sync_batchnorm/__init__.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +# File : __init__.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d +from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/dagan/sync_batchnorm/__pycache__/__init__.cpython-38.pyc b/dagan/sync_batchnorm/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 00000000..2c64d1ab Binary files /dev/null and b/dagan/sync_batchnorm/__pycache__/__init__.cpython-38.pyc differ diff --git a/dagan/sync_batchnorm/__pycache__/batchnorm.cpython-38.pyc b/dagan/sync_batchnorm/__pycache__/batchnorm.cpython-38.pyc new file mode 100644 index 00000000..db7b61ee Binary files /dev/null and b/dagan/sync_batchnorm/__pycache__/batchnorm.cpython-38.pyc differ diff --git a/dagan/sync_batchnorm/__pycache__/comm.cpython-38.pyc b/dagan/sync_batchnorm/__pycache__/comm.cpython-38.pyc new file mode 100644 index 00000000..9703e652 Binary files /dev/null and b/dagan/sync_batchnorm/__pycache__/comm.cpython-38.pyc differ diff --git a/dagan/sync_batchnorm/__pycache__/replicate.cpython-38.pyc b/dagan/sync_batchnorm/__pycache__/replicate.cpython-38.pyc new file mode 100644 index 00000000..418d6658 Binary files /dev/null and b/dagan/sync_batchnorm/__pycache__/replicate.cpython-38.pyc differ diff --git a/dagan/sync_batchnorm/batchnorm.py b/dagan/sync_batchnorm/batchnorm.py new file mode 100644 index 00000000..5f4e763f --- /dev/null +++ b/dagan/sync_batchnorm/batchnorm.py @@ -0,0 +1,315 @@ +# -*- coding: utf-8 -*- +# File : batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import collections + +import torch +import torch.nn.functional as F + +from torch.nn.modules.batchnorm import _BatchNorm +from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast + +from .comm import SyncMaster + +__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d'] + + +def _sum_ft(tensor): + """sum over the first and last dimention""" + return tensor.sum(dim=0).sum(dim=-1) + + +def _unsqueeze_ft(tensor): + """add new dementions at the front and the tail""" + return tensor.unsqueeze(0).unsqueeze(-1) + + +_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) +_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) + + +class _SynchronizedBatchNorm(_BatchNorm): + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True): + super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine) + + self._sync_master = SyncMaster(self._data_parallel_master) + + self._is_parallel = False + self._parallel_id = None + self._slave_pipe = None + + def forward(self, input): + # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. + if not (self._is_parallel and self.training): + return F.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + # Resize the input to (B, C, -1). + input_shape = input.size() + input = input.view(input.size(0), self.num_features, -1) + + # Compute the sum and square-sum. + sum_size = input.size(0) * input.size(2) + input_sum = _sum_ft(input) + input_ssum = _sum_ft(input ** 2) + + # Reduce-and-broadcast the statistics. + if self._parallel_id == 0: + mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) + else: + mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) + + # Compute the output. + if self.affine: + # MJY:: Fuse the multiplication for speed. + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) + else: + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) + + # Reshape it. + return output.view(input_shape) + + def __data_parallel_replicate__(self, ctx, copy_id): + self._is_parallel = True + self._parallel_id = copy_id + + # parallel_id == 0 means master device. + if self._parallel_id == 0: + ctx.sync_master = self._sync_master + else: + self._slave_pipe = ctx.sync_master.register_slave(copy_id) + + def _data_parallel_master(self, intermediates): + """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" + + # Always using same "device order" makes the ReduceAdd operation faster. + # Thanks to:: Tete Xiao (http://tetexiao.com/) + intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) + + to_reduce = [i[1][:2] for i in intermediates] + to_reduce = [j for i in to_reduce for j in i] # flatten + target_gpus = [i[1].sum.get_device() for i in intermediates] + + sum_size = sum([i[1].sum_size for i in intermediates]) + sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) + mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) + + broadcasted = Broadcast.apply(target_gpus, mean, inv_std) + + outputs = [] + for i, rec in enumerate(intermediates): + outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2]))) + + return outputs + + def _compute_mean_std(self, sum_, ssum, size): + """Compute the mean and standard-deviation with sum and square-sum. This method + also maintains the moving average on the master device.""" + assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' + mean = sum_ / size + sumvar = ssum - sum_ * mean + unbias_var = sumvar / (size - 1) + bias_var = sumvar / size + + self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data + self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data + + return mean, bias_var.clamp(self.eps) ** -0.5 + + +class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): + r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm1d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError('expected 2D or 3D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm1d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm2d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError('expected 4D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm2d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch + of 4d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm3d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm + or Spatio-temporal BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x depth x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, D, H, W)` + - Output: :math:`(N, C, D, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 5: + raise ValueError('expected 5D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm3d, self)._check_input_dim(input) diff --git a/dagan/sync_batchnorm/comm.py b/dagan/sync_batchnorm/comm.py new file mode 100644 index 00000000..922f8c4a --- /dev/null +++ b/dagan/sync_batchnorm/comm.py @@ -0,0 +1,137 @@ +# -*- coding: utf-8 -*- +# File : comm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import queue +import collections +import threading + +__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster'] + + +class FutureResult(object): + """A thread-safe future implementation. Used only as one-to-one pipe.""" + + def __init__(self): + self._result = None + self._lock = threading.Lock() + self._cond = threading.Condition(self._lock) + + def put(self, result): + with self._lock: + assert self._result is None, 'Previous result has\'t been fetched.' + self._result = result + self._cond.notify() + + def get(self): + with self._lock: + if self._result is None: + self._cond.wait() + + res = self._result + self._result = None + return res + + +_MasterRegistry = collections.namedtuple('MasterRegistry', ['result']) +_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result']) + + +class SlavePipe(_SlavePipeBase): + """Pipe for master-slave communication.""" + + def run_slave(self, msg): + self.queue.put((self.identifier, msg)) + ret = self.result.get() + self.queue.put(True) + return ret + + +class SyncMaster(object): + """An abstract `SyncMaster` object. + + - During the replication, as the data parallel will trigger an callback of each module, all slave devices should + call `register(id)` and obtain an `SlavePipe` to communicate with the master. + - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, + and passed to a registered callback. + - After receiving the messages, the master device should gather the information and determine to message passed + back to each slave devices. + """ + + def __init__(self, master_callback): + """ + + Args: + master_callback: a callback to be invoked after having collected messages from slave devices. + """ + self._master_callback = master_callback + self._queue = queue.Queue() + self._registry = collections.OrderedDict() + self._activated = False + + def __getstate__(self): + return {'master_callback': self._master_callback} + + def __setstate__(self, state): + self.__init__(state['master_callback']) + + def register_slave(self, identifier): + """ + Register an slave device. + + Args: + identifier: an identifier, usually is the device id. + + Returns: a `SlavePipe` object which can be used to communicate with the master device. + + """ + if self._activated: + assert self._queue.empty(), 'Queue is not clean before next initialization.' + self._activated = False + self._registry.clear() + future = FutureResult() + self._registry[identifier] = _MasterRegistry(future) + return SlavePipe(identifier, self._queue, future) + + def run_master(self, master_msg): + """ + Main entry for the master device in each forward pass. + The messages were first collected from each devices (including the master device), and then + an callback will be invoked to compute the message to be sent back to each devices + (including the master device). + + Args: + master_msg: the message that the master want to send to itself. This will be placed as the first + message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. + + Returns: the message to be sent back to the master device. + + """ + self._activated = True + + intermediates = [(0, master_msg)] + for i in range(self.nr_slaves): + intermediates.append(self._queue.get()) + + results = self._master_callback(intermediates) + assert results[0][0] == 0, 'The first result should belongs to the master.' + + for i, res in results: + if i == 0: + continue + self._registry[i].result.put(res) + + for i in range(self.nr_slaves): + assert self._queue.get() is True + + return results[0][1] + + @property + def nr_slaves(self): + return len(self._registry) diff --git a/dagan/sync_batchnorm/replicate.py b/dagan/sync_batchnorm/replicate.py new file mode 100644 index 00000000..b71c7b8e --- /dev/null +++ b/dagan/sync_batchnorm/replicate.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate diff --git a/dagan/sync_batchnorm/unittest.py b/dagan/sync_batchnorm/unittest.py new file mode 100644 index 00000000..0675c022 --- /dev/null +++ b/dagan/sync_batchnorm/unittest.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +# File : unittest.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import unittest + +import numpy as np +from torch.autograd import Variable + + +def as_numpy(v): + if isinstance(v, Variable): + v = v.data + return v.cpu().numpy() + + +class TorchTestCase(unittest.TestCase): + def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3): + npa, npb = as_numpy(a), as_numpy(b) + self.assertTrue( + np.allclose(npa, npb, atol=atol), + 'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max()) + ) diff --git a/dagan/test.py b/dagan/test.py new file mode 100644 index 00000000..52326f18 --- /dev/null +++ b/dagan/test.py @@ -0,0 +1,48 @@ +import os +from skimage import io + +# 检查数据集路径 +dataset_path = "/root/autodl-tmp/vox-png" +train_path = os.path.join(dataset_path, "train") +test_path = os.path.join(dataset_path, "test") + +print("数据集基本信息:") +print(f"数据集根目录: {dataset_path}") +print(f"训练集目录: {train_path}") +print(f"测试集目录: {test_path}") +print("-" * 50) + +# 验证训练集 +train_ids = os.listdir(train_path) +print(f"训练数据统计:") +print(f"训练标识数量: {len(train_ids)}") +print(f"前5个训练标识: {train_ids[:5]}") +print("-" * 50) + +# 检查图像读取 +sample_id = train_ids[0] +print(f"示例ID信息:") +print(f"选中的示例ID: {sample_id}") +print(f"示例ID完整路径: {os.path.join(train_path, sample_id)}") +print("-" * 50) + +try: + sample_video = os.listdir(os.path.join(train_path, sample_id))[0] + print(f"视频信息:") + print(f"示例视频名称: {sample_video}") + print(f"示例视频完整路径: {os.path.join(train_path, sample_id, sample_video)}") + print("-" * 50) + + sample_frame = os.listdir(os.path.join(train_path, sample_id, sample_video))[0] + print(f"帧信息:") + print(f"示例帧名称: {sample_frame}") + print(f"示例帧完整路径: {os.path.join(train_path, sample_id, sample_video, sample_frame)}") + print("-" * 50) + + sample_path = os.path.join(train_path, sample_id, sample_video, sample_frame) + image = io.imread(sample_path) + print(f"图像信息:") + print(f"图像形状: {image.shape}") + print(f"图像类型: {image.dtype}") +except Exception as e: + print(f"错误信息: {str(e)}") \ No newline at end of file diff --git a/dagan/train.py b/dagan/train.py new file mode 100644 index 00000000..9539eaa4 --- /dev/null +++ b/dagan/train.py @@ -0,0 +1,117 @@ +from tqdm import trange +import torch + +from torch.utils.data import DataLoader + +from logger import Logger +from modules.model import GeneratorFullModel, DiscriminatorFullModel +import modules.model as MODEL +from tqdm import tqdm +from torch.optim.lr_scheduler import MultiStepLR +from torch.nn.parallel import DistributedDataParallel as DDP +import pdb +from sync_batchnorm import DataParallelWithCallback +from evaluation_dataset import EvaluationDataset + +from frames_dataset import DatasetRepeater + + +def train(config, generator, discriminator, kp_detector, checkpoint, log_dir, dataset, rank,device,opt,writer): + train_params = config['train_params'] + + optimizer_generator = torch.optim.Adam(generator.parameters(), lr=train_params['lr_generator'], betas=(0.5, 0.999)) + optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=train_params['lr_discriminator'], betas=(0.5, 0.999)) + optimizer_kp_detector = torch.optim.Adam(kp_detector.parameters(), lr=train_params['lr_kp_detector'], betas=(0.5, 0.999)) + + if checkpoint is not None: + start_epoch = Logger.load_cpk(checkpoint, generator, discriminator, kp_detector, + optimizer_generator, optimizer_discriminator, + None if train_params['lr_kp_detector'] == 0 else optimizer_kp_detector) + else: + start_epoch = 0 + + scheduler_generator = MultiStepLR(optimizer_generator, train_params['epoch_milestones'], gamma=0.1, + last_epoch=start_epoch - 1) + scheduler_discriminator = MultiStepLR(optimizer_discriminator, train_params['epoch_milestones'], gamma=0.1, + last_epoch=start_epoch - 1) + scheduler_kp_detector = MultiStepLR(optimizer_kp_detector, train_params['epoch_milestones'], gamma=0.1, + last_epoch=-1 + start_epoch * (train_params['lr_kp_detector'] != 0)) + + if 'num_repeats' in train_params or train_params['num_repeats'] != 1: + dataset = DatasetRepeater(dataset, train_params['num_repeats']) + sampler = torch.utils.data.distributed.DistributedSampler(dataset,num_replicas=torch.cuda.device_count(),rank=rank) + dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=False, num_workers=16,sampler=sampler, drop_last=True) + + + generator_full = getattr(MODEL,opt.GFM)(kp_detector, generator, discriminator, train_params,opt) + discriminator_full = DiscriminatorFullModel(kp_detector, generator, discriminator, train_params) + test_dataset = EvaluationDataset(dataroot='/data/fhongac/origDataset/vox1_frames',pairs_list='data/vox_evaluation.csv') + test_dataloader = torch.utils.data.DataLoader( + test_dataset, + batch_size = 1, + shuffle=False, + num_workers=4) + with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], checkpoint_freq=train_params['checkpoint_freq']) as logger: + for epoch in trange(start_epoch, train_params['num_epochs']): + #parallel + sampler.set_epoch(epoch) + total = len(dataloader) + epoch_train_loss = 0 + generator.train(), discriminator.train(), kp_detector.train() + with tqdm(total=total) as par: + for i,x in enumerate(dataloader): + x['source'] = x['source'].to(device) + x['driving'] = x['driving'].to(device) + losses_generator, generated = generator_full(x) + + loss_values = [val.mean() for val in losses_generator.values()] + loss = sum(loss_values) + loss.backward() + optimizer_generator.step() + optimizer_generator.zero_grad() + optimizer_kp_detector.step() + optimizer_kp_detector.zero_grad() + epoch_train_loss+=loss.item() + + if train_params['loss_weights']['generator_gan'] != 0: + optimizer_discriminator.zero_grad() + losses_discriminator = discriminator_full(x, generated) + loss_values = [val.mean() for val in losses_discriminator.values()] + loss = sum(loss_values) + + loss.backward() + optimizer_discriminator.step() + optimizer_discriminator.zero_grad() + else: + losses_discriminator = {} + + losses_generator.update(losses_discriminator) + losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()} + # for k,v in losses.items(): + # writer.add_scalar(k, v, total*epoch+i) + logger.log_iter(losses=losses) + par.update(1) + epoch_train_loss = epoch_train_loss/total + if (epoch + 1) % train_params['checkpoint_freq'] == 0: + writer.add_scalar('epoch_train_loss', epoch_train_loss, epoch) + scheduler_generator.step() + scheduler_discriminator.step() + scheduler_kp_detector.step() + logger.log_epoch(epoch, {'generator': generator, + 'discriminator': discriminator, + 'kp_detector': kp_detector, + 'optimizer_generator': optimizer_generator, + 'optimizer_discriminator': optimizer_discriminator, + 'optimizer_kp_detector': optimizer_kp_detector}, inp=x, out=generated) + generator.eval(), discriminator.eval(), kp_detector.eval() + if (epoch + 1) % train_params['checkpoint_freq'] == 0: + epoch_eval_loss = 0 + for i, data in tqdm(enumerate(test_dataloader)): + data['source'] = data['source'].cuda() + data['driving'] = data['driving'].cuda() + losses_generator, generated = generator_full(data) + loss_values = [val.mean() for val in losses_generator.values()] + loss = sum(loss_values) + epoch_eval_loss+=loss.item() + epoch_eval_loss = epoch_eval_loss/len(test_dataloader) + writer.add_scalar('epoch_eval_loss', epoch_eval_loss, epoch) diff --git a/dagan/train_dataparallel.py b/dagan/train_dataparallel.py new file mode 100644 index 00000000..1a6a4fb4 --- /dev/null +++ b/dagan/train_dataparallel.py @@ -0,0 +1,122 @@ +from tqdm import trange +import torch + +from torch.utils.data import DataLoader + +from logger import Logger +from modules.model_dataparallel import DiscriminatorFullModel +import modules.model_dataparallel as MODEL +from tqdm import tqdm +from torch.optim.lr_scheduler import MultiStepLR +import pdb +from sync_batchnorm import DataParallelWithCallback +from evaluation_dataset import EvaluationDataset + +from frames_dataset import DatasetRepeater + + +def train(config, generator, discriminator, kp_detector, checkpoint, log_dir, dataset, device_ids, opt, writer): + train_params = config['train_params'] + + optimizer_generator = torch.optim.Adam(generator.parameters(), lr=train_params['lr_generator'], betas=(0.5, 0.999)) + optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=train_params['lr_discriminator'], + betas=(0.5, 0.999)) + optimizer_kp_detector = torch.optim.Adam(kp_detector.parameters(), lr=train_params['lr_kp_detector'], + betas=(0.5, 0.999)) + + if checkpoint is not None: + start_epoch = Logger.load_cpk(checkpoint, generator, discriminator, kp_detector, + optimizer_generator, optimizer_discriminator, + None if train_params['lr_kp_detector'] == 0 else optimizer_kp_detector) + else: + start_epoch = 0 + + scheduler_generator = MultiStepLR(optimizer_generator, train_params['epoch_milestones'], gamma=0.1, + last_epoch=start_epoch - 1) + scheduler_discriminator = MultiStepLR(optimizer_discriminator, train_params['epoch_milestones'], gamma=0.1, + last_epoch=start_epoch - 1) + scheduler_kp_detector = MultiStepLR(optimizer_kp_detector, train_params['epoch_milestones'], gamma=0.1, + last_epoch=-1 + start_epoch * (train_params['lr_kp_detector'] != 0)) + + if 'num_repeats' in train_params or train_params['num_repeats'] != 1: + dataset = DatasetRepeater(dataset, train_params['num_repeats']) + dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, num_workers=13, + drop_last=True) + + generator_full = getattr(MODEL, opt.GFM)(kp_detector, generator, discriminator, train_params, opt) + discriminator_full = DiscriminatorFullModel(kp_detector, generator, discriminator, train_params) + test_dataset = EvaluationDataset(dataroot='/root/autodl-tmp/vox-png', + pairs_list='data/vox_evaluation.csv') + test_dataloader = torch.utils.data.DataLoader( + test_dataset, + batch_size=4, + shuffle=False, + num_workers=13) + if torch.cuda.is_available(): + generator_full = DataParallelWithCallback(generator_full, device_ids=device_ids) + discriminator_full = DataParallelWithCallback(discriminator_full, device_ids=device_ids) + + with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], + checkpoint_freq=train_params['checkpoint_freq']) as logger: + for epoch in trange(start_epoch, train_params['num_epochs']): + # parallel + total = len(dataloader) + epoch_train_loss = 0 + generator.train(), discriminator.train(), kp_detector.train() + with tqdm(total=total) as par: + for i, x in enumerate(dataloader): + # x['source'] = x['source'].to(device) + # x['driving'] = x['driving'].to(device) + losses_generator, generated = generator_full(x) + + loss_values = [val.mean() for val in losses_generator.values()] + loss = sum(loss_values) + loss.backward() + optimizer_generator.step() + optimizer_generator.zero_grad() + optimizer_kp_detector.step() + optimizer_kp_detector.zero_grad() + epoch_train_loss += loss.item() + + if train_params['loss_weights']['generator_gan'] != 0: + optimizer_discriminator.zero_grad() + losses_discriminator = discriminator_full(x, generated) + loss_values = [val.mean() for val in losses_discriminator.values()] + loss = sum(loss_values) + + loss.backward() + optimizer_discriminator.step() + optimizer_discriminator.zero_grad() + else: + losses_discriminator = {} + + losses_generator.update(losses_discriminator) + losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()} + # for k,v in losses.items(): + # writer.add_scalar(k, v, total*epoch+i) + logger.log_iter(losses=losses) + par.update(1) + epoch_train_loss = epoch_train_loss / total + if (epoch + 1) % train_params['checkpoint_freq'] == 0: + writer.add_scalar('epoch_train_loss', epoch_train_loss, epoch) + scheduler_generator.step() + scheduler_discriminator.step() + scheduler_kp_detector.step() + logger.log_epoch(epoch, {'generator': generator, + 'discriminator': discriminator, + 'kp_detector': kp_detector, + 'optimizer_generator': optimizer_generator, + 'optimizer_discriminator': optimizer_discriminator, + 'optimizer_kp_detector': optimizer_kp_detector}, inp=x, out=generated) + generator.eval(), discriminator.eval(), kp_detector.eval() + if (epoch + 1) % train_params['checkpoint_freq'] == 0: + epoch_eval_loss = 0 + for i, data in tqdm(enumerate(test_dataloader)): + data['source'] = data['source'].cuda() + data['driving'] = data['driving'].cuda() + losses_generator, generated = generator_full(data) + loss_values = [val.mean() for val in losses_generator.values()] + loss = sum(loss_values) + epoch_eval_loss += loss.item() + epoch_eval_loss = epoch_eval_loss / len(test_dataloader) + writer.add_scalar('epoch_eval_loss', epoch_eval_loss, epoch) \ No newline at end of file diff --git a/dagan/utils.py b/dagan/utils.py new file mode 100644 index 00000000..1c35e223 --- /dev/null +++ b/dagan/utils.py @@ -0,0 +1,717 @@ +import os +import random +import numpy as np +import csv +import cv2 +import logging +import pdb +from collections import defaultdict +import sys +from tqdm import tqdm +from PIL import Image +import depth +import torch.nn as nn +from skimage.transform import resize +import torchvision.transforms as T +import torch +import matplotlib.pyplot as plt +import matplotlib as mpl +import matplotlib.cm as cm +import math +import imageio +from skimage import io, img_as_float32 +from skimage.color import gray2rgb +from sklearn.model_selection import train_test_split +from imageio import mimread, imsave +import pandas as pd +from scipy.spatial import ConvexHull + +logging.basicConfig( + level=logging.INFO, # 设置日志级别，可选 DEBUG, INFO, WARNING, ERROR, CRITICAL + format='%(asctime)s - %(levelname)s - %(message)s', # 设置日志格式 + handlers=[ + logging.FileHandler("find_best_frame_video.log"), # 将日志写入文件 + logging.StreamHandler() # 同时在控制台输出日志 + ] +) + +logger = logging.getLogger(__name__) + + +def count_test_video(path): + vis = {video[:video.find('#', 8)] for video in + os.listdir(path)} + print(vis) + print(len(vis)) + + +def create_same_id_test_set(path): + vis = os.listdir(path) + videos = np.random.choice(vis, replace=False, size=100) + f = open('./data/vox_evaluation.csv', 'w', encoding='utf-8') + source = [] + driving = [] + csv_writer = csv.writer(f) + csv_writer.writerow(["source", "driving", "frame"]) + for i in range(2083): + v = np.random.choice(videos, replace=False, size=1) + imgs = os.listdir(os.path.join(path, v[0])) + pair = np.random.choice(imgs, replace=False, size=2) + src = os.path.join(path, v[0], pair[0]) + dst = os.path.join(path, v[0], pair[1]) + source.append(src) + driving.append(dst) + sources = np.array(source).reshape(-1, 1) + driving = np.array(driving).reshape(-1, 1) + content = np.concatenate((sources, driving), 1) + csv_writer.writerows(content) + f.close() + + +def create_cross_id_test_set(path): + vis = os.listdir(path) + ids2video = defaultdict(list) + num = len('id10283') + for vi in vis: + ids2video[vi[:num]].append(vi) + ids = list(ids2video.keys()) + videos = np.random.choice(vis, replace=False, size=100) + f = open('./data/vox_cross_id_evaluation.csv', 'w', encoding='utf-8') + source = [] + driving = [] + csv_writer = csv.writer(f) + csv_writer.writerow(["source", "driving", "frame"]) + for i in range(2083): + id = np.random.choice(ids, replace=False, size=1) + vis = np.random.choice(ids2video[id[0]], replace=False, size=1) + imgs = os.listdir(os.path.join(path, vis[0])) + img = np.random.choice(imgs, replace=False, size=1) + src = os.path.join(path, vis[0], img[0]) + + other_id = list(set(ids).difference(set(id))) + id = np.random.choice(other_id, replace=False, size=1) + vis = np.random.choice(ids2video[id[0]], replace=False, size=1) + imgs = os.listdir(os.path.join(path, vis[0])) + img = np.random.choice(imgs, replace=False, size=1) + dst = os.path.join(path, vis[0], img[0]) + + source.append(src) + driving.append(dst) + sources = np.array(source).reshape(-1, 1) + driving = np.array(driving).reshape(-1, 1) + content = np.concatenate((sources, driving), 1) + csv_writer.writerows(content) + f.close() + + +def concate_compared_results(resust_path, cp_path): + imgs = os.listdir(resust_path) + for im in tqdm(imgs): + ours = cv2.imread(os.path.join(resust_path, im)) + fomm = cv2.imread(os.path.join(cp_path, im)) + rst = np.concatenate((ours, fomm), 0).astype(np.uint8) + cv2.imwrite(os.path.join('FID/compare', im), rst) + + +def render(path): + depth_encoder = depth.ResnetEncoder(18, False).cuda() + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + cvimg = cv2.resize(cv2.imread(path), (256, 256)) + img = Image.open(path).convert('RGB').resize((256, 256)) + tensor_img = T.ToTensor()(img).unsqueeze(0).cuda() + outputs = depth_decoder(depth_encoder(tensor_img)) + depth_source = outputs[("disp", 0)][0] + depth_source = depth_source.permute(1, 2, 0).detach().cpu().numpy() + heatmap = depth_source / np.max(depth_source) + heatmap = np.uint8(255 * heatmap) + heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) + superimposed_img1 = heatmap * 0.6 + cvimg + cv2.imwrite('{}.jpg'.format(path), superimposed_img1) + + +def depth_gray(path): + depth_encoder = depth.ResnetEncoder(18, False).cuda() + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + img = Image.open(path).convert('RGB').resize((256, 256)) + tensor_img = T.ToTensor()(img).unsqueeze(0).cuda() + outputs = depth_decoder(depth_encoder(tensor_img)) + depth_source = outputs[("disp", 0)][0] + depth_source = depth_source.permute(1, 2, 0).detach().cpu().numpy() * depth_source.permute(1, 2, + 0).detach().cpu().numpy() + heatmap = 1 - depth_source / np.max(depth_source) + heatmap = np.uint8(255 * heatmap) + cv2.imwrite('heatmap.jpg', heatmap) + + +def depth_rgb(path): + depth_encoder = depth.ResnetEncoder(18, False).cuda() + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + img = Image.open(path).convert('RGB').resize((256, 256)) + tensor_img = T.ToTensor()(img).unsqueeze(0).cuda() + outputs = depth_decoder(depth_encoder(tensor_img)) + disp = outputs[("disp", 0)] + # Saving colormapped depth image + disp_resized = torch.nn.functional.interpolate(disp, (256, 256), mode="bilinear", align_corners=False) + disp_resized_np = disp_resized.squeeze().detach().cpu().numpy() + vmax = np.percentile(disp_resized_np, 95) + normalizer = mpl.colors.Normalize(vmin=disp_resized_np.min(), vmax=vmax) + mapper = cm.ScalarMappable(norm=normalizer, cmap='rainbow') + + colormapped_im = (mapper.to_rgba(disp_resized_np)[:, :, :3] * 255).astype(np.uint8) + plt.axis('off') + plt.imshow(colormapped_im) + + # plt.colorbar(mapper) + plt.savefig(path + '.pdf') + # plt.savefig(path+'.png') + + plt.clf() + + +def process_celeV(path): + train_path = os.path.join(path, 'train') + test_path = os.path.join(path, 'test') + ids = os.listdir(path) + f = open('./data/celeV_cross_id_evaluation.csv', 'w', encoding='utf-8') + + # sample 2000 image sets from each identity + # if not os.path.exists(train_path): + # os.makedirs(train_path) + # if not os.path.exists(test_path): + # os.makedirs(test_path) + source = [] + driving = [] + csv_writer = csv.writer(f) + csv_writer.writerow(["source", "driving", "frame"]) + for i in range(2083): + src_id = np.random.choice(ids, replace=False, size=1) + imgs = os.listdir(os.path.join(path, src_id[0], 'Image')) + src_imgs = np.random.choice(imgs, replace=False, size=1) + src = os.path.join(path, src_id[0], 'Image', src_imgs[0]) + + res_ids = list(set(ids).difference(set(src_id))) + + dst_id = np.random.choice(res_ids, replace=False, size=1) + imgs = os.listdir(os.path.join(path, dst_id[0], 'Image')) + dst_imgs = np.random.choice(imgs, replace=False, size=1) + dst = os.path.join(path, dst_id[0], 'Image', dst_imgs[0]) + source.append(src) + driving.append(dst) + sources = np.array(source).reshape(-1, 1) + driving = np.array(driving).reshape(-1, 1) + content = np.concatenate((sources, driving), 1) + csv_writer.writerows(content) + f.close() + + +def compare(): + x2face = '/data/fhongac/workspace/gitrepo/X2Face/UnwrapMosaic/FID/celebv' + fomm = '/data/fhongac/workspace/gitrepo/first-order-model/FID/celebv' + osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/FID/celebv' + dagan = '/data/fhongac/workspace/src/parallel-fom-rgbd/log/vox-adv-256rgbd_kp_num15_rgbd_attnv2/celebv/concate' + + imgs = os.listdir(x2face) + for i in tqdm(range(len(imgs))): + im = imgs[i] + img_x2face = os.path.join(x2face, im) + img_x2face = cv2.imread(img_x2face) + + img_fomm = os.path.join(fomm, im) + img_fomm = cv2.imread(img_fomm) + + img_osfv = os.path.join(osfv, im) + img_osfv = cv2.imread(img_osfv) + + img_dagan = os.path.join(dagan, im) + img_dagan = cv2.imread(img_dagan) + + img = np.vstack((img_x2face, img_fomm, img_osfv, img_dagan)) + cv2.imwrite('FID/multiMethod/{}.jpg'.format(i), img) + + +def aus(path): + import cv2 + frame = cv2.imread(path) + from feat import Detector + detector = Detector() + # image_prediction = detector.detect_faces(path) + out1 = detector.detect_faces('FID/source/0.jpg') + out1.plot_aus(12, muscles={'all': "heatmap"}, gaze=None) + plt.savefig('a.jpg') + out2 = detector.detect_faces('FID/source/1.jpg') + p1 = out1.facepose.values + p2 = out2.facepose.values + + # landmarks = detector.detect_landmarks(frame, face) + # score = detector.detect_aus(frame,landmarks[0]) + + +def evaluate_PRMSE_AUCON(): + from feat import Detector + detector = Detector() + # x2face = '/data/fhongac/workspace/gitrepo/X2Face/UnwrapMosaic/FID' + # fomm = '/data/fhongac/workspace/gitrepo/first-order-model/FID' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/FID' + # dagan = '/data/fhongac/workspace/src/parallel-fom-rgbd/FID' + # test = dagan + path = sys.argv[1] + imgs = os.listdir(path + '/gt') + PRMSE = 0 + AUCON = 0 + counter = 1e-9 + CSIM = 0 + csim_counter = 1e-9 + ##########################################CSIM############################################################## + from facenet_pytorch import MTCNN, InceptionResnetV1 + + # If required, create a face detection pipeline using MTCNN: + mtcnn = MTCNN(image_size=256, margin=0) + + # Create an inception resnet (in eval mode): + resnet = InceptionResnetV1(pretrained='vggface2').eval() + ##################################################################################################################### + for im in tqdm(imgs): + try: + gt = os.path.join(path, 'gt', im) + gen = os.path.join(path, 'generate_form_self', im) + # gt_aus = detector.detect_aus(gt) + # generate_aus = detector.detect_aus(gen) + + # gt_pose = detector.detect_facepose(gt, detected_faces=None, landmarks=None) + # gt_pose = detector.detect_facepose(gt, detected_faces=None, landmarks=None) + + out_gt = detector.detect_image(gt) + out_generat = detector.detect_image(gen) + gt_pose = out_gt.facepose.values + generate_pose = out_generat.facepose.values + gt_aus = out_gt.aus.values + generate_aus = out_generat.aus.values + row, num = generate_aus.shape + prmse = np.sqrt(np.power(gt_pose - generate_pose, 2).sum() / 3) + if math.isnan(prmse): + print(im) + raise RuntimeError('NaN') + PRMSE += prmse + generate_aus = generate_aus > 0.5 + gt_aus = gt_aus > 0.5 + rst = ~ (generate_aus ^ gt_aus) + correct = rst.sum() + AUCON += (correct / num) + counter += 1 + except Exception as e: + print(e) + try: + source = Image.open(os.path.join(path, 'source', im)) + generate = Image.open(os.path.join(path, 'generate_form_self', im)) + + # Get cropped and prewhitened image tensor + img_cropped = mtcnn(source, save_path='src.jpg') + # img_cropped = T.ToTensor()(source).cuda() + # Calculate embedding (unsqueeze to add batch dimension) + source_emb = resnet(img_cropped.unsqueeze(0)) + + # Get cropped and prewhitened image tensor + img_cropped = mtcnn(generate, save_path='dst.jpg') + # img_cropped = T.ToTensor()(generate).cuda() + # Calculate embedding (unsqueeze to add batch dimension) + generate_emb = resnet(img_cropped.unsqueeze(0)) + CSIM += torch.cosine_similarity(source_emb, generate_emb).item() + csim_counter += 1 + except Exception as e: + print(e) + print(' PRMSE: {}, AUCON : {}, CSIM: {}'.format(PRMSE / counter, AUCON / counter, CSIM / csim_counter)) + + +def mergeimgs(paths): + pth = paths[0] + imgps = os.listdir(pth) + for i in tqdm(range(len(imgps))): + imgp = imgps[i] + cats = [] + for pth in paths: + img = os.path.join(pth, imgp) + img = cv2.imread(img) + cats.append(img) + img = np.vstack(cats) + cv2.imwrite('FID/mergeimgs/{}.jpg'.format(i), img) + + +def create_animate_pair(): + f = open('./data/vox_cross_id_animate.csv', 'w', encoding='utf-8') + csv_writer = csv.writer(f) + csv_writer.writerow(["source_frame", "driving_video"]) + pairs = pd.read_csv('data/vox_cross_id_evaluation.csv') + source = pairs['source'].tolist() + driving = pairs['driving'].tolist() + source_frames = [] + driving_videos = [] + + for src, dst in zip(source, driving): + video = os.path.dirname(dst).replace('vox1_frames', 'vox1') + source_frames.append(src) + driving_videos.append(video) + source_frames = np.array(source_frames).reshape(-1, 1) + driving_videos = np.array(driving_videos).reshape(-1, 1) + content = np.concatenate((source_frames, driving_videos), 1) + csv_writer.writerows(content) + f.close() + + +def merge_abla_imgs(paths): + pth = paths[0] + imgps = os.listdir(pth) + for i in tqdm(range(len(imgps))): + imgp = imgps[i] + cats = [] + for pth in paths: + img = os.path.join(pth, imgp) + img = cv2.imread(img) + cats.append(img) + img = np.vstack(cats) + cv2.imwrite('FID/abla/{}.jpg'.format(i), img) + + +def mergevideos(): + videos_path1 = 'animation' + videos_path2 = '/data/fhongac/workspace/gitrepo/first-order-model/animation' + videos = os.listdir(videos_path1) + save_path = 'merge_animation' + for vi in tqdm(videos): + fomm = np.array(mimread('{}/{}'.format(videos_path2, vi), memtest=False)) + ours = np.array(mimread('{}/{}'.format(videos_path1, vi), memtest=False)) + reader = imageio.get_reader('{}/{}'.format(videos_path2, vi)) + fps = reader.get_meta_data()['fps'] + if len(fomm.shape) == 3: + fomm = np.array([gray2rgb(frame) for frame in fomm]) + if fomm.shape[-1] == 4: + fomm = fomm[..., :3] + if len(ours.shape) == 3: + ours = np.array([gray2rgb(frame) for frame in ours]) + if ours.shape[-1] == 4: + ours = ours[..., :3] + fomm = fomm[:, :, -256:, :] + src_dst = ours[:, :, :512, :] + ours = ours[:, :, -256:, :] + merge = np.concatenate((src_dst, fomm, ours), 2) + imageio.mimsave('{}/{}'.format(save_path, vi), merge, fps=fps) + + +def extractFrames(): + videos_pairs = pd.read_csv('data/vox_cross_id_animate.csv') + source = videos_pairs['source_frame'].tolist() + driving = videos_pairs['driving_video'].tolist() + frame_pairs = pd.read_csv('data/vox_cross_id_evaluation.csv') + # source = videos_pairs['source_frame'].tolist() + driving_frame = frame_pairs['driving'].tolist() + concate = 'FID/video_cross_id' + generate = 'FID/video_generate' + videos = 'animation' + for i, (src, dst, number) in tqdm(enumerate(zip(source, driving, driving_frame))): + video = np.array(mimread('{}/{}.mp4'.format(videos, i), memtest=False)) + if len(video.shape) == 3: + video = np.array([gray2rgb(frame) for frame in video]) + if video.shape[-1] == 4: + video = video[..., :3] + num = int(os.path.basename(number)[:7]) + video_array = img_as_float32(video) + frame = (video_array[num] * 255).astype(np.uint8) + imsave('{}/{}.jpg'.format(concate, i), frame) + imsave('{}/{}.jpg'.format(generate, i), frame[:, -256:, :]) + + +class depth_network(nn.Module): + def __init__(self): + super(depth_network, self).__init__() + self.depth_encoder = depth.ResnetEncoder(18, False).cuda() + self.depth_decoder = depth.DepthDecoder(num_ch_enc=self.depth_encoder.num_ch_enc, scales=range(4)).cuda() + + def forward(self, x): + outputs = self.depth_decoder(self.depth_encoder(x)) + return outputs + + +def viewNetworkStructure(): + network = depth_network().cuda() + print(network) + import hiddenlayer as h + vis_graph = h.build_graph(network, torch.zeros([1, 3, 256, 256]).cuda()) # 获取绘制图像的对象 + vis_graph.theme = h.graph.THEMES["blue"].copy() # 指定主题颜色 + vis_graph.save("network_graph/depth_network.png") # 保存图像的路径 + + +def drawKPline(): + kp10 = [2.292730636, 0.870793269, 0.719648837] + kp15 = [2.335680558, 0.872849592, 0.7229482939818654] + kp20 = [2.268743373, 0.882716346, 0.67557838] + kp25 = [3.395401378, 0.827983638, 0.662669217] + data = np.array([kp10, kp15, kp20, kp25]) + x = [0, 1, 2, 3] + + fig, ax = plt.subplots() + ax.spines['right'].set_visible(False) + ax.spines['top'].set_visible(False) + l1 = plt.plot(x, data[:, 0], 'r--', label='PRMSE') + l2 = plt.plot(x, data[:, 1], 'g--', label='AUCON') + l3 = plt.plot(x, data[:, 2], 'b--', label='CSIM') + + plt.plot(x, data[:, 0], 'ro-', x, data[:, 1], 'g+-', x, data[:, 2], 'b^-') + plt.grid(linestyle=':') + # ax.tick_params(bottom=False) + plt.xticks(x, ["kp=10", "kp=15", "kp=20", "kp=25"]) # 去掉横坐标值 + # plt.yticks([]) #去掉纵坐标值 + # plt.setp(ax.get_xticklabels(), visible=False) + # plt.setp(ax.get_yticklabels(), visible=False) + plt.legend() + plt.savefig('network_graph/kp.pdf') + + +def all_depth(path): + imgs = os.listdir(path + '/gt') + + depth_encoder = depth.ResnetEncoder(18, False).cuda() + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + for im in tqdm(imgs): + driving = os.path.join(path, 'gt', im) + source = os.path.join(path, 'generate', im) + img = Image.open(path).convert('RGB').resize((256, 256)) + tensor_img = T.ToTensor()(img).unsqueeze(0).cuda() + outputs = depth_decoder(depth_encoder(tensor_img)) + disp = outputs[("disp", 0)] + # Saving colormapped depth image + disp_resized = torch.nn.functional.interpolate(disp, (256, 256), mode="bilinear", align_corners=False) + disp_resized_np = disp_resized.squeeze().detach().cpu().numpy() + vmax = np.percentile(disp_resized_np, 95) + normalizer = mpl.colors.Normalize(vmin=disp_resized_np.min(), vmax=vmax) + mapper = cm.ScalarMappable(norm=normalizer, cmap='rainbow') + + colormapped_im = (mapper.to_rgba(disp_resized_np)[:, :, :3] * 255).astype(np.uint8) + plt.axis('off') + plt.imshow(colormapped_im) + + # plt.colorbar(mapper) + plt.savefig(path + '.pdf') + # plt.savefig(path+'.png') + + plt.clf() + + +def changevideos(): + # videos_path1 = 'animation' + # videos_path2 = '/data/fhongac/workspace/gitrepo/first-order-model/animation' + # videos = os.listdir(videos_path1) + # save_path = 'merge_animation' + # for vi in tqdm(videos): + # fomm = np.array(mimread('{}/{}'.format(videos_path2,vi),memtest=False)) + # ours = np.array(mimread('{}/{}'.format(videos_path1,vi),memtest=False)) + # reader = imageio.get_reader('{}/{}'.format(videos_path2,vi)) + # fps = reader.get_meta_data()['fps'] + # if len(fomm.shape) == 3: + # fomm = np.array([gray2rgb(frame) for frame in fomm]) + # if fomm.shape[-1] == 4: + # fomm = fomm[..., :3] + # if len(ours.shape) == 3: + # ours = np.array([gray2rgb(frame) for frame in ours]) + # if ours.shape[-1] == 4: + # ours = ours[..., :3] + # fomm = fomm[:,:,-256:,:] + # src_dst = ours[:,:,:512,:] + # ours = ours[:,:,-256:,:] + # merge = np.concatenate((src_dst,fomm,ours),2) + # imageio.mimsave('{}/{}'.format(save_path,vi), merge, fps=fps) + # 155 + # path = 'merge_animation/155.mp4' + # disp = '/data/fhongac/workspace/src/depthEstimate/7PbDDjXgYzU/id10287#bP0bKbQQlzc#003638#003940_disp.mp4' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/155.mp4' + # save = 'FID/animation/155.mp4' + + # path = 'merge_animation/523.mp4' + # disp = '/data/fhongac/workspace/src/depthEstimate/7PbDDjXgYzU/id10287#4oOmqI1myzY#000381#000729_disp.mp4' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/523.mp4' + # save = 'FID/animation/523.mp4' + + # path = 'merge_animation/705.mp4' + # disp = '/data/fhongac/workspace/src/depthEstimate/705.mp4' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/705.mp4' + # save = 'FID/animation/705.mp4' + + # path = 'merge_animation/2062.mp4' + # disp = '/data/fhongac/workspace/src/depthEstimate/2062.mp4' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/2062.mp4' + # save = 'FID/animation/2062.mp4' + + # path = 'merge_animation/1841.mp4' + # disp = '/data/fhongac/workspace/src/depthEstimate/1841.mp4' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/1841.mp4' + # save = 'FID/animation/1841.mp4' + + path = 'merge_animation/1758.mp4' + disp = '/data/fhongac/workspace/src/depthEstimate/1758.mp4' + osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/1758.mp4' + save = 'FID/animation/1758.mp4' + + video = np.array(mimread('{}'.format(path), memtest=False)) + reader = imageio.get_reader('{}'.format(path)) + fps = reader.get_meta_data()['fps'] + video = np.array([gray2rgb(frame) for frame in video]) + + disp = np.array(mimread('{}'.format(disp), memtest=False)) + disp = np.array([gray2rgb(frame) for frame in disp]) + + osfv = np.array(mimread('{}'.format(osfv), memtest=False)) + osfv = np.array([gray2rgb(frame) for frame in osfv]) + + bz, h, w, c = video.shape + up_video = np.concatenate((video[:, :, :int(w / 2), :], disp), 2) + down_video = np.concatenate((video[:, :, int(w / 2):int(w / 4) * 3, :], osfv, video[:, :, int(w / 4) * 3:, :]), 2) + + up_zeros = np.ones((bz, 20, 256 * 3, 3)) * 255 + mid_zeros = np.ones((bz, 40, 256 * 3, 3)) * 255 + down_zeros = np.ones((bz, 40, 256 * 3, 3)) * 255 + video = np.concatenate((up_zeros, up_video, mid_zeros, down_video, down_zeros), 1) + imageio.mimsave('{}'.format(save), video, fps=fps) + + print('aa') + + +def find_best_frame_video(): + import pandas as pd + from tqdm import tqdm + + logger.info("开始执行 find_best_frame_video 函数") + + try: + pairs = pd.read_csv('./data/vox_cross_id_evaluation.csv') + sources = pairs['source'] + drivings = pairs['driving'] + best_frame = [] + total = len(sources) + logger.info(f"总共有 {total} 对 source 和 driving 需要处理") + except Exception as e: + logger.error(f"读取 CSV 文件时出错: {e}") + return + + for i, (src, dri) in enumerate(tqdm(zip(sources, drivings), total=total, desc="Processing pairs")): + try: + source_image = imageio.imread(src) + source_image = resize(source_image, (256, 256))[..., :3] + vpath = os.path.dirname(dri) + imgs = os.listdir(vpath) + driving_video = [] + for j, im in enumerate(imgs): + image = imageio.imread(os.path.join(vpath, im)) + driving_video.append(resize(image, (256, 256))[..., :3]) + idx = find_best_frame(source_image, driving_video) + bf = os.path.join(vpath, imgs[idx]) + best_frame.append(bf) + if (i + 1) % 100 == 0: + logger.info(f"已处理 {i + 1}/{total} 对 source 和 driving") + except Exception as e: + logger.error(f"处理第 {i + 1} 对 (source: {src}, driving: {dri}) 时出错: {e}") + best_frame.append("Error") + + try: + f = open('./data/vox_cross_id_evaluation_best_frame.csv', 'w', encoding='utf-8') + csv_writer = csv.writer(f) + csv_writer.writerow(["source", "driving", "best_frame"]) + sources = np.array(sources).reshape(-1, 1) + drivings = np.array(drivings).reshape(-1, 1) + best_frame = np.array(best_frame).reshape(-1, 1) + content = np.concatenate((sources, drivings, best_frame), 1) + csv_writer.writerows(content) + f.close() + logger.info("成功写入 vox_cross_id_evaluation_best_frame.csv 文件") + except Exception as e: + logger.error(f"写入 CSV 文件时出错: {e}") + + +def find_best_frame(source, driving, cpu=False): + import face_alignment + + def normalize_kp(kp): + kp = kp - kp.mean(axis=0, keepdims=True) + try: + area = ConvexHull(kp[:, :2]).volume + area = np.sqrt(area) + kp[:, :2] = kp[:, :2] / area + except Exception as e: + logger.warning(f"计算 ConvexHull 时出错: {e}") + area = 1 # 避免除以零 + kp[:, :2] = kp[:, :2] / area + return kp + + try: + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True, + device='cpu' if cpu else 'cuda') + kp_source = fa.get_landmarks(255 * source)[0] + kp_source = normalize_kp(kp_source) + except Exception as e: + logger.error(f"检测源图像关键点时出错: {e}") + return -1 # 返回无效索引 + + norm = float('inf') + frame_num = 0 + for i, image in enumerate(driving): + try: + kp_driving = fa.get_landmarks(255 * image)[0] + kp_driving = normalize_kp(kp_driving) + new_norm = (np.abs(kp_source - kp_driving) ** 2).sum() + if new_norm < norm: + norm = new_norm + frame_num = i + except Exception as e: + logger.warning(f"检测驱动图像第 {i + 1} 帧关键点时出错: {e}") + continue + return frame_num + + +if __name__ == '__main__': + # create_test_set('/data/fhongac/origDataset/vox1_frames/test') + # concate_compared_results('FID/generate','/data/fhongac/workspace/gitrepo/first-order-model/FID/generate') + # concate_compared_results('FID/concate','/data/fhongac/workspace/gitrepo/first-order-model/FID/concate') + # create_cross_id_test_set('/root/autodl-tmp/vox-png/test') + # render('ppt_figure/0000021.jpg') + # depth_rgb('ppt_figure/293.jpg') + # process_celeV('/data/fhongac/origDataset/CelebV') + # compare() + # evaluate_PRMSE_AUCON() # CUDA_VISIBLE_DEVICES=1 python utils.py + # viewNetworkStructure() + # aus('11.png') + # mergeimgs(['/data/fhongac/workspace/gitrepo/first-order-model/checkpoint/vox_cross_id/concate', + # '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/checkpoint/vox_cross_id/concate', + # '/data/fhongac/workspace/src/parallel-fom-rgbd/log/vox-adv-256rgbd_kp_num15_rgbd_attnv2/vox_cross_id/concate']) + # create_animate_pair() + # merge_abla_imgs(['/data/fhongac/workspace/gitrepo/first-order-model/FID/cross_id','log/vox-adv-256baseline/vox_cross_id/concate', 'log/vox-adv-256rgbd_kp_num15/vox_cross_id/concate','log/vox-adv-256rgbd_kp_num15_rgbd_attnv2_wo_D/vox_cross_id/concate','log/vox-adv-256rgbd_kp_num15_rgbd_attnv2/vox_cross_id/concate']) + extractFrames() + # mergevideos() + # drawKPline() + # extractFrames() + # find_best_frame_video() + diff --git a/dagan/utils2.py b/dagan/utils2.py new file mode 100644 index 00000000..a38d5337 --- /dev/null +++ b/dagan/utils2.py @@ -0,0 +1,718 @@ +import os +import random +import numpy as np +import csv +import cv2 +import logging +import pdb +from collections import defaultdict +import sys +from tqdm import tqdm +from PIL import Image +import depth +import torch.nn as nn +from skimage.transform import resize +import torchvision.transforms as T +import torch +import matplotlib.pyplot as plt +import matplotlib as mpl +import matplotlib.cm as cm +import math +import imageio +from skimage import io, img_as_float32 +from skimage.color import gray2rgb +from sklearn.model_selection import train_test_split +from imageio import mimread, imsave +import pandas as pd +from scipy.spatial import ConvexHull + +logging.basicConfig( + level=logging.INFO, # 设置日志级别，可选 DEBUG, INFO, WARNING, ERROR, CRITICAL + format='%(asctime)s - %(levelname)s - %(message)s', # 设置日志格式 + handlers=[ + logging.FileHandler("find_best_frame_video.log"), # 将日志写入文件 + logging.StreamHandler() # 同时在控制台输出日志 + ] +) + +logger = logging.getLogger(__name__) + + +def count_test_video(path): + vis = {video[:video.find('#', 8)] for video in + os.listdir(path)} + print(vis) + print(len(vis)) + + +def create_same_id_test_set(path): + vis = os.listdir(path) + videos = np.random.choice(vis, replace=False, size=100) + f = open('./data/vox_evaluation.csv', 'w', encoding='utf-8') + source = [] + driving = [] + csv_writer = csv.writer(f) + csv_writer.writerow(["source", "driving", "frame"]) + for i in range(2083): + v = np.random.choice(videos, replace=False, size=1) + imgs = os.listdir(os.path.join(path, v[0])) + pair = np.random.choice(imgs, replace=False, size=2) + src = os.path.join(path, v[0], pair[0]) + dst = os.path.join(path, v[0], pair[1]) + source.append(src) + driving.append(dst) + sources = np.array(source).reshape(-1, 1) + driving = np.array(driving).reshape(-1, 1) + content = np.concatenate((sources, driving), 1) + csv_writer.writerows(content) + f.close() + + +def create_cross_id_test_set(path): + vis = os.listdir(path) + ids2video = defaultdict(list) + num = len('id10283') + for vi in vis: + ids2video[vi[:num]].append(vi) + ids = list(ids2video.keys()) + videos = np.random.choice(vis, replace=False, size=100) + f = open('./data/vox_cross_id_evaluation.csv', 'w', encoding='utf-8') + source = [] + driving = [] + csv_writer = csv.writer(f) + csv_writer.writerow(["source", "driving", "frame"]) + for i in range(2083): + id = np.random.choice(ids, replace=False, size=1) + vis = np.random.choice(ids2video[id[0]], replace=False, size=1) + imgs = os.listdir(os.path.join(path, vis[0])) + img = np.random.choice(imgs, replace=False, size=1) + src = os.path.join(path, vis[0], img[0]) + + other_id = list(set(ids).difference(set(id))) + id = np.random.choice(other_id, replace=False, size=1) + vis = np.random.choice(ids2video[id[0]], replace=False, size=1) + imgs = os.listdir(os.path.join(path, vis[0])) + img = np.random.choice(imgs, replace=False, size=1) + dst = os.path.join(path, vis[0], img[0]) + + source.append(src) + driving.append(dst) + sources = np.array(source).reshape(-1, 1) + driving = np.array(driving).reshape(-1, 1) + content = np.concatenate((sources, driving), 1) + csv_writer.writerows(content) + f.close() + + +def concate_compared_results(resust_path, cp_path): + imgs = os.listdir(resust_path) + for im in tqdm(imgs): + ours = cv2.imread(os.path.join(resust_path, im)) + fomm = cv2.imread(os.path.join(cp_path, im)) + rst = np.concatenate((ours, fomm), 0).astype(np.uint8) + cv2.imwrite(os.path.join('FID/compare', im), rst) + + +def render(path): + depth_encoder = depth.ResnetEncoder(18, False).cuda() + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + cvimg = cv2.resize(cv2.imread(path), (256, 256)) + img = Image.open(path).convert('RGB').resize((256, 256)) + tensor_img = T.ToTensor()(img).unsqueeze(0).cuda() + outputs = depth_decoder(depth_encoder(tensor_img)) + depth_source = outputs[("disp", 0)][0] + depth_source = depth_source.permute(1, 2, 0).detach().cpu().numpy() + heatmap = depth_source / np.max(depth_source) + heatmap = np.uint8(255 * heatmap) + heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) + superimposed_img1 = heatmap * 0.6 + cvimg + cv2.imwrite('{}.jpg'.format(path), superimposed_img1) + + +def depth_gray(path): + depth_encoder = depth.ResnetEncoder(18, False).cuda() + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + img = Image.open(path).convert('RGB').resize((256, 256)) + tensor_img = T.ToTensor()(img).unsqueeze(0).cuda() + outputs = depth_decoder(depth_encoder(tensor_img)) + depth_source = outputs[("disp", 0)][0] + depth_source = depth_source.permute(1, 2, 0).detach().cpu().numpy() * depth_source.permute(1, 2, + 0).detach().cpu().numpy() + heatmap = 1 - depth_source / np.max(depth_source) + heatmap = np.uint8(255 * heatmap) + cv2.imwrite('heatmap.jpg', heatmap) + + +def depth_rgb(path): + depth_encoder = depth.ResnetEncoder(18, False).cuda() + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + img = Image.open(path).convert('RGB').resize((256, 256)) + tensor_img = T.ToTensor()(img).unsqueeze(0).cuda() + outputs = depth_decoder(depth_encoder(tensor_img)) + disp = outputs[("disp", 0)] + # Saving colormapped depth image + disp_resized = torch.nn.functional.interpolate(disp, (256, 256), mode="bilinear", align_corners=False) + disp_resized_np = disp_resized.squeeze().detach().cpu().numpy() + vmax = np.percentile(disp_resized_np, 95) + normalizer = mpl.colors.Normalize(vmin=disp_resized_np.min(), vmax=vmax) + mapper = cm.ScalarMappable(norm=normalizer, cmap='rainbow') + + colormapped_im = (mapper.to_rgba(disp_resized_np)[:, :, :3] * 255).astype(np.uint8) + plt.axis('off') + plt.imshow(colormapped_im) + + # plt.colorbar(mapper) + plt.savefig(path + '.pdf') + # plt.savefig(path+'.png') + + plt.clf() + + +def process_celeV(path): + train_path = os.path.join(path, 'train') + test_path = os.path.join(path, 'test') + ids = os.listdir(path) + f = open('./data/celeV_cross_id_evaluation.csv', 'w', encoding='utf-8') + + # sample 2000 image sets from each identity + # if not os.path.exists(train_path): + # os.makedirs(train_path) + # if not os.path.exists(test_path): + # os.makedirs(test_path) + source = [] + driving = [] + csv_writer = csv.writer(f) + csv_writer.writerow(["source", "driving", "frame"]) + for i in range(2083): + src_id = np.random.choice(ids, replace=False, size=1) + imgs = os.listdir(os.path.join(path, src_id[0], 'Image')) + src_imgs = np.random.choice(imgs, replace=False, size=1) + src = os.path.join(path, src_id[0], 'Image', src_imgs[0]) + + res_ids = list(set(ids).difference(set(src_id))) + + dst_id = np.random.choice(res_ids, replace=False, size=1) + imgs = os.listdir(os.path.join(path, dst_id[0], 'Image')) + dst_imgs = np.random.choice(imgs, replace=False, size=1) + dst = os.path.join(path, dst_id[0], 'Image', dst_imgs[0]) + source.append(src) + driving.append(dst) + sources = np.array(source).reshape(-1, 1) + driving = np.array(driving).reshape(-1, 1) + content = np.concatenate((sources, driving), 1) + csv_writer.writerows(content) + f.close() + + +def compare(): + x2face = '/data/fhongac/workspace/gitrepo/X2Face/UnwrapMosaic/FID/celebv' + fomm = '/data/fhongac/workspace/gitrepo/first-order-model/FID/celebv' + osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/FID/celebv' + dagan = '/data/fhongac/workspace/src/parallel-fom-rgbd/log/vox-adv-256rgbd_kp_num15_rgbd_attnv2/celebv/concate' + + imgs = os.listdir(x2face) + for i in tqdm(range(len(imgs))): + im = imgs[i] + img_x2face = os.path.join(x2face, im) + img_x2face = cv2.imread(img_x2face) + + img_fomm = os.path.join(fomm, im) + img_fomm = cv2.imread(img_fomm) + + img_osfv = os.path.join(osfv, im) + img_osfv = cv2.imread(img_osfv) + + img_dagan = os.path.join(dagan, im) + img_dagan = cv2.imread(img_dagan) + + img = np.vstack((img_x2face, img_fomm, img_osfv, img_dagan)) + cv2.imwrite('FID/multiMethod/{}.jpg'.format(i), img) + + +def aus(path): + import cv2 + frame = cv2.imread(path) + from feat import Detector + detector = Detector() + # image_prediction = detector.detect_faces(path) + out1 = detector.detect_faces('FID/source/0.jpg') + out1.plot_aus(12, muscles={'all': "heatmap"}, gaze=None) + plt.savefig('a.jpg') + out2 = detector.detect_faces('FID/source/1.jpg') + p1 = out1.facepose.values + p2 = out2.facepose.values + + # landmarks = detector.detect_landmarks(frame, face) + # score = detector.detect_aus(frame,landmarks[0]) + + +def evaluate_PRMSE_AUCON(): + from feat import Detector + detector = Detector() + # x2face = '/data/fhongac/workspace/gitrepo/X2Face/UnwrapMosaic/FID' + # fomm = '/data/fhongac/workspace/gitrepo/first-order-model/FID' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/FID' + # dagan = '/data/fhongac/workspace/src/parallel-fom-rgbd/FID' + # test = dagan + path = sys.argv[1] + imgs = os.listdir(path + '/gt') + PRMSE = 0 + AUCON = 0 + counter = 1e-9 + CSIM = 0 + csim_counter = 1e-9 + ##########################################CSIM############################################################## + from facenet_pytorch import MTCNN, InceptionResnetV1 + + # If required, create a face detection pipeline using MTCNN: + mtcnn = MTCNN(image_size=256, margin=0) + + # Create an inception resnet (in eval mode): + resnet = InceptionResnetV1(pretrained='vggface2').eval() + ##################################################################################################################### + for im in tqdm(imgs): + try: + gt = os.path.join(path, 'gt', im) + gen = os.path.join(path, 'generate_364_self', im) + # gt_aus = detector.detect_aus(gt) + # generate_aus = detector.detect_aus(gen) + + # gt_pose = detector.detect_facepose(gt, detected_faces=None, landmarks=None) + # gt_pose = detector.detect_facepose(gt, detected_faces=None, landmarks=None) + + out_gt = detector.detect_image(gt) + out_generat = detector.detect_image(gen) + gt_pose = out_gt.facepose.values + generate_pose = out_generat.facepose.values + gt_aus = out_gt.aus.values + generate_aus = out_generat.aus.values + row, num = generate_aus.shape + prmse = np.sqrt(np.power(gt_pose - generate_pose, 2).sum() / 3) + if math.isnan(prmse): + print(im) + raise RuntimeError('NaN') + PRMSE += prmse + generate_aus = generate_aus > 0.5 + gt_aus = gt_aus > 0.5 + rst = ~ (generate_aus ^ gt_aus) + correct = rst.sum() + AUCON += (correct / num) + counter += 1 + except Exception as e: + print(e) + try: + source = Image.open(os.path.join(path, 'source', im)) + generate = Image.open(os.path.join(path, 'generate_364_self', im)) + + # Get cropped and prewhitened image tensor + img_cropped = mtcnn(source, save_path='src.jpg') + # img_cropped = T.ToTensor()(source).cuda() + # Calculate embedding (unsqueeze to add batch dimension) + source_emb = resnet(img_cropped.unsqueeze(0)) + + # Get cropped and prewhitened image tensor + img_cropped = mtcnn(generate, save_path='dst.jpg') + # img_cropped = T.ToTensor()(generate).cuda() + # Calculate embedding (unsqueeze to add batch dimension) + generate_emb = resnet(img_cropped.unsqueeze(0)) + CSIM += torch.cosine_similarity(source_emb, generate_emb).item() + csim_counter += 1 + except Exception as e: + print(e) + print(' PRMSE: {}, AUCON : {}, CSIM: {}'.format(PRMSE / counter, AUCON / counter, CSIM / csim_counter)) + + +def mergeimgs(paths): + pth = paths[0] + imgps = os.listdir(pth) + for i in tqdm(range(len(imgps))): + imgp = imgps[i] + cats = [] + for pth in paths: + img = os.path.join(pth, imgp) + img = cv2.imread(img) + cats.append(img) + img = np.vstack(cats) + cv2.imwrite('FID/mergeimgs/{}.jpg'.format(i), img) + + +def create_animate_pair(): + f = open('./data/vox_cross_id_animate.csv', 'w', encoding='utf-8') + csv_writer = csv.writer(f) + csv_writer.writerow(["source_frame", "driving_video"]) + pairs = pd.read_csv('data/vox_cross_id_evaluation.csv') + source = pairs['source'].tolist() + driving = pairs['driving'].tolist() + source_frames = [] + driving_videos = [] + + for src, dst in zip(source, driving): + video = os.path.dirname(dst).replace('vox1_frames', 'vox1') + source_frames.append(src) + driving_videos.append(video) + source_frames = np.array(source_frames).reshape(-1, 1) + driving_videos = np.array(driving_videos).reshape(-1, 1) + content = np.concatenate((source_frames, driving_videos), 1) + csv_writer.writerows(content) + f.close() + + +def merge_abla_imgs(paths): + pth = paths[0] + imgps = os.listdir(pth) + for i in tqdm(range(len(imgps))): + imgp = imgps[i] + cats = [] + for pth in paths: + img = os.path.join(pth, imgp) + img = cv2.imread(img) + cats.append(img) + img = np.vstack(cats) + cv2.imwrite('FID/abla/{}.jpg'.format(i), img) + + +def mergevideos(): + videos_path1 = 'animation' + videos_path2 = '/data/fhongac/workspace/gitrepo/first-order-model/animation' + videos = os.listdir(videos_path1) + save_path = 'merge_animation' + for vi in tqdm(videos): + fomm = np.array(mimread('{}/{}'.format(videos_path2, vi), memtest=False)) + ours = np.array(mimread('{}/{}'.format(videos_path1, vi), memtest=False)) + reader = imageio.get_reader('{}/{}'.format(videos_path2, vi)) + fps = reader.get_meta_data()['fps'] + if len(fomm.shape) == 3: + fomm = np.array([gray2rgb(frame) for frame in fomm]) + if fomm.shape[-1] == 4: + fomm = fomm[..., :3] + if len(ours.shape) == 3: + ours = np.array([gray2rgb(frame) for frame in ours]) + if ours.shape[-1] == 4: + ours = ours[..., :3] + fomm = fomm[:, :, -256:, :] + src_dst = ours[:, :, :512, :] + ours = ours[:, :, -256:, :] + merge = np.concatenate((src_dst, fomm, ours), 2) + imageio.mimsave('{}/{}'.format(save_path, vi), merge, fps=fps) + + +def extractFrames(): + videos_pairs = pd.read_csv('data/vox_cross_id_animate.csv') + source = videos_pairs['source_frame'].tolist() + driving = videos_pairs['driving_video'].tolist() + frame_pairs = pd.read_csv('data/vox_cross_id_evaluation.csv') + # source = videos_pairs['source_frame'].tolist() + driving_frame = frame_pairs['driving'].tolist() + concate = 'FID/video_cross_id' + generate = 'FID/video_generate' + videos = 'animation' + for i, (src, dst, number) in tqdm(enumerate(zip(source, driving, driving_frame))): + video = np.array(mimread('{}/{}.mp4'.format(videos, i), memtest=False)) + if len(video.shape) == 3: + video = np.array([gray2rgb(frame) for frame in video]) + if video.shape[-1] == 4: + video = video[..., :3] + num = int(os.path.basename(number)[:7]) + video_array = img_as_float32(video) + frame = (video_array[num] * 255).astype(np.uint8) + imsave('{}/{}.jpg'.format(concate, i), frame) + imsave('{}/{}.jpg'.format(generate, i), frame[:, -256:, :]) + + +class depth_network(nn.Module): + def __init__(self): + super(depth_network, self).__init__() + self.depth_encoder = depth.ResnetEncoder(18, False).cuda() + self.depth_decoder = depth.DepthDecoder(num_ch_enc=self.depth_encoder.num_ch_enc, scales=range(4)).cuda() + + def forward(self, x): + outputs = self.depth_decoder(self.depth_encoder(x)) + return outputs + + +def viewNetworkStructure(): + network = depth_network().cuda() + print(network) + import hiddenlayer as h + vis_graph = h.build_graph(network, torch.zeros([1, 3, 256, 256]).cuda()) # 获取绘制图像的对象 + vis_graph.theme = h.graph.THEMES["blue"].copy() # 指定主题颜色 + vis_graph.save("network_graph/depth_network.png") # 保存图像的路径 + + +def drawKPline(): + os.makedirs('network_graph', exist_ok=True) + kp10 = [2.292730636, 0.870793269, 0.719648837] + kp15 = [2.335680558, 0.872849592, 0.7229482939818654] + kp20 = [2.268743373, 0.882716346, 0.67557838] + kp25 = [3.395401378, 0.827983638, 0.662669217] + data = np.array([kp10, kp15, kp20, kp25]) + x = [0, 1, 2, 3] + + fig, ax = plt.subplots() + ax.spines['right'].set_visible(False) + ax.spines['top'].set_visible(False) + l1 = plt.plot(x, data[:, 0], 'r--', label='PRMSE') + l2 = plt.plot(x, data[:, 1], 'g--', label='AUCON') + l3 = plt.plot(x, data[:, 2], 'b--', label='CSIM') + + plt.plot(x, data[:, 0], 'ro-', x, data[:, 1], 'g+-', x, data[:, 2], 'b^-') + plt.grid(linestyle=':') + # ax.tick_params(bottom=False) + plt.xticks(x, ["kp=10", "kp=15", "kp=20", "kp=25"]) # 去掉横坐标值 + # plt.yticks([]) #去掉纵坐标值 + # plt.setp(ax.get_xticklabels(), visible=False) + # plt.setp(ax.get_yticklabels(), visible=False) + plt.legend() + plt.savefig('network_graph/kp.pdf') + + +def all_depth(path): + imgs = os.listdir(path + '/gt') + + depth_encoder = depth.ResnetEncoder(18, False).cuda() + depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda() + loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth') + loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth') + filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} + depth_encoder.load_state_dict(filtered_dict_enc) + depth_decoder.load_state_dict(loaded_dict_dec) + depth_encoder.eval() + depth_decoder.eval() + for im in tqdm(imgs): + driving = os.path.join(path, 'gt', im) + source = os.path.join(path, 'generate', im) + img = Image.open(path).convert('RGB').resize((256, 256)) + tensor_img = T.ToTensor()(img).unsqueeze(0).cuda() + outputs = depth_decoder(depth_encoder(tensor_img)) + disp = outputs[("disp", 0)] + # Saving colormapped depth image + disp_resized = torch.nn.functional.interpolate(disp, (256, 256), mode="bilinear", align_corners=False) + disp_resized_np = disp_resized.squeeze().detach().cpu().numpy() + vmax = np.percentile(disp_resized_np, 95) + normalizer = mpl.colors.Normalize(vmin=disp_resized_np.min(), vmax=vmax) + mapper = cm.ScalarMappable(norm=normalizer, cmap='rainbow') + + colormapped_im = (mapper.to_rgba(disp_resized_np)[:, :, :3] * 255).astype(np.uint8) + plt.axis('off') + plt.imshow(colormapped_im) + + # plt.colorbar(mapper) + plt.savefig(path + '.pdf') + # plt.savefig(path+'.png') + + plt.clf() + + +def changevideos(): + # videos_path1 = 'animation' + # videos_path2 = '/data/fhongac/workspace/gitrepo/first-order-model/animation' + # videos = os.listdir(videos_path1) + # save_path = 'merge_animation' + # for vi in tqdm(videos): + # fomm = np.array(mimread('{}/{}'.format(videos_path2,vi),memtest=False)) + # ours = np.array(mimread('{}/{}'.format(videos_path1,vi),memtest=False)) + # reader = imageio.get_reader('{}/{}'.format(videos_path2,vi)) + # fps = reader.get_meta_data()['fps'] + # if len(fomm.shape) == 3: + # fomm = np.array([gray2rgb(frame) for frame in fomm]) + # if fomm.shape[-1] == 4: + # fomm = fomm[..., :3] + # if len(ours.shape) == 3: + # ours = np.array([gray2rgb(frame) for frame in ours]) + # if ours.shape[-1] == 4: + # ours = ours[..., :3] + # fomm = fomm[:,:,-256:,:] + # src_dst = ours[:,:,:512,:] + # ours = ours[:,:,-256:,:] + # merge = np.concatenate((src_dst,fomm,ours),2) + # imageio.mimsave('{}/{}'.format(save_path,vi), merge, fps=fps) + # 155 + # path = 'merge_animation/155.mp4' + # disp = '/data/fhongac/workspace/src/depthEstimate/7PbDDjXgYzU/id10287#bP0bKbQQlzc#003638#003940_disp.mp4' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/155.mp4' + # save = 'FID/animation/155.mp4' + + # path = 'merge_animation/523.mp4' + # disp = '/data/fhongac/workspace/src/depthEstimate/7PbDDjXgYzU/id10287#4oOmqI1myzY#000381#000729_disp.mp4' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/523.mp4' + # save = 'FID/animation/523.mp4' + + # path = 'merge_animation/705.mp4' + # disp = '/data/fhongac/workspace/src/depthEstimate/705.mp4' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/705.mp4' + # save = 'FID/animation/705.mp4' + + # path = 'merge_animation/2062.mp4' + # disp = '/data/fhongac/workspace/src/depthEstimate/2062.mp4' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/2062.mp4' + # save = 'FID/animation/2062.mp4' + + # path = 'merge_animation/1841.mp4' + # disp = '/data/fhongac/workspace/src/depthEstimate/1841.mp4' + # osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/1841.mp4' + # save = 'FID/animation/1841.mp4' + + path = 'merge_animation/1758.mp4' + disp = '/data/fhongac/workspace/src/depthEstimate/1758.mp4' + osfv = '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/1758.mp4' + save = 'FID/animation/1758.mp4' + + video = np.array(mimread('{}'.format(path), memtest=False)) + reader = imageio.get_reader('{}'.format(path)) + fps = reader.get_meta_data()['fps'] + video = np.array([gray2rgb(frame) for frame in video]) + + disp = np.array(mimread('{}'.format(disp), memtest=False)) + disp = np.array([gray2rgb(frame) for frame in disp]) + + osfv = np.array(mimread('{}'.format(osfv), memtest=False)) + osfv = np.array([gray2rgb(frame) for frame in osfv]) + + bz, h, w, c = video.shape + up_video = np.concatenate((video[:, :, :int(w / 2), :], disp), 2) + down_video = np.concatenate((video[:, :, int(w / 2):int(w / 4) * 3, :], osfv, video[:, :, int(w / 4) * 3:, :]), 2) + + up_zeros = np.ones((bz, 20, 256 * 3, 3)) * 255 + mid_zeros = np.ones((bz, 40, 256 * 3, 3)) * 255 + down_zeros = np.ones((bz, 40, 256 * 3, 3)) * 255 + video = np.concatenate((up_zeros, up_video, mid_zeros, down_video, down_zeros), 1) + imageio.mimsave('{}'.format(save), video, fps=fps) + + print('aa') + + +def find_best_frame_video(): + import pandas as pd + from tqdm import tqdm + + logger.info("开始执行 find_best_frame_video 函数") + + try: + pairs = pd.read_csv('./data/vox_cross_id_evaluation.csv') + sources = pairs['source'] + drivings = pairs['driving'] + best_frame = [] + total = len(sources) + logger.info(f"总共有 {total} 对 source 和 driving 需要处理") + except Exception as e: + logger.error(f"读取 CSV 文件时出错: {e}") + return + + for i, (src, dri) in enumerate(tqdm(zip(sources, drivings), total=total, desc="Processing pairs")): + try: + source_image = imageio.imread(src) + source_image = resize(source_image, (256, 256))[..., :3] + vpath = os.path.dirname(dri) + imgs = os.listdir(vpath) + driving_video = [] + for j, im in enumerate(imgs): + image = imageio.imread(os.path.join(vpath, im)) + driving_video.append(resize(image, (256, 256))[..., :3]) + idx = find_best_frame(source_image, driving_video) + bf = os.path.join(vpath, imgs[idx]) + best_frame.append(bf) + if (i + 1) % 100 == 0: + logger.info(f"已处理 {i + 1}/{total} 对 source 和 driving") + except Exception as e: + logger.error(f"处理第 {i + 1} 对 (source: {src}, driving: {dri}) 时出错: {e}") + best_frame.append("Error") + + try: + f = open('./data/vox_cross_id_evaluation_best_frame.csv', 'w', encoding='utf-8') + csv_writer = csv.writer(f) + csv_writer.writerow(["source", "driving", "best_frame"]) + sources = np.array(sources).reshape(-1, 1) + drivings = np.array(drivings).reshape(-1, 1) + best_frame = np.array(best_frame).reshape(-1, 1) + content = np.concatenate((sources, drivings, best_frame), 1) + csv_writer.writerows(content) + f.close() + logger.info("成功写入 vox_cross_id_evaluation_best_frame.csv 文件") + except Exception as e: + logger.error(f"写入 CSV 文件时出错: {e}") + + +def find_best_frame(source, driving, cpu=False): + import face_alignment + + def normalize_kp(kp): + kp = kp - kp.mean(axis=0, keepdims=True) + try: + area = ConvexHull(kp[:, :2]).volume + area = np.sqrt(area) + kp[:, :2] = kp[:, :2] / area + except Exception as e: + logger.warning(f"计算 ConvexHull 时出错: {e}") + area = 1 # 避免除以零 + kp[:, :2] = kp[:, :2] / area + return kp + + try: + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True, + device='cpu' if cpu else 'cuda') + kp_source = fa.get_landmarks(255 * source)[0] + kp_source = normalize_kp(kp_source) + except Exception as e: + logger.error(f"检测源图像关键点时出错: {e}") + return -1 # 返回无效索引 + + norm = float('inf') + frame_num = 0 + for i, image in enumerate(driving): + try: + kp_driving = fa.get_landmarks(255 * image)[0] + kp_driving = normalize_kp(kp_driving) + new_norm = (np.abs(kp_source - kp_driving) ** 2).sum() + if new_norm < norm: + norm = new_norm + frame_num = i + except Exception as e: + logger.warning(f"检测驱动图像第 {i + 1} 帧关键点时出错: {e}") + continue + return frame_num + + +if __name__ == '__main__': + # create_test_set('/data/fhongac/origDataset/vox1_frames/test') + # concate_compared_results('FID/generate','/data/fhongac/workspace/gitrepo/first-order-model/FID/generate') + # concate_compared_results('FID/concate','/data/fhongac/workspace/gitrepo/first-order-model/FID/concate') + # create_cross_id_test_set('/root/autodl-tmp/vox-png/test') + # render('ppt_figure/0000021.jpg') + # depth_rgb('ppt_figure/293.jpg') + # process_celeV('/data/fhongac/origDataset/CelebV') + # compare() + evaluate_PRMSE_AUCON() # CUDA_VISIBLE_DEVICES=1 python utils.py + # viewNetworkStructure() + # aus('11.png') + # mergeimgs(['/data/fhongac/workspace/gitrepo/first-order-model/checkpoint/vox_cross_id/concate', + # '/data/fhongac/workspace/gitrepo/One-Shot_Free-View_Neural_Talking_Head_Synthesis/checkpoint/vox_cross_id/concate', + # '/data/fhongac/workspace/src/parallel-fom-rgbd/log/vox-adv-256rgbd_kp_num15_rgbd_attnv2/vox_cross_id/concate']) + # create_animate_pair() + # merge_abla_imgs(['/data/fhongac/workspace/gitrepo/first-order-model/FID/cross_id','log/vox-adv-256baseline/vox_cross_id/concate', 'log/vox-adv-256rgbd_kp_num15/vox_cross_id/concate','log/vox-adv-256rgbd_kp_num15_rgbd_attnv2_wo_D/vox_cross_id/concate','log/vox-adv-256rgbd_kp_num15_rgbd_attnv2/vox_cross_id/concate']) + # extractFrames() + # mergevideos() + # drawKPline() + # changevideos() + # find_best_frame_video() + diff --git a/dagan/vox256.py b/dagan/vox256.py new file mode 100644 index 00000000..dd9bf6f9 --- /dev/null +++ b/dagan/vox256.py @@ -0,0 +1,74 @@ +import os +import csv +import random +from pathlib import Path +import numpy as np + + +def create_vox_csv(dataset_path, output_file='vox256.csv', num_pairs=8000): + """ + 为 PNG 格式的数据集生成配置文件 + + 参数: + dataset_path: 数据集根目录路径（包含 PNG 文件） + output_file: 输出的 CSV 文件路径 + num_pairs: 需要生成的配对数量 + """ + # 获取当前工作目录 + current_dir = os.getcwd() + output_path = os.path.join(current_dir, output_file) + + # 获取所有 PNG 文件 + png_files = [] + if os.path.exists(dataset_path): + for file in os.listdir(dataset_path): + if file.lower().endswith('.png'): + full_path = os.path.join(dataset_path, file) + png_files.append(full_path) + + print(f"找到 {len(png_files)} 个 PNG 文件") + + if len(png_files) < 2: + raise ValueError(f"在 {dataset_path} 中找到的 PNG 文件数量不足，至少需要2个文件") + + # 写入 CSV 文件 + with open(output_path, 'w', newline='', encoding='utf-8') as f: + writer = csv.writer(f) + writer.writerow(['source', 'driving', 'frame']) # 写入标题行 + + pairs_created = 0 + max_attempts = num_pairs * 2 + attempts = 0 + + while pairs_created < num_pairs and attempts < max_attempts: + attempts += 1 + try: + # 随机选择两个不同的 PNG 文件作为源和驱动 + source_file, driving_file = random.sample(png_files, 2) + + # 获取相对路径（如果需要） + source_relative = os.path.relpath(source_file, current_dir) + driving_relative = os.path.relpath(driving_file, current_dir) + + # 生成随机帧号（这里使用0，因为每个文件都是单独的帧） + frame_number = 0 + + # 写入 CSV + writer.writerow([source_relative, driving_relative, frame_number]) + pairs_created += 1 + + if pairs_created % 1000 == 0: + print(f"已生成 {pairs_created} 个配对...") + + except Exception as e: + print(f"生成配对时发生错误: {str(e)}") + continue + + print(f"配置文件已生成: {output_path}") + print(f"共生成 {pairs_created} 个配对") + + +if __name__ == "__main__": + # 设置数据集路径 + dataset_path = "/root/autodl-tmp/dataset/vox-png" + create_vox_csv(dataset_path) \ No newline at end of file