From 17d1774b032fd503bfe53c57d295db719f9e3da1 Mon Sep 17 00:00:00 2001 From: Peter Lin Date: Mon, 30 Aug 2021 14:01:39 -0700 Subject: [PATCH] Code release --- LICENSE | 674 ++++ README.md | 225 ++ README_zh_Hans.md | 224 ++ dataset/augmentation.py | 260 ++ dataset/coco.py | 103 + dataset/imagematte.py | 98 + dataset/spd.py | 27 + dataset/videomatte.py | 125 + dataset/youtubevis.py | 123 + documentation/image/showreel.gif | Bin 0 -> 1011847 bytes documentation/image/teaser.gif | Bin 0 -> 3501589 bytes documentation/inference.md | 352 ++ documentation/inference_zh_Hans.md | 353 ++ documentation/misc/aim_test.txt | 11 + documentation/misc/d646_test.txt | 11 + .../misc/dvm_background_test_clips.txt | 162 + .../misc/dvm_background_train_clips.txt | 3117 +++++++++++++++++ documentation/misc/imagematte_train.txt | 420 +++ documentation/misc/imagematte_valid.txt | 15 + documentation/misc/spd_preprocess.py | 45 + documentation/training.md | 158 + evaluation/evaluate_hr.py | 216 ++ evaluation/evaluate_lr.py | 254 ++ ...nerate_imagematte_with_background_image.py | 146 + ...nerate_imagematte_with_background_video.py | 189 + ...nerate_videomatte_with_background_image.py | 78 + ...nerate_videomatte_with_background_video.py | 118 + hubconf.py | 39 + inference.py | 208 ++ inference_speed_test.py | 50 + inference_utils.py | 88 + model/__init__.py | 1 + model/decoder.py | 210 ++ model/deep_guided_filter.py | 61 + model/fast_guided_filter.py | 76 + model/lraspp.py | 29 + model/mobilenetv3.py | 72 + model/model.py | 79 + model/resnet.py | 45 + requirements_inference.txt | 5 + requirements_training.txt | 5 + train.py | 504 +++ train_config.py | 68 + train_loss.py | 101 + 44 files changed, 9145 insertions(+) create mode 100644 LICENSE create mode 100644 README.md create mode 100644 README_zh_Hans.md create mode 100644 dataset/augmentation.py create mode 100644 dataset/coco.py create mode 100644 dataset/imagematte.py create mode 100644 dataset/spd.py create mode 100644 dataset/videomatte.py create mode 100644 dataset/youtubevis.py create mode 100644 documentation/image/showreel.gif create mode 100644 documentation/image/teaser.gif create mode 100644 documentation/inference.md create mode 100644 documentation/inference_zh_Hans.md create mode 100644 documentation/misc/aim_test.txt create mode 100644 documentation/misc/d646_test.txt create mode 100644 documentation/misc/dvm_background_test_clips.txt create mode 100644 documentation/misc/dvm_background_train_clips.txt create mode 100644 documentation/misc/imagematte_train.txt create mode 100644 documentation/misc/imagematte_valid.txt create mode 100644 documentation/misc/spd_preprocess.py create mode 100644 documentation/training.md create mode 100644 evaluation/evaluate_hr.py create mode 100644 evaluation/evaluate_lr.py create mode 100644 evaluation/generate_imagematte_with_background_image.py create mode 100644 evaluation/generate_imagematte_with_background_video.py create mode 100644 evaluation/generate_videomatte_with_background_image.py create mode 100644 evaluation/generate_videomatte_with_background_video.py create mode 100644 hubconf.py create mode 100644 inference.py create mode 100644 inference_speed_test.py create mode 100644 inference_utils.py create mode 100644 model/__init__.py create mode 100644 model/decoder.py create mode 100644 model/deep_guided_filter.py create mode 100644 model/fast_guided_filter.py create mode 100644 model/lraspp.py create mode 100644 model/mobilenetv3.py create mode 100644 model/model.py create mode 100644 model/resnet.py create mode 100644 requirements_inference.txt create mode 100644 requirements_training.txt create mode 100644 train.py create mode 100644 train_config.py create mode 100644 train_loss.py diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..e72bfdd --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..31d7ab7 --- /dev/null +++ b/README.md @@ -0,0 +1,225 @@ +# Robust Video Matting (RVM) + +![Teaser](/documentation/image/teaser.gif) + +

English | 中文

+ +Official repository for the paper [Robust High-Resolution Video Matting with Temporal Guidance](https://peterl1n.github.io/RobustVideoMatting/). RVM is specifically designed for robust human video matting. Unlike existing neural models that process frames as independent images, RVM uses a recurrent neural network to process videos with temporal memory. RVM can perform matting in real-time on any videos without additional inputs. It achieves **4K 76FPS** and **HD 104FPS** on an Nvidia GTX 1080 Ti GPU. The project was developed at [ByteDance Inc.](https://www.bytedance.com/) + +
+ +## News + +* [Aug 25 2021] Source code and pretrained models are published. +* [Jul 27 2021] Paper is accepted by WACV 2022. + +
+ +## Showreel +Watch the showreel video ([YouTube](https://youtu.be/Jvzltozpbpk), [Bilibili](https://www.bilibili.com/video/BV1Z3411B7g7/)) to see the model's performance. + +

+ + + +

+ +All footage in the video are available in [Google Drive](https://drive.google.com/drive/folders/1VFnWwuu-YXDKG-N6vcjK_nL7YZMFapMU?usp=sharing) and [Baidu Pan](https://pan.baidu.com/s/1igMteDwN5rO1Sn7YIhBlvQ) (code: tb3w). + +
+ + +## Demo +* [Webcam Demo](https://peterl1n.github.io/RobustVideoMatting/#/demo): Run the model live in your browser. Visualize recurrent states. +* [Colab Demo](https://colab.research.google.com/drive/10z-pNKRnVNsp0Lq9tH1J_XPZ7CBC_uHm?usp=sharing): Test our model on your own videos with free GPU. + +
+ +## Download + +We recommend MobileNetv3 models for most use cases. ResNet50 models are the larger variant with small performance improvements. Our model is available on various inference frameworks. See [inference documentation](documentation/inference.md) for more instructions. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FrameworkDownloadNotes
PyTorch + rvm_mobilenetv3.pth
+ rvm_resnet50.pth +
+ Official weights for PyTorch. Doc +
TorchHub + Nothing to Download. + + Easiest way to use our model in your PyTorch project. Doc +
TorchScript + rvm_mobilenetv3_fp32.torchscript
+ rvm_mobilenetv3_fp16.torchscript
+ rvm_resnet50_fp32.torchscript
+ rvm_resnet50_fp16.torchscript +
+ If inference on mobile, consider export int8 quantized models yourself. Doc +
ONNX + rvm_mobilenetv3_fp32.onnx
+ rvm_mobilenetv3_fp16.onnx
+ rvm_resnet50_fp32.onnx
+ rvm_resnet50_fp16.onnx +
+ Tested on ONNX Runtime with CPU and CUDA backends. Provided models use opset 12. Doc, Exporter. +
TensorFlow + rvm_mobilenetv3_tf.zip
+ rvm_resnet50_tf.zip +
+ TensorFlow 2 SavedModel. Doc +
TensorFlow.js + rvm_mobilenetv3_tfjs_int8.zip
+
+ Run the model on the web. Demo, Starter Code +
CoreML + rvm_mobilenetv3_1280x720_s0.375_fp16.mlmodel
+ rvm_mobilenetv3_1280x720_s0.375_int8.mlmodel
+ rvm_mobilenetv3_1920x1080_s0.25_fp16.mlmodel
+ rvm_mobilenetv3_1920x1080_s0.25_int8.mlmodel
+
+ CoreML does not support dynamic resolution. Other resolutions can be exported yourself. Models require iOS 13+. s denotes downsample_ratio. Doc, Exporter +
+ +All models are available in [Google Drive](https://drive.google.com/drive/folders/1pBsG-SCTatv-95SnEuxmnvvlRx208VKj?usp=sharing) and [Baidu Pan](https://pan.baidu.com/s/1puPSxQqgBFOVpW4W7AolkA) (code: gym7). + +
+ +## PyTorch Example + +1. Install dependencies: +```sh +pip install -r requirements_inference.txt +``` + +2. Load the model: + +```python +import torch +from model import MattingNetwork + +model = MattingNetwork('mobilenetv3').eval().cuda() # or "resnet50" +model.load_state_dict(torch.load('rvm_mobilenetv3.pth')) +``` + +3. To convert videos, we provide a simple conversion API: + +```python +from inference import convert_video + +convert_video( + model, # The model, can be on any device (cpu or cuda). + input_source='input.mp4', # A video file or an image sequence directory. + output_type='video', # Choose "video" or "png_sequence" + output_composition='output.mp4', # File path if video; directory path if png sequence. + output_video_mbps=4, # Output video mbps. Not needed for png sequence. + downsample_ratio=None, # A hyperparameter to adjust or use None for auto. + seq_chunk=12, # Process n frames at once for better parallelism. +) +``` + +4. Or write your own inference code: +```python +from torch.utils.data import DataLoader +from torchvision.transforms import ToTensor +from inference_utils import VideoReader, VideoWriter + +reader = VideoReader('input.mp4', transform=ToTensor()) +writer = VideoWriter('output.mp4', frame_rate=30) + +bgr = torch.tensor([.47, 1, .6]).view(3, 1, 1).cuda() # Green background. +rec = [None] * 4 # Initial recurrent states. +downsample_ratio = 0.25 # Adjust based on your video. + +with torch.no_grad(): + for src in DataLoader(reader): # RGB tensor normalized to 0 ~ 1. + fgr, pha, *rec = model(src.cuda(), *rec, downsample_ratio) # Cycle the recurrent states. + com = fgr * pha + bgr * (1 - pha) # Composite to green background. + writer.write(com) # Write frame. +``` + +5. The models and converter API are also available through TorchHub. + +```python +# Load the model. +model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") # or "resnet50" + +# Converter API. +convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter") +``` + +Please see [inference documentation](documentation/inference.md) for details on `downsample_ratio` hyperparameter, more converter arguments, and more advanced usage. + +
+ +## Training and Evaluation + +Please refer to the [training documentation](documentation/training.md) to train and evaluate your own model. + +
+ +## Speed + +Speed is measured with `inference_speed_test.py` for reference. + +| GPU | dType | HD (1920x1080) | 4K (3840x2160) | +| -------------- | ----- | -------------- |----------------| +| RTX 3090 | FP16 | 172 FPS | 154 FPS | +| RTX 2060 Super | FP16 | 134 FPS | 108 FPS | +| GTX 1080 Ti | FP32 | 104 FPS | 74 FPS | + +* Note 1: HD uses `downsample_ratio=0.25`, 4K uses `downsample_ratio=0.125`. All tests use batch size 1 and frame chunk 1. +* Note 2: GPUs before Turing architecture does not support FP16 inference, so GTX 1080 Ti uses FP32. +* Note 3: We only measure tensor throughput. The provided video conversion script in this repo is expected to be much slower, because it does not utilize hardware video encoding/decoding and does not have the tensor transfer done on parallel threads. If you are interested in implementing hardware video encoding/decoding in Python, please refer to [PyNvCodec](https://github.com/NVIDIA/VideoProcessingFramework). + +
+ +## Project Members +* [Shanchuan Lin](https://www.linkedin.com/in/shanchuanlin/) +* [Linjie Yang](https://sites.google.com/site/linjieyang89/) +* [Imran Saleemi](https://www.linkedin.com/in/imran-saleemi/) +* [Soumyadip Sengupta](https://homes.cs.washington.edu/~soumya91/) diff --git a/README_zh_Hans.md b/README_zh_Hans.md new file mode 100644 index 0000000..5cdfd72 --- /dev/null +++ b/README_zh_Hans.md @@ -0,0 +1,224 @@ +# 稳定视频抠像 (RVM) + +![Teaser](/documentation/image/teaser.gif) + +

English | 中文

+ +论文 [Robust High-Resolution Video Matting with Temporal Guidance](https://peterl1n.github.io/RobustVideoMatting/) 的官方 GitHub 库。RVM 专为稳定人物视频抠像设计。不同于现有神经网络将每一帧作为单独图片处理,RVM 使用循环神经网络,在处理视频流时有时间记忆。RVM 可在任意视频上做实时高清抠像。在 Nvidia GTX 1080Ti 上实现 **4K 76FPS** 和 **HD 104FPS**。此研究项目来自[字节跳动](https://www.bytedance.com/)。 + +
+ +## 更新 + +* [2021年8月25日] 公开代码和模型。 +* [2021年7月27日] 论文被 WACV 2022 收录。 + +
+ +## 展示视频 +观看展示视频 ([YouTube](https://youtu.be/Jvzltozpbpk), [Bilibili](https://www.bilibili.com/video/BV1Z3411B7g7/)),了解模型能力。 +

+ + + +

+ +视频中的所有素材都提供下载,可用于测试模型:[Google Drive](https://drive.google.com/drive/folders/1VFnWwuu-YXDKG-N6vcjK_nL7YZMFapMU?usp=sharing) 或[百度网盘](https://pan.baidu.com/s/1igMteDwN5rO1Sn7YIhBlvQ)(密码: tb3w) + +
+ + +## Demo +* [网页](https://peterl1n.github.io/RobustVideoMatting/#/demo): 在浏览器里看摄像头抠像效果,展示模型内部循环记忆值。 +* [Colab](https://colab.research.google.com/drive/10z-pNKRnVNsp0Lq9tH1J_XPZ7CBC_uHm?usp=sharing): 用我们的模型转换你的视频。 + +
+ +## 下载 + +推荐在通常情况下使用 MobileNetV3 的模型。ResNet50 的模型大很多,效果稍有提高。我们的模型支持很多框架。详情请阅读[推断文档](documentation/inference_zh_Hans.md)。 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
框架下载备注
PyTorch + rvm_mobilenetv3.pth
+ rvm_resnet50.pth +
+ 官方 PyTorch 模型权值。文档 +
TorchHub + 无需手动下载。 + + 更方便地在你的 PyTorch 项目里使用此模型。文档 +
TorchScript + rvm_mobilenetv3_fp32.torchscript
+ rvm_mobilenetv3_fp16.torchscript
+ rvm_resnet50_fp32.torchscript
+ rvm_resnet50_fp16.torchscript +
+ 若需在移动端推断,可以考虑自行导出 int8 量化的模型。文档 +
ONNX + rvm_mobilenetv3_fp32.onnx
+ rvm_mobilenetv3_fp16.onnx
+ rvm_resnet50_fp32.onnx
+ rvm_resnet50_fp16.onnx +
+ 在 ONNX Runtime 的 CPU 和 CUDA backend 上测试过。提供的模型用 opset 12。文档导出 +
TensorFlow + rvm_mobilenetv3_tf.zip
+ rvm_resnet50_tf.zip +
+ TensorFlow 2 SavedModel 格式。文档 +
TensorFlow.js + rvm_mobilenetv3_tfjs_int8.zip
+
+ 在网页上跑模型。展示示范代码 +
CoreML + rvm_mobilenetv3_1280x720_s0.375_fp16.mlmodel
+ rvm_mobilenetv3_1280x720_s0.375_int8.mlmodel
+ rvm_mobilenetv3_1920x1080_s0.25_fp16.mlmodel
+ rvm_mobilenetv3_1920x1080_s0.25_int8.mlmodel
+
+ CoreML 只能导出固定分辨率,其他分辨率可自行导出。支持 iOS 13+。s 代表下采样比。文档导出 +
+ +所有模型可在 [Google Drive](https://drive.google.com/drive/folders/1pBsG-SCTatv-95SnEuxmnvvlRx208VKj?usp=sharing) 或[百度网盘](https://pan.baidu.com/s/1puPSxQqgBFOVpW4W7AolkA)(密码: gym7)上下载。 + +
+ +## PyTorch 范例 + +1. 安装 Python 库: +```sh +pip install -r requirements_inference.txt +``` + +2. 加载模型: + +```python +import torch +from model import MattingNetwork + +model = MattingNetwork('mobilenetv3').eval().cuda() # 或 "resnet50" +model.load_state_dict(torch.load('rvm_mobilenetv3.pth')) +``` + +3. 若只需要做视频抠像处理,我们提供简单的 API: + +```python +from inference import convert_video + +convert_video( + model, # 模型,可以加载到任何设备(cpu 或 cuda) + input_source='input.mp4', # 视频文件,或图片序列文件夹 + output_type='video', # 可选 "video"(视频)或 "png_sequence"(PNG 序列) + output_composition='output.mp4', # 若导出视频,提供文件路径。若导出 PNG 序列,提供文件夹路径 + output_video_mbps=4, # 若导出视频,提供视频码率 + downsample_ratio=None, # 下采样比,可根据具体视频调节,或 None 选择自动 + seq_chunk=12, # 设置多帧并行计算 +) +``` + +4. 或自己写推断逻辑: +```python +from torch.utils.data import DataLoader +from torchvision.transforms import ToTensor +from inference_utils import VideoReader, VideoWriter + +reader = VideoReader('input.mp4', transform=ToTensor()) +writer = VideoWriter('output.mp4', frame_rate=30) + +bgr = torch.tensor([.47, 1, .6]).view(3, 1, 1).cuda() # 绿背景 +rec = [None] * 4 # 初始循环记忆(Recurrent States) +downsample_ratio = 0.25 # 下采样比,根据视频调节 + +with torch.no_grad(): + for src in DataLoader(reader): # 输入张量,RGB通道,范围为 0~1 + fgr, pha, *rec = model(src.cuda(), *rec, downsample_ratio) # 将上一帧的记忆给下一帧 + com = fgr * pha + bgr * (1 - pha) # 将前景合成到绿色背景 + writer.write(com) # 输出帧 +``` + +5. 模型和 API 也可通过 TorchHub 快速载入。 + +```python +# 加载模型 +model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") # 或 "resnet50" + +# 转换 API +convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter") +``` + +[推断文档](documentation/inference_zh_Hans.md)里有对 `downsample_ratio` 参数,API 使用,和高阶使用的讲解。 + +
+ +## 训练和评估 + +请参照[训练文档(英文)](documentation/training.md)。 + +
+ +## 速度 + +速度用 `inference_speed_test.py` 测量以供参考。 + +| GPU | dType | HD (1920x1080) | 4K (3840x2160) | +| -------------- | ----- | -------------- |----------------| +| RTX 3090 | FP16 | 172 FPS | 154 FPS | +| RTX 2060 Super | FP16 | 134 FPS | 108 FPS | +| GTX 1080 Ti | FP32 | 104 FPS | 74 FPS | + +* 注释1:HD 使用 `downsample_ratio=0.25`,4K 使用 `downsample_ratio=0.125`。 所有测试都使用 batch size 1 和 frame chunk 1。 +* 注释2:图灵架构之前的 GPU 不支持 FP16 推理,所以 GTX 1080 Ti 使用 FP32。 +* 注释3:我们只测量张量吞吐量(tensor throughput)。 提供的视频转换脚本会慢得多,因为它不使用硬件视频编码/解码,也没有在并行线程上完成张量传输。如果您有兴趣在 Python 中实现硬件视频编码/解码,请参考 [PyNvCodec](https://github.com/NVIDIA/VideoProcessingFramework)。 + +
+ +## 项目成员 +* [Shanchuan Lin](https://www.linkedin.com/in/shanchuanlin/) +* [Linjie Yang](https://sites.google.com/site/linjieyang89/) +* [Imran Saleemi](https://www.linkedin.com/in/imran-saleemi/) +* [Soumyadip Sengupta](https://homes.cs.washington.edu/~soumya91/) diff --git a/dataset/augmentation.py b/dataset/augmentation.py new file mode 100644 index 0000000..4a12869 --- /dev/null +++ b/dataset/augmentation.py @@ -0,0 +1,260 @@ +import easing_functions as ef +import random +import torch +from torchvision import transforms +from torchvision.transforms import functional as F + + +class MotionAugmentation: + def __init__(self, + size, + prob_fgr_affine, + prob_bgr_affine, + prob_noise, + prob_color_jitter, + prob_grayscale, + prob_sharpness, + prob_blur, + prob_hflip, + prob_pause, + static_affine=True, + aspect_ratio_range=(0.9, 1.1)): + self.size = size + self.prob_fgr_affine = prob_fgr_affine + self.prob_bgr_affine = prob_bgr_affine + self.prob_noise = prob_noise + self.prob_color_jitter = prob_color_jitter + self.prob_grayscale = prob_grayscale + self.prob_sharpness = prob_sharpness + self.prob_blur = prob_blur + self.prob_hflip = prob_hflip + self.prob_pause = prob_pause + self.static_affine = static_affine + self.aspect_ratio_range = aspect_ratio_range + + def __call__(self, fgrs, phas, bgrs): + # Foreground affine + if random.random() < self.prob_fgr_affine: + fgrs, phas = self._motion_affine(fgrs, phas) + + # Background affine + if random.random() < self.prob_bgr_affine / 2: + bgrs = self._motion_affine(bgrs) + if random.random() < self.prob_bgr_affine / 2: + fgrs, phas, bgrs = self._motion_affine(fgrs, phas, bgrs) + + # Still Affine + if self.static_affine: + fgrs, phas = self._static_affine(fgrs, phas, scale_ranges=(0.5, 1)) + bgrs = self._static_affine(bgrs, scale_ranges=(1, 1.5)) + + # To tensor + fgrs = torch.stack([F.to_tensor(fgr) for fgr in fgrs]) + phas = torch.stack([F.to_tensor(pha) for pha in phas]) + bgrs = torch.stack([F.to_tensor(bgr) for bgr in bgrs]) + + # Resize + params = transforms.RandomResizedCrop.get_params(fgrs, scale=(1, 1), ratio=self.aspect_ratio_range) + fgrs = F.resized_crop(fgrs, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + phas = F.resized_crop(phas, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + params = transforms.RandomResizedCrop.get_params(bgrs, scale=(1, 1), ratio=self.aspect_ratio_range) + bgrs = F.resized_crop(bgrs, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + + # Horizontal flip + if random.random() < self.prob_hflip: + fgrs = F.hflip(fgrs) + phas = F.hflip(phas) + if random.random() < self.prob_hflip: + bgrs = F.hflip(bgrs) + + # Noise + if random.random() < self.prob_noise: + fgrs, bgrs = self._motion_noise(fgrs, bgrs) + + # Color jitter + if random.random() < self.prob_color_jitter: + fgrs = self._motion_color_jitter(fgrs) + if random.random() < self.prob_color_jitter: + bgrs = self._motion_color_jitter(bgrs) + + # Grayscale + if random.random() < self.prob_grayscale: + fgrs = F.rgb_to_grayscale(fgrs, num_output_channels=3).contiguous() + bgrs = F.rgb_to_grayscale(bgrs, num_output_channels=3).contiguous() + + # Sharpen + if random.random() < self.prob_sharpness: + sharpness = random.random() * 8 + fgrs = F.adjust_sharpness(fgrs, sharpness) + phas = F.adjust_sharpness(phas, sharpness) + bgrs = F.adjust_sharpness(bgrs, sharpness) + + # Blur + if random.random() < self.prob_blur / 3: + fgrs, phas = self._motion_blur(fgrs, phas) + if random.random() < self.prob_blur / 3: + bgrs = self._motion_blur(bgrs) + if random.random() < self.prob_blur / 3: + fgrs, phas, bgrs = self._motion_blur(fgrs, phas, bgrs) + + # Pause + if random.random() < self.prob_pause: + fgrs, phas, bgrs = self._motion_pause(fgrs, phas, bgrs) + + return fgrs, phas, bgrs + + def _static_affine(self, *imgs, scale_ranges): + params = transforms.RandomAffine.get_params( + degrees=(-10, 10), translate=(0.1, 0.1), scale_ranges=scale_ranges, + shears=(-5, 5), img_size=imgs[0][0].size) + imgs = [[F.affine(t, *params, F.InterpolationMode.BILINEAR) for t in img] for img in imgs] + return imgs if len(imgs) > 1 else imgs[0] + + def _motion_affine(self, *imgs): + config = dict(degrees=(-10, 10), translate=(0.1, 0.1), + scale_ranges=(0.9, 1.1), shears=(-5, 5), img_size=imgs[0][0].size) + angleA, (transXA, transYA), scaleA, (shearXA, shearYA) = transforms.RandomAffine.get_params(**config) + angleB, (transXB, transYB), scaleB, (shearXB, shearYB) = transforms.RandomAffine.get_params(**config) + + T = len(imgs[0]) + easing = random_easing_fn() + for t in range(T): + percentage = easing(t / (T - 1)) + angle = lerp(angleA, angleB, percentage) + transX = lerp(transXA, transXB, percentage) + transY = lerp(transYA, transYB, percentage) + scale = lerp(scaleA, scaleB, percentage) + shearX = lerp(shearXA, shearXB, percentage) + shearY = lerp(shearYA, shearYB, percentage) + for img in imgs: + img[t] = F.affine(img[t], angle, (transX, transY), scale, (shearX, shearY), F.InterpolationMode.BILINEAR) + return imgs if len(imgs) > 1 else imgs[0] + + def _motion_noise(self, *imgs): + grain_size = random.random() * 3 + 1 # range 1 ~ 4 + monochrome = random.random() < 0.5 + for img in imgs: + T, C, H, W = img.shape + noise = torch.randn((T, 1 if monochrome else C, round(H / grain_size), round(W / grain_size))) + noise.mul_(random.random() * 0.2 / grain_size) + if grain_size != 1: + noise = F.resize(noise, (H, W)) + img.add_(noise).clamp_(0, 1) + return imgs if len(imgs) > 1 else imgs[0] + + def _motion_color_jitter(self, *imgs): + brightnessA, brightnessB, contrastA, contrastB, saturationA, saturationB, hueA, hueB \ + = torch.randn(8).mul(0.1).tolist() + strength = random.random() * 0.2 + easing = random_easing_fn() + T = len(imgs[0]) + for t in range(T): + percentage = easing(t / (T - 1)) * strength + for img in imgs: + img[t] = F.adjust_brightness(img[t], max(1 + lerp(brightnessA, brightnessB, percentage), 0.1)) + img[t] = F.adjust_contrast(img[t], max(1 + lerp(contrastA, contrastB, percentage), 0.1)) + img[t] = F.adjust_saturation(img[t], max(1 + lerp(brightnessA, brightnessB, percentage), 0.1)) + img[t] = F.adjust_hue(img[t], min(0.5, max(-0.5, lerp(hueA, hueB, percentage) * 0.1))) + return imgs if len(imgs) > 1 else imgs[0] + + def _motion_blur(self, *imgs): + blurA = random.random() * 10 + blurB = random.random() * 10 + + T = len(imgs[0]) + easing = random_easing_fn() + for t in range(T): + percentage = easing(t / (T - 1)) + blur = max(lerp(blurA, blurB, percentage), 0) + if blur != 0: + kernel_size = int(blur * 2) + if kernel_size % 2 == 0: + kernel_size += 1 # Make kernel_size odd + for img in imgs: + img[t] = F.gaussian_blur(img[t], kernel_size, sigma=blur) + + return imgs if len(imgs) > 1 else imgs[0] + + def _motion_pause(self, *imgs): + T = len(imgs[0]) + pause_frame = random.choice(range(T - 1)) + pause_length = random.choice(range(T - pause_frame)) + for img in imgs: + img[pause_frame + 1 : pause_frame + pause_length] = img[pause_frame] + return imgs if len(imgs) > 1 else imgs[0] + + +def lerp(a, b, percentage): + return a * (1 - percentage) + b * percentage + + +def random_easing_fn(): + if random.random() < 0.2: + return ef.LinearInOut() + else: + return random.choice([ + ef.BackEaseIn, + ef.BackEaseOut, + ef.BackEaseInOut, + ef.BounceEaseIn, + ef.BounceEaseOut, + ef.BounceEaseInOut, + ef.CircularEaseIn, + ef.CircularEaseOut, + ef.CircularEaseInOut, + ef.CubicEaseIn, + ef.CubicEaseOut, + ef.CubicEaseInOut, + ef.ExponentialEaseIn, + ef.ExponentialEaseOut, + ef.ExponentialEaseInOut, + ef.ElasticEaseIn, + ef.ElasticEaseOut, + ef.ElasticEaseInOut, + ef.QuadEaseIn, + ef.QuadEaseOut, + ef.QuadEaseInOut, + ef.QuarticEaseIn, + ef.QuarticEaseOut, + ef.QuarticEaseInOut, + ef.QuinticEaseIn, + ef.QuinticEaseOut, + ef.QuinticEaseInOut, + ef.SineEaseIn, + ef.SineEaseOut, + ef.SineEaseInOut, + Step, + ])() + +class Step: # Custom easing function for sudden change. + def __call__(self, value): + return 0 if value < 0.5 else 1 + + +# ---------------------------- Frame Sampler ---------------------------- + + +class TrainFrameSampler: + def __init__(self, speed=[0.5, 1, 2, 3, 4, 5]): + self.speed = speed + + def __call__(self, seq_length): + frames = list(range(seq_length)) + + # Speed up + speed = random.choice(self.speed) + frames = [int(f * speed) for f in frames] + + # Shift + shift = random.choice(range(seq_length)) + frames = [f + shift for f in frames] + + # Reverse + if random.random() < 0.5: + frames = frames[::-1] + + return frames + +class ValidFrameSampler: + def __call__(self, seq_length): + return range(seq_length) diff --git a/dataset/coco.py b/dataset/coco.py new file mode 100644 index 0000000..3c183cb --- /dev/null +++ b/dataset/coco.py @@ -0,0 +1,103 @@ +import os +import numpy as np +import random +import json +import os +from torch.utils.data import Dataset +from torchvision import transforms +from torchvision.transforms import functional as F +from PIL import Image + + +class CocoPanopticDataset(Dataset): + def __init__(self, + imgdir: str, + anndir: str, + annfile: str, + transform=None): + with open(annfile) as f: + self.data = json.load(f)['annotations'] + self.data = list(filter(lambda data: any(info['category_id'] == 1 for info in data['segments_info']), self.data)) + self.imgdir = imgdir + self.anndir = anndir + self.transform = transform + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + data = self.data[idx] + img = self._load_img(data) + seg = self._load_seg(data) + + if self.transform is not None: + img, seg = self.transform(img, seg) + + return img, seg + + def _load_img(self, data): + with Image.open(os.path.join(self.imgdir, data['file_name'].replace('.png', '.jpg'))) as img: + return img.convert('RGB') + + def _load_seg(self, data): + with Image.open(os.path.join(self.anndir, data['file_name'])) as ann: + ann.load() + + ann = np.array(ann, copy=False).astype(np.int32) + ann = ann[:, :, 0] + 256 * ann[:, :, 1] + 256 * 256 * ann[:, :, 2] + seg = np.zeros(ann.shape, np.uint8) + + for segments_info in data['segments_info']: + if segments_info['category_id'] in [1, 27, 32]: # person, backpack, tie + seg[ann == segments_info['id']] = 255 + + return Image.fromarray(seg) + + +class CocoPanopticTrainAugmentation: + def __init__(self, size): + self.size = size + self.jitter = transforms.ColorJitter(0.1, 0.1, 0.1, 0.1) + + def __call__(self, img, seg): + # Affine + params = transforms.RandomAffine.get_params(degrees=(-20, 20), translate=(0.1, 0.1), + scale_ranges=(1, 1), shears=(-10, 10), img_size=img.size) + img = F.affine(img, *params, interpolation=F.InterpolationMode.BILINEAR) + seg = F.affine(seg, *params, interpolation=F.InterpolationMode.NEAREST) + + # Resize + params = transforms.RandomResizedCrop.get_params(img, scale=(0.5, 1), ratio=(0.7, 1.3)) + img = F.resized_crop(img, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + seg = F.resized_crop(seg, *params, self.size, interpolation=F.InterpolationMode.NEAREST) + + # Horizontal flip + if random.random() < 0.5: + img = F.hflip(img) + seg = F.hflip(seg) + + # Color jitter + img = self.jitter(img) + + # To tensor + img = F.to_tensor(img) + seg = F.to_tensor(seg) + + return img, seg + + +class CocoPanopticValidAugmentation: + def __init__(self, size): + self.size = size + + def __call__(self, img, seg): + # Resize + params = transforms.RandomResizedCrop.get_params(img, scale=(1, 1), ratio=(1., 1.)) + img = F.resized_crop(img, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + seg = F.resized_crop(seg, *params, self.size, interpolation=F.InterpolationMode.NEAREST) + + # To tensor + img = F.to_tensor(img) + seg = F.to_tensor(seg) + + return img, seg \ No newline at end of file diff --git a/dataset/imagematte.py b/dataset/imagematte.py new file mode 100644 index 0000000..124faca --- /dev/null +++ b/dataset/imagematte.py @@ -0,0 +1,98 @@ +import os +import random +from torch.utils.data import Dataset +from PIL import Image + +from .augmentation import MotionAugmentation + + +class ImageMatteDataset(Dataset): + def __init__(self, + imagematte_dir, + background_image_dir, + background_video_dir, + size, + seq_length, + seq_sampler, + transform): + self.imagematte_dir = imagematte_dir + self.imagematte_files = os.listdir(os.path.join(imagematte_dir, 'fgr')) + self.background_image_dir = background_image_dir + self.background_image_files = os.listdir(background_image_dir) + self.background_video_dir = background_video_dir + self.background_video_clips = os.listdir(background_video_dir) + self.background_video_frames = [sorted(os.listdir(os.path.join(background_video_dir, clip))) + for clip in self.background_video_clips] + self.seq_length = seq_length + self.seq_sampler = seq_sampler + self.size = size + self.transform = transform + + def __len__(self): + return max(len(self.imagematte_files), len(self.background_image_files) + len(self.background_video_clips)) + + def __getitem__(self, idx): + if random.random() < 0.5: + bgrs = self._get_random_image_background() + else: + bgrs = self._get_random_video_background() + + fgrs, phas = self._get_imagematte(idx) + + if self.transform is not None: + return self.transform(fgrs, phas, bgrs) + + return fgrs, phas, bgrs + + def _get_imagematte(self, idx): + with Image.open(os.path.join(self.imagematte_dir, 'fgr', self.imagematte_files[idx % len(self.imagematte_files)])) as fgr, \ + Image.open(os.path.join(self.imagematte_dir, 'pha', self.imagematte_files[idx % len(self.imagematte_files)])) as pha: + fgr = self._downsample_if_needed(fgr.convert('RGB')) + pha = self._downsample_if_needed(pha.convert('L')) + fgrs = [fgr] * self.seq_length + phas = [pha] * self.seq_length + return fgrs, phas + + def _get_random_image_background(self): + with Image.open(os.path.join(self.background_image_dir, self.background_image_files[random.choice(range(len(self.background_image_files)))])) as bgr: + bgr = self._downsample_if_needed(bgr.convert('RGB')) + bgrs = [bgr] * self.seq_length + return bgrs + + def _get_random_video_background(self): + clip_idx = random.choice(range(len(self.background_video_clips))) + frame_count = len(self.background_video_frames[clip_idx]) + frame_idx = random.choice(range(max(1, frame_count - self.seq_length))) + clip = self.background_video_clips[clip_idx] + bgrs = [] + for i in self.seq_sampler(self.seq_length): + frame_idx_t = frame_idx + i + frame = self.background_video_frames[clip_idx][frame_idx_t % frame_count] + with Image.open(os.path.join(self.background_video_dir, clip, frame)) as bgr: + bgr = self._downsample_if_needed(bgr.convert('RGB')) + bgrs.append(bgr) + return bgrs + + def _downsample_if_needed(self, img): + w, h = img.size + if min(w, h) > self.size: + scale = self.size / min(w, h) + w = int(scale * w) + h = int(scale * h) + img = img.resize((w, h)) + return img + +class ImageMatteAugmentation(MotionAugmentation): + def __init__(self, size): + super().__init__( + size=size, + prob_fgr_affine=0.95, + prob_bgr_affine=0.3, + prob_noise=0.05, + prob_color_jitter=0.3, + prob_grayscale=0.03, + prob_sharpness=0.05, + prob_blur=0.02, + prob_hflip=0.5, + prob_pause=0.03, + ) diff --git a/dataset/spd.py b/dataset/spd.py new file mode 100644 index 0000000..7f76b82 --- /dev/null +++ b/dataset/spd.py @@ -0,0 +1,27 @@ +import os +from torch.utils.data import Dataset +from PIL import Image + + +class SuperviselyPersonDataset(Dataset): + def __init__(self, imgdir, segdir, transform=None): + self.img_dir = imgdir + self.img_files = sorted(os.listdir(imgdir)) + self.seg_dir = segdir + self.seg_files = sorted(os.listdir(segdir)) + assert len(self.img_files) == len(self.seg_files) + self.transform = transform + + def __len__(self): + return len(self.img_files) + + def __getitem__(self, idx): + with Image.open(os.path.join(self.img_dir, self.img_files[idx])) as img, \ + Image.open(os.path.join(self.seg_dir, self.seg_files[idx])) as seg: + img = img.convert('RGB') + seg = seg.convert('L') + + if self.transform is not None: + img, seg = self.transform(img, seg) + + return img, seg diff --git a/dataset/videomatte.py b/dataset/videomatte.py new file mode 100644 index 0000000..555911b --- /dev/null +++ b/dataset/videomatte.py @@ -0,0 +1,125 @@ +import os +import random +from torch.utils.data import Dataset +from PIL import Image + +from .augmentation import MotionAugmentation + + +class VideoMatteDataset(Dataset): + def __init__(self, + videomatte_dir, + background_image_dir, + background_video_dir, + size, + seq_length, + seq_sampler, + transform=None): + self.background_image_dir = background_image_dir + self.background_image_files = os.listdir(background_image_dir) + self.background_video_dir = background_video_dir + self.background_video_clips = sorted(os.listdir(background_video_dir)) + self.background_video_frames = [sorted(os.listdir(os.path.join(background_video_dir, clip))) + for clip in self.background_video_clips] + + self.videomatte_dir = videomatte_dir + self.videomatte_clips = sorted(os.listdir(os.path.join(videomatte_dir, 'fgr'))) + self.videomatte_frames = [sorted(os.listdir(os.path.join(videomatte_dir, 'fgr', clip))) + for clip in self.videomatte_clips] + self.videomatte_idx = [(clip_idx, frame_idx) + for clip_idx in range(len(self.videomatte_clips)) + for frame_idx in range(0, len(self.videomatte_frames[clip_idx]), seq_length)] + self.size = size + self.seq_length = seq_length + self.seq_sampler = seq_sampler + self.transform = transform + + def __len__(self): + return len(self.videomatte_idx) + + def __getitem__(self, idx): + if random.random() < 0.5: + bgrs = self._get_random_image_background() + else: + bgrs = self._get_random_video_background() + + fgrs, phas = self._get_videomatte(idx) + + if self.transform is not None: + return self.transform(fgrs, phas, bgrs) + + return fgrs, phas, bgrs + + def _get_random_image_background(self): + with Image.open(os.path.join(self.background_image_dir, random.choice(self.background_image_files))) as bgr: + bgr = self._downsample_if_needed(bgr.convert('RGB')) + bgrs = [bgr] * self.seq_length + return bgrs + + def _get_random_video_background(self): + clip_idx = random.choice(range(len(self.background_video_clips))) + frame_count = len(self.background_video_frames[clip_idx]) + frame_idx = random.choice(range(max(1, frame_count - self.seq_length))) + clip = self.background_video_clips[clip_idx] + bgrs = [] + for i in self.seq_sampler(self.seq_length): + frame_idx_t = frame_idx + i + frame = self.background_video_frames[clip_idx][frame_idx_t % frame_count] + with Image.open(os.path.join(self.background_video_dir, clip, frame)) as bgr: + bgr = self._downsample_if_needed(bgr.convert('RGB')) + bgrs.append(bgr) + return bgrs + + def _get_videomatte(self, idx): + clip_idx, frame_idx = self.videomatte_idx[idx] + clip = self.videomatte_clips[clip_idx] + frame_count = len(self.videomatte_frames[clip_idx]) + fgrs, phas = [], [] + for i in self.seq_sampler(self.seq_length): + frame = self.videomatte_frames[clip_idx][(frame_idx + i) % frame_count] + with Image.open(os.path.join(self.videomatte_dir, 'fgr', clip, frame)) as fgr, \ + Image.open(os.path.join(self.videomatte_dir, 'pha', clip, frame)) as pha: + fgr = self._downsample_if_needed(fgr.convert('RGB')) + pha = self._downsample_if_needed(pha.convert('L')) + fgrs.append(fgr) + phas.append(pha) + return fgrs, phas + + def _downsample_if_needed(self, img): + w, h = img.size + if min(w, h) > self.size: + scale = self.size / min(w, h) + w = int(scale * w) + h = int(scale * h) + img = img.resize((w, h)) + return img + +class VideoMatteTrainAugmentation(MotionAugmentation): + def __init__(self, size): + super().__init__( + size=size, + prob_fgr_affine=0.3, + prob_bgr_affine=0.3, + prob_noise=0.1, + prob_color_jitter=0.3, + prob_grayscale=0.02, + prob_sharpness=0.1, + prob_blur=0.02, + prob_hflip=0.5, + prob_pause=0.03, + ) + +class VideoMatteValidAugmentation(MotionAugmentation): + def __init__(self, size): + super().__init__( + size=size, + prob_fgr_affine=0, + prob_bgr_affine=0, + prob_noise=0, + prob_color_jitter=0, + prob_grayscale=0, + prob_sharpness=0, + prob_blur=0, + prob_hflip=0, + prob_pause=0, + ) diff --git a/dataset/youtubevis.py b/dataset/youtubevis.py new file mode 100644 index 0000000..babbf0e --- /dev/null +++ b/dataset/youtubevis.py @@ -0,0 +1,123 @@ +import torch +import os +import json +import numpy as np +import random +from torch.utils.data import Dataset +from PIL import Image +from torchvision import transforms +from torchvision.transforms import functional as F + + +class YouTubeVISDataset(Dataset): + def __init__(self, videodir, annfile, size, seq_length, seq_sampler, transform=None): + self.videodir = videodir + self.size = size + self.seq_length = seq_length + self.seq_sampler = seq_sampler + self.transform = transform + + with open(annfile) as f: + data = json.load(f) + + self.masks = {} + for ann in data['annotations']: + if ann['category_id'] == 26: # person + video_id = ann['video_id'] + if video_id not in self.masks: + self.masks[video_id] = [[] for _ in range(len(ann['segmentations']))] + for frame, mask in zip(self.masks[video_id], ann['segmentations']): + if mask is not None: + frame.append(mask) + + self.videos = {} + for video in data['videos']: + video_id = video['id'] + if video_id in self.masks: + self.videos[video_id] = video + + self.index = [] + for video_id in self.videos.keys(): + for frame in range(len(self.videos[video_id])): + self.index.append((video_id, frame)) + + def __len__(self): + return len(self.index) + + def __getitem__(self, idx): + video_id, frame_id = self.index[idx] + video = self.videos[video_id] + frame_count = len(self.videos[video_id]['file_names']) + H, W = video['height'], video['width'] + + imgs, segs = [], [] + for t in self.seq_sampler(self.seq_length): + frame = (frame_id + t) % frame_count + + filename = video['file_names'][frame] + masks = self.masks[video_id][frame] + + with Image.open(os.path.join(self.videodir, filename)) as img: + imgs.append(self._downsample_if_needed(img.convert('RGB'), Image.BILINEAR)) + + seg = np.zeros((H, W), dtype=np.uint8) + for mask in masks: + seg |= self._decode_rle(mask) + segs.append(self._downsample_if_needed(Image.fromarray(seg), Image.NEAREST)) + + if self.transform is not None: + imgs, segs = self.transform(imgs, segs) + + return imgs, segs + + def _decode_rle(self, rle): + H, W = rle['size'] + msk = np.zeros(H * W, dtype=np.uint8) + encoding = rle['counts'] + skip = 0 + for i in range(0, len(encoding) - 1, 2): + skip += encoding[i] + draw = encoding[i + 1] + msk[skip : skip + draw] = 255 + skip += draw + return msk.reshape(W, H).transpose() + + def _downsample_if_needed(self, img, resample): + w, h = img.size + if min(w, h) > self.size: + scale = self.size / min(w, h) + w = int(scale * w) + h = int(scale * h) + img = img.resize((w, h), resample) + return img + + +class YouTubeVISAugmentation: + def __init__(self, size): + self.size = size + self.jitter = transforms.ColorJitter(0.3, 0.3, 0.3, 0.15) + + def __call__(self, imgs, segs): + + # To tensor + imgs = torch.stack([F.to_tensor(img) for img in imgs]) + segs = torch.stack([F.to_tensor(seg) for seg in segs]) + + # Resize + params = transforms.RandomResizedCrop.get_params(imgs, scale=(0.8, 1), ratio=(0.9, 1.1)) + imgs = F.resized_crop(imgs, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + segs = F.resized_crop(segs, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + + # Color jitter + imgs = self.jitter(imgs) + + # Grayscale + if random.random() < 0.05: + imgs = F.rgb_to_grayscale(imgs, num_output_channels=3) + + # Horizontal flip + if random.random() < 0.5: + imgs = F.hflip(imgs) + segs = F.hflip(segs) + + return imgs, 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