mirror of https://github.com/ZHKKKe/MODNet.git
upload the code of MODNet training iteration
ZHKKKe
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README.md
44
README.md
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@ -20,13 +20,15 @@ WebCam Video Demo [<a href="demo/video_matting/webcam">Offline</a>][<a href="htt
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## News
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- [Jan 28 2021] Release the [code](src/trainer.py) of MODNet training iteration.
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- [Dec 25 2020] ***Merry Christmas!*** :christmas_tree: Release Custom Video Matting Demo [[Offline](demo/video_matting/custom)] for user videos.
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- [Dec 15 2020] A cool [WebGUI](https://gradio.app/g/modnet) for image matting based on MODNet is built by the [Gradio](https://github.com/gradio-app/gradio) team!
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- [Dec 10 2020] Release WebCam Video Matting Demo [[Offline](demo/video_matting/webcam)][[Colab](https://colab.research.google.com/drive/1Pt3KDSc2q7WxFvekCnCLD8P0gBEbxm6J?usp=sharing)] and Image Matting Demo [[Colab](https://colab.research.google.com/drive/1GANpbKT06aEFiW-Ssx0DQnnEADcXwQG6?usp=sharing)].
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- [Nov 24 2020] Release [Arxiv Preprint](https://arxiv.org/pdf/2011.11961.pdf) and [Supplementary Video](https://youtu.be/PqJ3BRHX3Lc).
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## Video Matting Demo
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## Demos
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### Video Matting
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We provide two real-time portrait video matting demos based on WebCam. When using the demo, you can move the WebCam around at will.
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If you have an Ubuntu system, we recommend you to try the [offline demo](demo/video_matting/webcam) to get a higher *fps*. Otherwise, you can access the [online Colab demo](https://colab.research.google.com/drive/1Pt3KDSc2q7WxFvekCnCLD8P0gBEbxm6J?usp=sharing).
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We also provide an [offline demo](demo/video_matting/custom) that allows you to process custom videos.
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@ -34,21 +36,36 @@ We also provide an [offline demo](demo/video_matting/custom) that allows you to
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<img src="doc/gif/video_matting_demo.gif" width='60%'>
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## Image Matting Demo
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### Image Matting
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We provide an [online Colab demo](https://colab.research.google.com/drive/1GANpbKT06aEFiW-Ssx0DQnnEADcXwQG6?usp=sharing) for portrait image matting.
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It allows you to upload portrait images and predict/visualize/download the alpha mattes.
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<img src="doc/gif/image_matting_demo.gif" width='40%'>
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You can also use this [WebGUI](https://gradio.app/g/modnet) (hosted on [Gradio](https://github.com/gradio-app/gradio)) for portrait image matting from your browser without any code!
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<img src="https://i.ibb.co/9gLxFXF/modnet.gif" width='40%'>
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### Community
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Here we share some cool applications of MODNet built by the community.
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- **WebGUI for Image Matting**
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You can try [this WebGUI](https://gradio.app/g/modnet) (hosted on [Gradio](https://www.gradio.app/)) for portrait matting from your browser without any code!
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<!-- <img src="https://i.ibb.co/9gLxFXF/modnet.gif" width='40%'> -->
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- **Colab Demo of Bokeh (Blur Background)**
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You can try [this Colab demo](https://colab.research.google.com/github/eyaler/avatars4all/blob/master/yarok.ipynb) (built by [@eyaler](https://github.com/eyaler)) to blur the backgroud based on MODNet!
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## TO DO
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- Release training code (scheduled in **Jan. 2021**)
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- Release PPM-100 validation benchmark (scheduled in **Feb. 2021**)
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## Code
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We provide the [code](src/trainer.py) of MODNet training iteration, including:
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- **Supervised Training**: Train MODNet on a labeled matting dataset
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- **SOC Adaptation**: Adapt a trained MODNet to an unlabeled dataset
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In the function comments, we provide examples of how to call the function.
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## TODO
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- Release the code of One-Frame Delay (OFD)
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- Release PPM-100 validation benchmark (scheduled in **Feb 2021**)
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**NOTE**: PPM-100 is a **validation set**. Our training set will not be published
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## License
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@ -56,8 +73,12 @@ This project (code, pre-trained models, demos, *etc.*) is released under the [Cr
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## Acknowledgement
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We thank [City University of Hong Kong](https://www.cityu.edu.hk/) and [SenseTime](https://www.sensetime.com/) for their support to this project.
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We thank the [Gradio](https://github.com/gradio-app/gradio) team for their contributions to building the demos.
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- We thank [City University of Hong Kong](https://www.cityu.edu.hk/) and [SenseTime](https://www.sensetime.com/) for their support to this project.
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- We thank
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[the Gradio team](https://github.com/gradio-app/gradio), [@eyaler](https://github.com/eyaler),
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for their cool applications based on MODNet.
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## Citation
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If this work helps your research, please consider to cite:
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@ -71,6 +92,7 @@ If this work helps your research, please consider to cite:
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}
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```
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## Contact
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This project is currently maintained by Zhanghan Ke ([@ZHKKKe](https://github.com/ZHKKKe)).
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If you have any questions, please feel free to contact `kezhanghan@outlook.com`.
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@ -0,0 +1,295 @@
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import math
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import scipy
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import numpy as np
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from scipy.ndimage import grey_dilation, grey_erosion
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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__all__ = [
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'supervised_training_iter',
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'soc_adaptation_iter',
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]
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# ----------------------------------------------------------------------------------
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# Tool Classes/Functions
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# ----------------------------------------------------------------------------------
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class GaussianBlurLayer(nn.Module):
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""" Add Gaussian Blur to a 4D tensors
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This layer takes a 4D tensor of {N, C, H, W} as input.
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The Gaussian blur will be performed in given channel number (C) splitly.
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"""
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def __init__(self, channels, kernel_size):
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"""
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Arguments:
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channels (int): Channel for input tensor
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kernel_size (int): Size of the kernel used in blurring
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"""
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super(GaussianBlurLayer, self).__init__()
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self.channels = channels
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self.kernel_size = kernel_size
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assert self.kernel_size % 2 != 0
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self.op = nn.Sequential(
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nn.ReflectionPad2d(math.floor(self.kernel_size / 2)),
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nn.Conv2d(channels, channels, self.kernel_size,
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stride=1, padding=0, bias=None, groups=channels)
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)
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self._init_kernel()
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def forward(self, x):
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"""
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Arguments:
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x (torch.Tensor): input 4D tensor
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Returns:
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torch.Tensor: Blurred version of the input
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"""
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if not len(list(x.shape)) == 4:
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print('\'GaussianBlurLayer\' requires a 4D tensor as input\n')
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exit()
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elif not x.shape[1] == self.channels:
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print('In \'GaussianBlurLayer\', the required channel ({0}) is'
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'not the same as input ({1})\n'.format(self.channels, x.shape[1]))
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exit()
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return self.op(x)
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def _init_kernel(self):
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sigma = 0.3 * ((self.kernel_size - 1) * 0.5 - 1) + 0.8
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n = np.zeros((self.kernel_size, self.kernel_size))
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i = math.floor(self.kernel_size / 2)
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n[i, i] = 1
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kernel = scipy.ndimage.gaussian_filter(n, sigma)
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for name, param in self.named_parameters():
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param.data.copy_(torch.from_numpy(kernel))
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# ----------------------------------------------------------------------------------
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# ----------------------------------------------------------------------------------
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# MODNet Training Functions
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# ----------------------------------------------------------------------------------
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blurer = GaussianBlurLayer(1, 3).cuda()
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def supervised_training_iter(
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modnet, optimizer, image, trimap, gt_matte,
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semantic_scale=10.0, detail_scale=10.0, matte_scale=1.0):
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""" Supervised training iteration of MODNet
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This function trains MODNet for one iteration in a labeled dataset.
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Arguments:
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modnet (torch.nn.Module): instance of MODNet
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optimizer (torch.optim.Optimizer): optimizer for supervised training
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image (torch.autograd.Variable): input RGB image
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trimap (torch.autograd.Variable): trimap used to calculate the losses
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NOTE: foreground=1, background=0, unknown=0.5
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gt_matte (torch.autograd.Variable): ground truth alpha matte
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semantic_scale (float): scale of the semantic loss
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NOTE: please adjust according to your dataset
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detail_scale (float): scale of the detail loss
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NOTE: please adjust according to your dataset
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matte_scale (float): scale of the matte loss
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NOTE: please adjust according to your dataset
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Returns:
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semantic_loss (torch.Tensor): loss of the semantic estimation [Low-Resolution (LR) Branch]
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detail_loss (torch.Tensor): loss of the detail prediction [High-Resolution (HR) Branch]
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matte_loss (torch.Tensor): loss of the semantic-detail fusion [Fusion Branch]
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Example:
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import torch
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from src.models.modnet import MODNet
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from src.trainer import supervised_training_iter
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bs = 16 # batch size
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lr = 0.01 # learn rate
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epochs = 40 # total epochs
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modnet = torch.nn.DataParallel(MODNet()).cuda()
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optimizer = torch.optim.SGD(modnet.parameters(), lr=lr, momentum=0.9)
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lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=int(0.25 * epochs), gamma=0.1)
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dataloader = CREATE_YOUR_DATALOADER(bs) # NOTE: please finish this function
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for epoch in range(0, epochs):
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for idx, (image, trimap, gt_matte) in enumerate(dataloader):
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semantic_loss, detail_loss, matte_loss = \
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supervised_training_iter(modnet, optimizer, image, trimap, gt_matte)
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lr_scheduler.step()
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"""
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global blurer
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# set the model to train mode and clear the optimizer
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modnet.train()
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optimizer.zero_grad()
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# forward the model
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pred_semantic, pred_detail, pred_matte = modnet(image, False)
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# calculate the boundary mask from the trimap
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boundaries = (trimap < 0.5) + (trimap > 0.5)
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# calculate the semantic loss
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gt_semantic = F.interpolate(gt_matte, scale_factor=1/16, mode='bilinear')
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gt_semantic = blurer(gt_semantic)
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semantic_loss = torch.mean(F.mse_loss(pred_semantic, gt_semantic))
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semantic_loss = semantic_scale * semantic_loss
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# calculate the detail loss
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pred_boundary_detail = torch.where(boundaries, trimap, pred_detail)
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gt_detail = torch.where(boundaries, trimap, gt_matte)
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detail_loss = torch.mean(F.l1_loss(pred_boundary_detail, gt_detail))
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detail_loss = detail_scale * detail_loss
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# calculate the matte loss
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pred_boundary_matte = torch.where(boundaries, trimap, pred_matte)
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matte_l1_loss = F.l1_loss(pred_matte, gt_matte) + 4.0 * F.l1_loss(pred_boundary_matte, gt_matte)
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matte_compositional_loss = F.l1_loss(image * pred_matte, image * gt_matte) \
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+ 4.0 * F.l1_loss(image * pred_boundary_matte, image * gt_matte)
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matte_loss = torch.mean(matte_l1_loss + matte_compositional_loss)
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matte_loss = matte_scale * matte_loss
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# calculate the final loss, backward the loss, and update the model
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loss = semantic_loss + detail_loss + matte_loss
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loss.backward()
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optimizer.step()
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# for test
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return semantic_loss, detail_loss, matte_loss
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def soc_adaptation_iter(
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modnet, backup_modnet, optimizer, image,
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soc_semantic_scale=100.0, soc_detail_scale=1.0):
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""" Self-Supervised sub-objective consistency (SOC) adaptation iteration of MODNet
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This function fine-tunes MODNet for one iteration in an unlabeled dataset.
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Note that SOC can only fine-tune a converged MODNet, i.e., MODNet that has been
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trained in a labeled dataset.
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Arguments:
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modnet (torch.nn.Module): instance of MODNet
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backup_modnet (torch.nn.Module): backup of the trained MODNet
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optimizer (torch.optim.Optimizer): optimizer for self-supervised SOC
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image (torch.autograd.Variable): input RGB image
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soc_semantic_scale (float): scale of the SOC semantic loss
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NOTE: please adjust according to your dataset
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soc_detail_scale (float): scale of the SOC detail loss
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NOTE: please adjust according to your dataset
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Returns:
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soc_semantic_loss (torch.Tensor): loss of the semantic SOC
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soc_detail_loss (torch.Tensor): loss of the detail SOC
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Example:
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import copy
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import torch
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from src.models.modnet import MODNet
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from src.trainer import soc_adaptation_iter
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bs = 1 # batch size
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lr = 0.00001 # learn rate
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epochs = 10 # total epochs
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modnet = torch.nn.DataParallel(MODNet()).cuda()
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modnet = LOAD_TRAINED_CKPT() # NOTE: please finish this function
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optimizer = torch.optim.Adam(modnet.parameters(), lr=lr, betas=(0.9, 0.99))
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dataloader = CREATE_YOUR_DATALOADER(bs) # NOTE: please finish this function
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for epoch in range(0, epochs):
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backup_modnet = copy.deepcopy(modnet)
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for idx, (image) in enumerate(dataloader):
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soc_semantic_loss, soc_detail_loss = \
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soc_adaptation_iter(modnet, backup_modnet, optimizer, image)
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"""
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global blurer
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# set the backup model to eval mode
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backup_modnet.eval()
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# set the main model to train mode and freeze its norm layers
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modnet.train()
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modnet.module.freeze_norm()
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# clear the optimizer
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optimizer.zero_grad()
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# forward the main model
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pred_semantic, pred_detail, pred_matte = modnet(image, False)
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# forward the backup model
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with torch.no_grad():
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_, pred_backup_detail, pred_backup_matte = backup_modnet(image, False)
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# calculate the boundary mask from `pred_matte` and `pred_semantic`
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pred_matte_fg = (pred_matte.detach() > 0.1).float()
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pred_semantic_fg = (pred_semantic.detach() > 0.1).float()
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pred_semantic_fg = F.interpolate(pred_semantic_fg, scale_factor=16, mode='bilinear')
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pred_fg = pred_matte_fg * pred_semantic_fg
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n, c, h, w = pred_matte.shape
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np_pred_fg = pred_fg.data.cpu().numpy()
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np_boundaries = np.zeros([n, c, h, w])
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for sdx in range(0, n):
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sample_np_boundaries = np_boundaries[sdx, 0, ...]
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sample_np_pred_fg = np_pred_fg[sdx, 0, ...]
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side = int((h + w) / 2 * 0.05)
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dilated = grey_dilation(sample_np_pred_fg, size=(side, side))
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eroded = grey_erosion(sample_np_pred_fg, size=(side, side))
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sample_np_boundaries[np.where(dilated - eroded != 0)] = 1
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np_boundaries[sdx, 0, ...] = sample_np_boundaries
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boundaries = torch.tensor(np_boundaries).float().cuda()
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# sub-objectives consistency between `pred_semantic` and `pred_matte`
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# generate pseudo ground truth for `pred_semantic`
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downsampled_pred_matte = blurer(F.interpolate(pred_matte, scale_factor=1/16, mode='bilinear'))
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pseudo_gt_semantic = downsampled_pred_matte.detach()
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pseudo_gt_semantic = pseudo_gt_semantic * (pseudo_gt_semantic > 0.01).float()
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# generate pseudo ground truth for `pred_matte`
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pseudo_gt_matte = pred_semantic.detach()
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pseudo_gt_matte = pseudo_gt_matte * (pseudo_gt_matte > 0.01).float()
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# calculate the SOC semantic loss
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soc_semantic_loss = F.mse_loss(pred_semantic, pseudo_gt_semantic) + F.mse_loss(downsampled_pred_matte, pseudo_gt_matte)
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soc_semantic_loss = soc_semantic_scale * torch.mean(soc_semantic_loss)
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# NOTE: using the formulas in our paper to calculate the following losses has similar results
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# sub-objectives consistency between `pred_detail` and `pred_backup_detail` (on boundaries only)
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backup_detail_loss = boundaries * F.l1_loss(pred_detail, pred_backup_detail)
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backup_detail_loss = torch.sum(backup_detail_loss, dim=(1,2,3)) / torch.sum(boundaries, dim=(1,2,3))
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backup_detail_loss = torch.mean(backup_detail_loss)
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# sub-objectives consistency between pred_matte` and `pred_backup_matte` (on boundaries only)
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backup_matte_loss = boundaries * F.l1_loss(pred_matte, pred_backup_matte)
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backup_matte_loss = torch.sum(backup_matte_loss, dim=(1,2,3)) / torch.sum(boundaries, dim=(1,2,3))
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backup_matte_loss = torch.mean(backup_matte_loss)
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soc_detail_loss = soc_detail_scale * (backup_detail_loss + backup_matte_loss)
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# calculate the final loss, backward the loss, and update the model
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loss = soc_semantic_loss + soc_detail_loss
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loss.backward()
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optimizer.step()
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return soc_semantic_loss, soc_detail_loss
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# ----------------------------------------------------------------------------------
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