Initial clean project import
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cat-shark
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## MODNet - TorchScript Model
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This TorchScript version of MODNet is provided by [@yarkable](https://github.com/yarkable) from the community.
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Please note that the PyTorch version required for this TorchScript export function is higher than the official MODNet code (torch>=1.2.0).
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You can also download the TorchScript version of the official **Image Matting Model** from [this link](https://pan.baidu.com/s/1kOmmmbG7lSZiSmDdE7CaRw) with the exextraction code `dm9e`.
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To export the TorchScript version of MODNet (assuming you are currently in project root directory):
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1. Download the pre-trained **Image Matting Model** from this [link](https://drive.google.com/drive/folders/1umYmlCulvIFNaqPjwod1SayFmSRHziyR?usp=sharing) and put the model into the folder `MODNet/pretrained/`.
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2. Ensure your PyTorch version >= 1.2.0.
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3. Export the TorchScript version of MODNet by:
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```shell
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python -m torchscript.export_torchscript \
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--ckpt-path=pretrained/modnet_photographic_portrait_matting.ckpt \
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--output-path=pretrained/modnet_photographic_portrait_matting.torchscript
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```
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"""
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Export TorchScript model of MODNet
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Arguments:
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--ckpt-path: path of the checkpoint that will be converted
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--output-path: path for saving the TorchScript model
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Example:
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python export_torchscript.py \
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--ckpt-path=modnet_photographic_portrait_matting.ckpt \
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--output-path=modnet_photographic_portrait_matting.torchscript
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"""
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import os
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import argparse
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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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from . import modnet_torchscript
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if __name__ == '__main__':
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# define cmd arguments
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parser = argparse.ArgumentParser()
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parser.add_argument('--ckpt-path', type=str, required=True, help='path of the checkpoint that will be converted')
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parser.add_argument('--output-path', type=str, required=True, help='path for saving the TorchScript model')
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args = parser.parse_args()
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# check input arguments
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if not os.path.exists(args.ckpt_path):
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print(args.ckpt_path)
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print('Cannot find checkpoint path: {0}'.format(args.ckpt_path))
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exit()
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# create MODNet and load the pre-trained ckpt
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modnet = modnet_torchscript.MODNet(backbone_pretrained=False)
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modnet = nn.DataParallel(modnet).cuda()
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state_dict = torch.load(args.ckpt_path)
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modnet.load_state_dict(state_dict)
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modnet.eval()
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# export to TorchScript model
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scripted_model = torch.jit.script(modnet.module)
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torch.jit.save(scripted_model, os.path.join(args.output_path))
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"""
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This file contains a modified version of the original file `modnet.py` without
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`pred_semantic` and `pred_details` as these both returns None when `inference=True`
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And it does not contain `inference` argument which will make it easier to
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convert checkpoint to TorchScript model.
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"""
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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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from src.models.backbones import SUPPORTED_BACKBONES
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#------------------------------------------------------------------------------
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# MODNet Basic Modules
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#------------------------------------------------------------------------------
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class IBNorm(nn.Module):
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""" Combine Instance Norm and Batch Norm into One Layer
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"""
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def __init__(self, in_channels):
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super(IBNorm, self).__init__()
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in_channels = in_channels
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self.bnorm_channels = int(in_channels / 2)
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self.inorm_channels = in_channels - self.bnorm_channels
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self.bnorm = nn.BatchNorm2d(self.bnorm_channels, affine=True)
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self.inorm = nn.InstanceNorm2d(self.inorm_channels, affine=False)
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def forward(self, x):
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bn_x = self.bnorm(x[:, :self.bnorm_channels, ...].contiguous())
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in_x = self.inorm(x[:, self.bnorm_channels:, ...].contiguous())
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return torch.cat((bn_x, in_x), 1)
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class Conv2dIBNormRelu(nn.Module):
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""" Convolution + IBNorm + ReLu
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"""
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def __init__(self, in_channels, out_channels, kernel_size,
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stride=1, padding=0, dilation=1, groups=1, bias=True,
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with_ibn=True, with_relu=True):
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super(Conv2dIBNormRelu, self).__init__()
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layers = [
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nn.Conv2d(in_channels, out_channels, kernel_size,
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stride=stride, padding=padding, dilation=dilation,
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groups=groups, bias=bias)
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]
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if with_ibn:
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layers.append(IBNorm(out_channels))
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if with_relu:
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layers.append(nn.ReLU(inplace=True))
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self.layers = nn.Sequential(*layers)
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def forward(self, x):
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return self.layers(x)
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class SEBlock(nn.Module):
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""" SE Block Proposed in https://arxiv.org/pdf/1709.01507.pdf
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"""
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def __init__(self, in_channels, out_channels, reduction=1):
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super(SEBlock, self).__init__()
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self.pool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Sequential(
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nn.Linear(in_channels, int(in_channels // reduction), bias=False),
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nn.ReLU(inplace=True),
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nn.Linear(int(in_channels // reduction), out_channels, bias=False),
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nn.Sigmoid()
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)
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def forward(self, x):
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b, c, _, _ = x.size()
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w = self.pool(x).view(b, c)
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w = self.fc(w).view(b, c, 1, 1)
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return x * w.expand_as(x)
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#------------------------------------------------------------------------------
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# MODNet Branches
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#------------------------------------------------------------------------------
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class LRBranch(nn.Module):
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""" Low Resolution Branch of MODNet
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"""
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def __init__(self, backbone):
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super(LRBranch, self).__init__()
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enc_channels = backbone.enc_channels
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self.backbone = backbone
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self.se_block = SEBlock(enc_channels[4], enc_channels[4], reduction=4)
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self.conv_lr16x = Conv2dIBNormRelu(enc_channels[4], enc_channels[3], 5, stride=1, padding=2)
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self.conv_lr8x = Conv2dIBNormRelu(enc_channels[3], enc_channels[2], 5, stride=1, padding=2)
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self.conv_lr = Conv2dIBNormRelu(enc_channels[2], 1, kernel_size=3, stride=2, padding=1, with_ibn=False, with_relu=False)
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def forward(self, img):
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enc_features = self.backbone.forward(img)
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enc2x, enc4x, enc32x = enc_features[0], enc_features[1], enc_features[4]
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enc32x = self.se_block(enc32x)
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lr16x = F.interpolate(enc32x, scale_factor=2.0, mode='bilinear', align_corners=False)
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lr16x = self.conv_lr16x(lr16x)
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lr8x = F.interpolate(lr16x, scale_factor=2.0, mode='bilinear', align_corners=False)
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lr8x = self.conv_lr8x(lr8x)
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return lr8x, enc2x, enc4x
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class HRBranch(nn.Module):
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""" High Resolution Branch of MODNet
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"""
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def __init__(self, hr_channels, enc_channels):
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super(HRBranch, self).__init__()
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self.tohr_enc2x = Conv2dIBNormRelu(enc_channels[0], hr_channels, 1, stride=1, padding=0)
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self.conv_enc2x = Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=2, padding=1)
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self.tohr_enc4x = Conv2dIBNormRelu(enc_channels[1], hr_channels, 1, stride=1, padding=0)
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self.conv_enc4x = Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1)
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self.conv_hr4x = nn.Sequential(
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Conv2dIBNormRelu(3 * hr_channels + 3, 2 * hr_channels, 3, stride=1, padding=1),
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Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1),
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Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1),
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)
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self.conv_hr2x = nn.Sequential(
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Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1),
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Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1),
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Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1),
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Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1),
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)
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self.conv_hr = nn.Sequential(
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Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=1, padding=1),
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Conv2dIBNormRelu(hr_channels, 1, kernel_size=1, stride=1, padding=0, with_ibn=False, with_relu=False),
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)
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def forward(self, img, enc2x, enc4x, lr8x):
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img2x = F.interpolate(img, scale_factor=1/2, mode='bilinear', align_corners=False)
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img4x = F.interpolate(img, scale_factor=1/4, mode='bilinear', align_corners=False)
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enc2x = self.tohr_enc2x(enc2x)
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hr4x = self.conv_enc2x(torch.cat((img2x, enc2x), dim=1))
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enc4x = self.tohr_enc4x(enc4x)
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hr4x = self.conv_enc4x(torch.cat((hr4x, enc4x), dim=1))
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lr4x = F.interpolate(lr8x, scale_factor=2.0, mode='bilinear', align_corners=False)
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hr4x = self.conv_hr4x(torch.cat((hr4x, lr4x, img4x), dim=1))
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hr2x = F.interpolate(hr4x, scale_factor=2.0, mode='bilinear', align_corners=False)
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hr2x = self.conv_hr2x(torch.cat((hr2x, enc2x), dim=1))
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return hr2x
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class FusionBranch(nn.Module):
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""" Fusion Branch of MODNet
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"""
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def __init__(self, hr_channels, enc_channels):
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super(FusionBranch, self).__init__()
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self.conv_lr4x = Conv2dIBNormRelu(enc_channels[2], hr_channels, 5, stride=1, padding=2)
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self.conv_f2x = Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1)
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self.conv_f = nn.Sequential(
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Conv2dIBNormRelu(hr_channels + 3, int(hr_channels / 2), 3, stride=1, padding=1),
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Conv2dIBNormRelu(int(hr_channels / 2), 1, 1, stride=1, padding=0, with_ibn=False, with_relu=False),
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)
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def forward(self, img, lr8x, hr2x):
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lr4x = F.interpolate(lr8x, scale_factor=2.0, mode='bilinear', align_corners=False)
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lr4x = self.conv_lr4x(lr4x)
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lr2x = F.interpolate(lr4x, scale_factor=2.0, mode='bilinear', align_corners=False)
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f2x = self.conv_f2x(torch.cat((lr2x, hr2x), dim=1))
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f = F.interpolate(f2x, scale_factor=2.0, mode='bilinear', align_corners=False)
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f = self.conv_f(torch.cat((f, img), dim=1))
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pred_matte = torch.sigmoid(f)
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return pred_matte
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#------------------------------------------------------------------------------
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# MODNet
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#------------------------------------------------------------------------------
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class MODNet(nn.Module):
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""" Architecture of MODNet
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"""
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def __init__(self, in_channels=3, hr_channels=32, backbone_arch='mobilenetv2', backbone_pretrained=True):
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super(MODNet, self).__init__()
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self.in_channels = in_channels
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self.hr_channels = hr_channels
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self.backbone_arch = backbone_arch
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self.backbone_pretrained = backbone_pretrained
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self.backbone = SUPPORTED_BACKBONES[self.backbone_arch](self.in_channels)
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self.lr_branch = LRBranch(self.backbone)
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self.hr_branch = HRBranch(self.hr_channels, self.backbone.enc_channels)
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self.f_branch = FusionBranch(self.hr_channels, self.backbone.enc_channels)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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self._init_conv(m)
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elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.InstanceNorm2d):
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self._init_norm(m)
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if self.backbone_pretrained:
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self.backbone.load_pretrained_ckpt()
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def forward(self, img):
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# NOTE
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lr_out = self.lr_branch(img)
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lr8x = lr_out[0]
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enc2x = lr_out[1]
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enc4x = lr_out[2]
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hr2x = self.hr_branch(img, enc2x, enc4x, lr8x)
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pred_matte = self.f_branch(img, lr8x, hr2x)
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return pred_matte
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def freeze_norm(self):
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norm_types = [nn.BatchNorm2d, nn.InstanceNorm2d]
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for m in self.modules():
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for n in norm_types:
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if isinstance(m, n):
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m.eval()
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continue
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def _init_conv(self, conv):
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nn.init.kaiming_uniform_(
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conv.weight, a=0, mode='fan_in', nonlinearity='relu')
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if conv.bias is not None:
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nn.init.constant_(conv.bias, 0)
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def _init_norm(self, norm):
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if norm.weight is not None:
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nn.init.constant_(norm.weight, 1)
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nn.init.constant_(norm.bias, 0)
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