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| """ | |
| This file contains a modified version of the original file `modnet.py` without | |
| `pred_semantic` and `pred_details` as these both returns None when `inference=True` | |
| And it does not contain `inference` argument which will make it easier to | |
| convert checkpoint to TorchScript model. | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from src.models.backbones import SUPPORTED_BACKBONES | |
| #------------------------------------------------------------------------------ | |
| # MODNet Basic Modules | |
| #------------------------------------------------------------------------------ | |
| class IBNorm(nn.Module): | |
| """ Combine Instance Norm and Batch Norm into One Layer | |
| """ | |
| def __init__(self, in_channels): | |
| super(IBNorm, self).__init__() | |
| in_channels = in_channels | |
| self.bnorm_channels = int(in_channels / 2) | |
| self.inorm_channels = in_channels - self.bnorm_channels | |
| self.bnorm = nn.BatchNorm2d(self.bnorm_channels, affine=True) | |
| self.inorm = nn.InstanceNorm2d(self.inorm_channels, affine=False) | |
| def forward(self, x): | |
| bn_x = self.bnorm(x[:, :self.bnorm_channels, ...].contiguous()) | |
| in_x = self.inorm(x[:, self.bnorm_channels:, ...].contiguous()) | |
| return torch.cat((bn_x, in_x), 1) | |
| class Conv2dIBNormRelu(nn.Module): | |
| """ Convolution + IBNorm + ReLu | |
| """ | |
| def __init__(self, in_channels, out_channels, kernel_size, | |
| stride=1, padding=0, dilation=1, groups=1, bias=True, | |
| with_ibn=True, with_relu=True): | |
| super(Conv2dIBNormRelu, self).__init__() | |
| layers = [ | |
| nn.Conv2d(in_channels, out_channels, kernel_size, | |
| stride=stride, padding=padding, dilation=dilation, | |
| groups=groups, bias=bias) | |
| ] | |
| if with_ibn: | |
| layers.append(IBNorm(out_channels)) | |
| if with_relu: | |
| layers.append(nn.ReLU(inplace=True)) | |
| self.layers = nn.Sequential(*layers) | |
| def forward(self, x): | |
| return self.layers(x) | |
| class SEBlock(nn.Module): | |
| """ SE Block Proposed in https://arxiv.org/pdf/1709.01507.pdf | |
| """ | |
| def __init__(self, in_channels, out_channels, reduction=1): | |
| super(SEBlock, self).__init__() | |
| self.pool = nn.AdaptiveAvgPool2d(1) | |
| self.fc = nn.Sequential( | |
| nn.Linear(in_channels, int(in_channels // reduction), bias=False), | |
| nn.ReLU(inplace=True), | |
| nn.Linear(int(in_channels // reduction), out_channels, bias=False), | |
| nn.Sigmoid() | |
| ) | |
| def forward(self, x): | |
| b, c, _, _ = x.size() | |
| w = self.pool(x).view(b, c) | |
| w = self.fc(w).view(b, c, 1, 1) | |
| return x * w.expand_as(x) | |
| #------------------------------------------------------------------------------ | |
| # MODNet Branches | |
| #------------------------------------------------------------------------------ | |
| class LRBranch(nn.Module): | |
| """ Low Resolution Branch of MODNet | |
| """ | |
| def __init__(self, backbone): | |
| super(LRBranch, self).__init__() | |
| enc_channels = backbone.enc_channels | |
| self.backbone = backbone | |
| self.se_block = SEBlock(enc_channels[4], enc_channels[4], reduction=4) | |
| self.conv_lr16x = Conv2dIBNormRelu(enc_channels[4], enc_channels[3], 5, stride=1, padding=2) | |
| self.conv_lr8x = Conv2dIBNormRelu(enc_channels[3], enc_channels[2], 5, stride=1, padding=2) | |
| self.conv_lr = Conv2dIBNormRelu(enc_channels[2], 1, kernel_size=3, stride=2, padding=1, with_ibn=False, with_relu=False) | |
| def forward(self, img): | |
| enc_features = self.backbone.forward(img) | |
| enc2x, enc4x, enc32x = enc_features[0], enc_features[1], enc_features[4] | |
| enc32x = self.se_block(enc32x) | |
| lr16x = F.interpolate(enc32x, scale_factor=2.0, mode='bilinear', align_corners=False) | |
| lr16x = self.conv_lr16x(lr16x) | |
| lr8x = F.interpolate(lr16x, scale_factor=2.0, mode='bilinear', align_corners=False) | |
| lr8x = self.conv_lr8x(lr8x) | |
| return lr8x, enc2x, enc4x | |
| class HRBranch(nn.Module): | |
| """ High Resolution Branch of MODNet | |
| """ | |
| def __init__(self, hr_channels, enc_channels): | |
| super(HRBranch, self).__init__() | |
| self.tohr_enc2x = Conv2dIBNormRelu(enc_channels[0], hr_channels, 1, stride=1, padding=0) | |
| self.conv_enc2x = Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=2, padding=1) | |
| self.tohr_enc4x = Conv2dIBNormRelu(enc_channels[1], hr_channels, 1, stride=1, padding=0) | |
| self.conv_enc4x = Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1) | |
| self.conv_hr4x = nn.Sequential( | |
| Conv2dIBNormRelu(3 * hr_channels + 3, 2 * hr_channels, 3, stride=1, padding=1), | |
| Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1), | |
| Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1), | |
| ) | |
| self.conv_hr2x = nn.Sequential( | |
| Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1), | |
| Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1), | |
| Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1), | |
| Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1), | |
| ) | |
| self.conv_hr = nn.Sequential( | |
| Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=1, padding=1), | |
| Conv2dIBNormRelu(hr_channels, 1, kernel_size=1, stride=1, padding=0, with_ibn=False, with_relu=False), | |
| ) | |
| def forward(self, img, enc2x, enc4x, lr8x): | |
| img2x = F.interpolate(img, scale_factor=1/2, mode='bilinear', align_corners=False) | |
| img4x = F.interpolate(img, scale_factor=1/4, mode='bilinear', align_corners=False) | |
| enc2x = self.tohr_enc2x(enc2x) | |
| hr4x = self.conv_enc2x(torch.cat((img2x, enc2x), dim=1)) | |
| enc4x = self.tohr_enc4x(enc4x) | |
| hr4x = self.conv_enc4x(torch.cat((hr4x, enc4x), dim=1)) | |
| lr4x = F.interpolate(lr8x, scale_factor=2.0, mode='bilinear', align_corners=False) | |
| hr4x = self.conv_hr4x(torch.cat((hr4x, lr4x, img4x), dim=1)) | |
| hr2x = F.interpolate(hr4x, scale_factor=2.0, mode='bilinear', align_corners=False) | |
| hr2x = self.conv_hr2x(torch.cat((hr2x, enc2x), dim=1)) | |
| return hr2x | |
| class FusionBranch(nn.Module): | |
| """ Fusion Branch of MODNet | |
| """ | |
| def __init__(self, hr_channels, enc_channels): | |
| super(FusionBranch, self).__init__() | |
| self.conv_lr4x = Conv2dIBNormRelu(enc_channels[2], hr_channels, 5, stride=1, padding=2) | |
| self.conv_f2x = Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1) | |
| self.conv_f = nn.Sequential( | |
| Conv2dIBNormRelu(hr_channels + 3, int(hr_channels / 2), 3, stride=1, padding=1), | |
| Conv2dIBNormRelu(int(hr_channels / 2), 1, 1, stride=1, padding=0, with_ibn=False, with_relu=False), | |
| ) | |
| def forward(self, img, lr8x, hr2x): | |
| lr4x = F.interpolate(lr8x, scale_factor=2.0, mode='bilinear', align_corners=False) | |
| lr4x = self.conv_lr4x(lr4x) | |
| lr2x = F.interpolate(lr4x, scale_factor=2.0, mode='bilinear', align_corners=False) | |
| f2x = self.conv_f2x(torch.cat((lr2x, hr2x), dim=1)) | |
| f = F.interpolate(f2x, scale_factor=2.0, mode='bilinear', align_corners=False) | |
| f = self.conv_f(torch.cat((f, img), dim=1)) | |
| pred_matte = torch.sigmoid(f) | |
| return pred_matte | |
| #------------------------------------------------------------------------------ | |
| # MODNet | |
| #------------------------------------------------------------------------------ | |
| class MODNet(nn.Module): | |
| """ Architecture of MODNet | |
| """ | |
| def __init__(self, in_channels=3, hr_channels=32, backbone_arch='mobilenetv2', backbone_pretrained=True): | |
| super(MODNet, self).__init__() | |
| self.in_channels = in_channels | |
| self.hr_channels = hr_channels | |
| self.backbone_arch = backbone_arch | |
| self.backbone_pretrained = backbone_pretrained | |
| self.backbone = SUPPORTED_BACKBONES[self.backbone_arch](self.in_channels) | |
| self.lr_branch = LRBranch(self.backbone) | |
| self.hr_branch = HRBranch(self.hr_channels, self.backbone.enc_channels) | |
| self.f_branch = FusionBranch(self.hr_channels, self.backbone.enc_channels) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| self._init_conv(m) | |
| elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.InstanceNorm2d): | |
| self._init_norm(m) | |
| if self.backbone_pretrained: | |
| self.backbone.load_pretrained_ckpt() | |
| def forward(self, img): | |
| # NOTE | |
| lr_out = self.lr_branch(img) | |
| lr8x = lr_out[0] | |
| enc2x = lr_out[1] | |
| enc4x = lr_out[2] | |
| hr2x = self.hr_branch(img, enc2x, enc4x, lr8x) | |
| pred_matte = self.f_branch(img, lr8x, hr2x) | |
| return pred_matte | |
| def freeze_norm(self): | |
| norm_types = [nn.BatchNorm2d, nn.InstanceNorm2d] | |
| for m in self.modules(): | |
| for n in norm_types: | |
| if isinstance(m, n): | |
| m.eval() | |
| continue | |
| def _init_conv(self, conv): | |
| nn.init.kaiming_uniform_( | |
| conv.weight, a=0, mode='fan_in', nonlinearity='relu') | |
| if conv.bias is not None: | |
| nn.init.constant_(conv.bias, 0) | |
| def _init_norm(self, norm): | |
| if norm.weight is not None: | |
| nn.init.constant_(norm.weight, 1) | |
| nn.init.constant_(norm.bias, 0) | |