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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 .backbones import SUPPORTED_BACKBONES |
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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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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, inference): |
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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, 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, mode='bilinear', align_corners=False) |
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lr8x = self.conv_lr8x(lr8x) |
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pred_semantic = None |
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if not inference: |
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lr = self.conv_lr(lr8x) |
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pred_semantic = torch.sigmoid(lr) |
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return pred_semantic, 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, inference): |
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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, 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, mode='bilinear', align_corners=False) |
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hr2x = self.conv_hr2x(torch.cat((hr2x, enc2x), dim=1)) |
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pred_detail = None |
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if not inference: |
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hr = F.interpolate(hr2x, scale_factor=2, mode='bilinear', align_corners=False) |
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hr = self.conv_hr(torch.cat((hr, img), dim=1)) |
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pred_detail = torch.sigmoid(hr) |
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return pred_detail, 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, 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, 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, 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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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, inference): |
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pred_semantic, lr8x, [enc2x, enc4x] = self.lr_branch(img, inference) |
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pred_detail, hr2x = self.hr_branch(img, enc2x, enc4x, lr8x, inference) |
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pred_matte = self.f_branch(img, lr8x, hr2x) |
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return pred_semantic, pred_detail, 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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