""" 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)