import torch import torch.nn as nn import torch.nn.functional as F from kornia.filters import laplacian from huggingface_hub import PyTorchModelHubMixin from config import Config from dataset import class_labels_TR_sorted from models.backbones.build_backbone import build_backbone from models.modules.decoder_blocks import BasicDecBlk, ResBlk, HierarAttDecBlk from models.modules.lateral_blocks import BasicLatBlk from models.modules.aspp import ASPP, ASPPDeformable from models.modules.ing import * from models.refinement.refiner import Refiner, RefinerPVTInChannels4, RefUNet from models.refinement.stem_layer import StemLayer class BiRefNet( nn.Module, PyTorchModelHubMixin, library_name="birefnet", repo_url="https://github.com/ZhengPeng7/BiRefNet", tags=['Image Segmentation', 'Background Removal', 'Mask Generation', 'Dichotomous Image Segmentation', 'Camouflaged Object Detection', 'Salient Object Detection'] ): def __init__(self, bb_pretrained=True): super(BiRefNet, self).__init__() self.config = Config() self.epoch = 1 self.bb = build_backbone(self.config.bb, pretrained=bb_pretrained) channels = self.config.lateral_channels_in_collection if self.config.auxiliary_classification: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.cls_head = nn.Sequential( nn.Linear(channels[0], len(class_labels_TR_sorted)) ) if self.config.squeeze_block: self.squeeze_module = nn.Sequential(*[ eval(self.config.squeeze_block.split('_x')[0])(channels[0]+sum(self.config.cxt), channels[0]) for _ in range(eval(self.config.squeeze_block.split('_x')[1])) ]) self.decoder = Decoder(channels) if self.config.ender: self.dec_end = nn.Sequential( nn.Conv2d(1, 16, 3, 1, 1), nn.Conv2d(16, 1, 3, 1, 1), nn.ReLU(inplace=True), ) # refine patch-level segmentation if self.config.refine: if self.config.refine == 'itself': self.stem_layer = StemLayer(in_channels=3+1, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN') else: self.refiner = eval('{}({})'.format(self.config.refine, 'in_channels=3+1')) if self.config.freeze_bb: # Freeze the backbone... print(self.named_parameters()) for key, value in self.named_parameters(): if 'bb.' in key and 'refiner.' not in key: value.requires_grad = False def forward_enc(self, x): if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']: x1 = self.bb.conv1(x); x2 = self.bb.conv2(x1); x3 = self.bb.conv3(x2); x4 = self.bb.conv4(x3) else: x1, x2, x3, x4 = self.bb(x) if self.config.mul_scl_ipt == 'cat': B, C, H, W = x.shape x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True)) x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1) x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1) x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1) x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1) elif self.config.mul_scl_ipt == 'add': B, C, H, W = x.shape x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True)) x1 = x1 + F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True) x2 = x2 + F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True) x3 = x3 + F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True) x4 = x4 + F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True) class_preds = self.cls_head(self.avgpool(x4).view(x4.shape[0], -1)) if self.training and self.config.auxiliary_classification else None if self.config.cxt: x4 = torch.cat( ( *[ F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True), F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True), F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True), ][-len(self.config.cxt):], x4 ), dim=1 ) return (x1, x2, x3, x4), class_preds def forward_ori(self, x): ########## Encoder ########## (x1, x2, x3, x4), class_preds = self.forward_enc(x) if self.config.squeeze_block: x4 = self.squeeze_module(x4) ########## Decoder ########## features = [x, x1, x2, x3, x4] if self.training and self.config.out_ref: features.append(laplacian(torch.mean(x, dim=1).unsqueeze(1), kernel_size=5)) scaled_preds = self.decoder(features) return scaled_preds, class_preds def forward(self, x): scaled_preds, class_preds = self.forward_ori(x) class_preds_lst = [class_preds] return [scaled_preds, class_preds_lst] if self.training else scaled_preds class Decoder(nn.Module): def __init__(self, channels): super(Decoder, self).__init__() self.config = Config() DecoderBlock = eval(self.config.dec_blk) LateralBlock = eval(self.config.lat_blk) if self.config.dec_ipt: self.split = self.config.dec_ipt_split N_dec_ipt = 64 DBlock = SimpleConvs ic = 64 ipt_cha_opt = 1 self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic) self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic) self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic) self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic) self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic) else: self.split = None self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[1]) self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[2]) self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3]) self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3]//2) self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt] if self.config.dec_ipt else 0), 1, 1, 1, 0)) self.lateral_block4 = LateralBlock(channels[1], channels[1]) self.lateral_block3 = LateralBlock(channels[2], channels[2]) self.lateral_block2 = LateralBlock(channels[3], channels[3]) if self.config.ms_supervision: self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0) self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0) self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0) if self.config.out_ref: _N = 16 self.gdt_convs_4 = nn.Sequential(nn.Conv2d(channels[1], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True)) self.gdt_convs_3 = nn.Sequential(nn.Conv2d(channels[2], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True)) self.gdt_convs_2 = nn.Sequential(nn.Conv2d(channels[3], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True)) self.gdt_convs_pred_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) self.gdt_convs_pred_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) self.gdt_convs_pred_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) self.gdt_convs_attn_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) self.gdt_convs_attn_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) self.gdt_convs_attn_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0)) def get_patches_batch(self, x, p): _size_h, _size_w = p.shape[2:] patches_batch = [] for idx in range(x.shape[0]): columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1) patches_x = [] for column_x in columns_x: patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)] patch_sample = torch.cat(patches_x, dim=1) patches_batch.append(patch_sample) return torch.cat(patches_batch, dim=0) def forward(self, features): if self.training and self.config.out_ref: outs_gdt_pred = [] outs_gdt_label = [] x, x1, x2, x3, x4, gdt_gt = features else: x, x1, x2, x3, x4 = features outs = [] if self.config.dec_ipt: patches_batch = self.get_patches_batch(x, x4) if self.split else x x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1) p4 = self.decoder_block4(x4) m4 = self.conv_ms_spvn_4(p4) if self.config.ms_supervision else None if self.config.out_ref: p4_gdt = self.gdt_convs_4(p4) if self.training: # >> GT: m4_dia = m4 gdt_label_main_4 = gdt_gt * F.interpolate(m4_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True) outs_gdt_label.append(gdt_label_main_4) # >> Pred: gdt_pred_4 = self.gdt_convs_pred_4(p4_gdt) outs_gdt_pred.append(gdt_pred_4) gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid() # >> Finally: p4 = p4 * gdt_attn_4 _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True) _p3 = _p4 + self.lateral_block4(x3) if self.config.dec_ipt: patches_batch = self.get_patches_batch(x, _p3) if self.split else x _p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1) p3 = self.decoder_block3(_p3) m3 = self.conv_ms_spvn_3(p3) if self.config.ms_supervision else None if self.config.out_ref: p3_gdt = self.gdt_convs_3(p3) if self.training: # >> GT: # m3 --dilation--> m3_dia # G_3^gt * m3_dia --> G_3^m, which is the label of gradient m3_dia = m3 gdt_label_main_3 = gdt_gt * F.interpolate(m3_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True) outs_gdt_label.append(gdt_label_main_3) # >> Pred: # p3 --conv--BN--> F_3^G, where F_3^G predicts the \hat{G_3} with xx # F_3^G --sigmoid--> A_3^G gdt_pred_3 = self.gdt_convs_pred_3(p3_gdt) outs_gdt_pred.append(gdt_pred_3) gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid() # >> Finally: # p3 = p3 * A_3^G p3 = p3 * gdt_attn_3 _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True) _p2 = _p3 + self.lateral_block3(x2) if self.config.dec_ipt: patches_batch = self.get_patches_batch(x, _p2) if self.split else x _p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1) p2 = self.decoder_block2(_p2) m2 = self.conv_ms_spvn_2(p2) if self.config.ms_supervision else None if self.config.out_ref: p2_gdt = self.gdt_convs_2(p2) if self.training: # >> GT: m2_dia = m2 gdt_label_main_2 = gdt_gt * F.interpolate(m2_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True) outs_gdt_label.append(gdt_label_main_2) # >> Pred: gdt_pred_2 = self.gdt_convs_pred_2(p2_gdt) outs_gdt_pred.append(gdt_pred_2) gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid() # >> Finally: p2 = p2 * gdt_attn_2 _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True) _p1 = _p2 + self.lateral_block2(x1) if self.config.dec_ipt: patches_batch = self.get_patches_batch(x, _p1) if self.split else x _p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1) _p1 = self.decoder_block1(_p1) _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True) if self.config.dec_ipt: patches_batch = self.get_patches_batch(x, _p1) if self.split else x _p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1) p1_out = self.conv_out1(_p1) if self.config.ms_supervision: outs.append(m4) outs.append(m3) outs.append(m2) outs.append(p1_out) return outs if not (self.config.out_ref and self.training) else ([outs_gdt_pred, outs_gdt_label], outs) class SimpleConvs(nn.Module): def __init__( self, in_channels: int, out_channels: int, inter_channels=64 ) -> None: super().__init__() self.conv1 = nn.Conv2d(in_channels, inter_channels, 3, 1, 1) self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1) def forward(self, x): return self.conv_out(self.conv1(x))