commit BiRefNetUltra node
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import torch
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import torch.nn as nn
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from collections import OrderedDict
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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 torchvision.models import vgg16, vgg16_bn
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from torchvision.models import resnet50
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from ..config import Config
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from ..dataset import class_labels_TR_sorted
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from BiRefNet.backbones.build_backbone import build_backbone
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from BiRefNet.modules.decoder_blocks import BasicDecBlk
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from BiRefNet.modules.lateral_blocks import BasicLatBlk
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from BiRefNet.modules.ing import *
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from BiRefNet.refinement.stem_layer import StemLayer
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class RefinerPVTInChannels4(nn.Module):
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def __init__(self, in_channels=3+1):
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super(RefinerPVTInChannels4, self).__init__()
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self.config = Config()
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self.epoch = 1
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self.bb = build_backbone(self.config.bb, params_settings='in_channels=4')
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lateral_channels_in_collection = {
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'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
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'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
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'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
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}
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channels = lateral_channels_in_collection[self.config.bb]
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self.squeeze_module = BasicDecBlk(channels[0], channels[0])
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self.decoder = Decoder(channels)
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if 0:
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for key, value in self.named_parameters():
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if 'bb.' in key:
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value.requires_grad = False
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def forward(self, x):
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if isinstance(x, list):
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x = torch.cat(x, dim=1)
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########## Encoder ##########
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if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
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x1 = self.bb.conv1(x)
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x2 = self.bb.conv2(x1)
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x3 = self.bb.conv3(x2)
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x4 = self.bb.conv4(x3)
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else:
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x1, x2, x3, x4 = self.bb(x)
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x4 = self.squeeze_module(x4)
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########## Decoder ##########
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features = [x, x1, x2, x3, x4]
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scaled_preds = self.decoder(features)
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return scaled_preds
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class Refiner(nn.Module):
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def __init__(self, in_channels=3+1):
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super(Refiner, self).__init__()
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self.config = Config()
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self.epoch = 1
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self.stem_layer = StemLayer(in_channels=in_channels, inter_channels=48, out_channels=3)
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self.bb = build_backbone(self.config.bb)
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lateral_channels_in_collection = {
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'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
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'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
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'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
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}
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channels = lateral_channels_in_collection[self.config.bb]
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self.squeeze_module = BasicDecBlk(channels[0], channels[0])
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self.decoder = Decoder(channels)
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if 0:
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for key, value in self.named_parameters():
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if 'bb.' in key:
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value.requires_grad = False
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def forward(self, x):
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if isinstance(x, list):
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x = torch.cat(x, dim=1)
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x = self.stem_layer(x)
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########## Encoder ##########
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if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
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x1 = self.bb.conv1(x)
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x2 = self.bb.conv2(x1)
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x3 = self.bb.conv3(x2)
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x4 = self.bb.conv4(x3)
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else:
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x1, x2, x3, x4 = self.bb(x)
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x4 = self.squeeze_module(x4)
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########## Decoder ##########
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features = [x, x1, x2, x3, x4]
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scaled_preds = self.decoder(features)
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return scaled_preds
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class Decoder(nn.Module):
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def __init__(self, channels):
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super(Decoder, self).__init__()
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self.config = Config()
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DecoderBlock = eval('BasicDecBlk')
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LateralBlock = eval('BasicLatBlk')
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self.decoder_block4 = DecoderBlock(channels[0], channels[1])
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self.decoder_block3 = DecoderBlock(channels[1], channels[2])
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self.decoder_block2 = DecoderBlock(channels[2], channels[3])
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self.decoder_block1 = DecoderBlock(channels[3], channels[3]//2)
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self.lateral_block4 = LateralBlock(channels[1], channels[1])
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self.lateral_block3 = LateralBlock(channels[2], channels[2])
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self.lateral_block2 = LateralBlock(channels[3], channels[3])
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if self.config.ms_supervision:
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self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
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self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
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self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
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self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2, 1, 1, 1, 0))
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def forward(self, features):
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x, x1, x2, x3, x4 = features
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outs = []
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p4 = self.decoder_block4(x4)
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_p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
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_p3 = _p4 + self.lateral_block4(x3)
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p3 = self.decoder_block3(_p3)
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_p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
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_p2 = _p3 + self.lateral_block3(x2)
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p2 = self.decoder_block2(_p2)
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_p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
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_p1 = _p2 + self.lateral_block2(x1)
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_p1 = self.decoder_block1(_p1)
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_p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
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p1_out = self.conv_out1(_p1)
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if self.config.ms_supervision:
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outs.append(self.conv_ms_spvn_4(p4))
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outs.append(self.conv_ms_spvn_3(p3))
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outs.append(self.conv_ms_spvn_2(p2))
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outs.append(p1_out)
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return outs
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class RefUNet(nn.Module):
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# Refinement
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def __init__(self, in_channels=3+1):
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super(RefUNet, self).__init__()
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self.encoder_1 = nn.Sequential(
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nn.Conv2d(in_channels, 64, 3, 1, 1),
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nn.Conv2d(64, 64, 3, 1, 1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True)
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)
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self.encoder_2 = nn.Sequential(
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nn.MaxPool2d(2, 2, ceil_mode=True),
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nn.Conv2d(64, 64, 3, 1, 1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True)
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)
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self.encoder_3 = nn.Sequential(
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nn.MaxPool2d(2, 2, ceil_mode=True),
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nn.Conv2d(64, 64, 3, 1, 1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True)
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)
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self.encoder_4 = nn.Sequential(
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nn.MaxPool2d(2, 2, ceil_mode=True),
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nn.Conv2d(64, 64, 3, 1, 1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True)
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)
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self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True)
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#####
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self.decoder_5 = nn.Sequential(
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nn.Conv2d(64, 64, 3, 1, 1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True)
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)
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#####
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self.decoder_4 = nn.Sequential(
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nn.Conv2d(128, 64, 3, 1, 1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True)
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)
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self.decoder_3 = nn.Sequential(
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nn.Conv2d(128, 64, 3, 1, 1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True)
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)
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self.decoder_2 = nn.Sequential(
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nn.Conv2d(128, 64, 3, 1, 1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True)
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)
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self.decoder_1 = nn.Sequential(
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nn.Conv2d(128, 64, 3, 1, 1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True)
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)
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self.conv_d0 = nn.Conv2d(64, 1, 3, 1, 1)
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self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
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def forward(self, x):
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outs = []
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if isinstance(x, list):
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x = torch.cat(x, dim=1)
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hx = x
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hx1 = self.encoder_1(hx)
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hx2 = self.encoder_2(hx1)
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hx3 = self.encoder_3(hx2)
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hx4 = self.encoder_4(hx3)
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hx = self.decoder_5(self.pool4(hx4))
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hx = torch.cat((self.upscore2(hx), hx4), 1)
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d4 = self.decoder_4(hx)
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hx = torch.cat((self.upscore2(d4), hx3), 1)
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d3 = self.decoder_3(hx)
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hx = torch.cat((self.upscore2(d3), hx2), 1)
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d2 = self.decoder_2(hx)
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hx = torch.cat((self.upscore2(d2), hx1), 1)
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d1 = self.decoder_1(hx)
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x = self.conv_d0(d1)
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outs.append(x)
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return outs
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@@ -0,0 +1,45 @@
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import torch.nn as nn
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from BiRefNet.modules.utils import build_act_layer, build_norm_layer
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class StemLayer(nn.Module):
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r""" Stem layer of InternImage
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Args:
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in_channels (int): number of input channels
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out_channels (int): number of output channels
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act_layer (str): activation layer
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norm_layer (str): normalization layer
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"""
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def __init__(self,
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in_channels=3+1,
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inter_channels=48,
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out_channels=96,
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act_layer='GELU',
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norm_layer='BN'):
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super().__init__()
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self.conv1 = nn.Conv2d(in_channels,
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inter_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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self.norm1 = build_norm_layer(
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inter_channels, norm_layer, 'channels_first', 'channels_first'
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)
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self.act = build_act_layer(act_layer)
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self.conv2 = nn.Conv2d(inter_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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self.norm2 = build_norm_layer(
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out_channels, norm_layer, 'channels_first', 'channels_first'
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)
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def forward(self, x):
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x = self.conv1(x)
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x = self.norm1(x)
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x = self.act(x)
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x = self.conv2(x)
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x = self.norm2(x)
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return x
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