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gfm.py
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gfm.py
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"""
Bridging Composite and Real: Towards End-to-end Deep Image Matting [IJCV-2021]
Main network file (GFM).
Copyright (c) 2021, Jizhizi Li ([email protected])
Licensed under the MIT License (see LICENSE for details)
Github repo: https://github.com/JizhiziLi/GFM
Paper link (Arxiv): https://arxiv.org/abs/2010.16188
"""
import torch
import torch.nn as nn
from torchvision import models
import torch.nn.functional as F
from config import *
from util import *
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv_up_psp(in_channels, out_channels, up_sample):
return nn.Sequential(
nn.Conv2d(in_channels,out_channels,3,padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Upsample(scale_factor=up_sample, mode='bilinear',align_corners = False))
def build_bb(in_channels, mid_channels, out_channels):
return nn.Sequential(
nn.Conv2d(in_channels,mid_channels,3,dilation=2, padding=2),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels,out_channels,3,dilation=2, padding=2),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels,out_channels,3,dilation=2, padding=2),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True))
def build_decoder(in_channels, mid_channels_1, mid_channels_2, out_channels, last_bnrelu, upsample_flag):
layers = []
layers += [nn.Conv2d(in_channels,mid_channels_1,3,padding=1),
nn.BatchNorm2d(mid_channels_1),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels_1,mid_channels_2,3,padding=1),
nn.BatchNorm2d(mid_channels_2),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels_2,out_channels,3,padding=1)]
if last_bnrelu:
layers += [nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),]
if upsample_flag:
layers += [nn.Upsample(scale_factor=2, mode='bilinear')]
sequential = nn.Sequential(*layers)
return sequential
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class PSPModule(nn.Module):
def __init__(self, features, out_features=1024, sizes=(1, 2, 3, 6)):
super().__init__()
self.stages = []
self.stages = nn.ModuleList([self._make_stage(features, size) for size in sizes])
self.bottleneck = nn.Conv2d(features * (len(sizes) + 1), out_features, kernel_size=1)
self.relu = nn.ReLU()
def _make_stage(self, features, size):
prior = nn.AdaptiveAvgPool2d(output_size=(size, size))
conv = nn.Conv2d(features, features, kernel_size=1, bias=False)
return nn.Sequential(prior, conv)
def forward(self, feats):
h, w = feats.size(2), feats.size(3)
priors = [F.upsample(input=stage(feats), size=(h, w), mode='bilinear',align_corners = True) for stage in self.stages] + [feats]
bottle = self.bottleneck(torch.cat(priors, 1))
return self.relu(bottle)
class SELayer(nn.Module):
def __init__(self, channel, reduction=4):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)
class GFM(nn.Module):
def __init__(self, args):
super().__init__()
self.backbone = args.backbone
self.rosta = args.rosta
if self.rosta=='TT':
self.gd_channel = 3
else:
self.gd_channel = 2
if self.backbone=='r34_2b':
##################################
### Backbone - Resnet34 + 2 blocks
##################################
self.resnet = models.resnet34(pretrained=True)
self.encoder0 = nn.Sequential(
nn.Conv2d(3,64,3,padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True))
self.encoder1 = self.resnet.layer1
self.encoder2 = self.resnet.layer2
self.encoder3 = self.resnet.layer3
self.encoder4 = self.resnet.layer4
self.encoder5 = nn.Sequential(
nn.MaxPool2d(2, 2, ceil_mode=True),
BasicBlock(512,512),
BasicBlock(512,512),
BasicBlock(512,512))
self.encoder6 = nn.Sequential(
nn.MaxPool2d(2, 2, ceil_mode=True),
BasicBlock(512,512),
BasicBlock(512,512),
BasicBlock(512,512))
self.psp_module = PSPModule(512, 512, (1, 3, 5))
self.psp6 = conv_up_psp(512, 512, 2)
self.psp5 = conv_up_psp(512, 512, 4)
self.psp4 = conv_up_psp(512, 256, 8)
self.psp3 = conv_up_psp(512, 128, 16)
self.psp2 = conv_up_psp(512, 64, 32)
self.psp1 = conv_up_psp(512, 64, 32)
self.decoder6_g = build_decoder(1024, 512, 512, 512, True, True)
self.decoder5_g = build_decoder(1024, 512, 512, 512, True, True)
self.decoder4_g = build_decoder(1024, 512, 512, 256, True, True)
self.decoder3_g = build_decoder(512, 256, 256, 128, True, True)
self.decoder2_g = build_decoder(256, 128, 128, 64, True, True)
self.decoder1_g = build_decoder(128, 64, 64, 64, True, False)
self.bridge_block = build_bb(512, 512, 512)
self.decoder6_f = build_decoder(1024, 512, 512, 512, True, True)
self.decoder5_f = build_decoder(1024, 512, 512, 512, True, True)
self.decoder4_f = build_decoder(1024, 512, 512, 256, True, True)
self.decoder3_f = build_decoder(512, 256, 256, 128, True, True)
self.decoder2_f = build_decoder(256, 128, 128, 64, True, True)
self.decoder1_f = build_decoder(128, 64, 64, 64, True, False)
if self.rosta == 'RIM':
self.decoder0_g_tt = nn.Sequential(nn.Conv2d(64,3,3,padding=1))
self.decoder0_g_ft = nn.Sequential(nn.Conv2d(64,2,3,padding=1))
self.decoder0_g_bt = nn.Sequential(nn.Conv2d(64,2,3,padding=1))
self.decoder0_f_tt = nn.Sequential(nn.Conv2d(64,1,3,padding=1))
self.decoder0_f_ft = nn.Sequential(nn.Conv2d(64,1,3,padding=1))
self.decoder0_f_bt = nn.Sequential(nn.Conv2d(64,1,3,padding=1))
else:
self.decoder0_g = nn.Sequential(nn.Conv2d(64,self.gd_channel,3,padding=1))
self.decoder0_f = nn.Sequential(nn.Conv2d(64,1,3,padding=1))
if self.backbone=='r34':
##########################
### Backbone - Resnet34
##########################
self.resnet = models.resnet34(pretrained=True)
self.encoder0 = nn.Sequential(
self.resnet.conv1,
self.resnet.bn1,
self.resnet.relu)
self.encoder1 = nn.Sequential(
self.resnet.maxpool,
self.resnet.layer1)
self.encoder2 = self.resnet.layer2
self.encoder3 = self.resnet.layer3
self.encoder4 = self.resnet.layer4
self.psp_module = PSPModule(512, 512, (1, 3, 5))
self.psp4 = conv_up_psp(512, 256, 2)
self.psp3 = conv_up_psp(512, 128, 4)
self.psp2 = conv_up_psp(512, 64, 8)
self.psp1 = conv_up_psp(512, 64, 16)
self.decoder4_g = build_decoder(1024, 512, 512, 256, True, True)
self.decoder3_g = build_decoder(512, 256, 256, 128, True, True)
self.decoder2_g = build_decoder(256, 128, 128, 64, True, True)
self.decoder1_g = build_decoder(128, 64, 64, 64, True, True)
self.bridge_block = build_bb(512, 512, 512)
self.decoder4_f = build_decoder(1024, 512, 512, 256, True, True)
self.decoder3_f = build_decoder(512, 256, 256, 128, True, True)
self.decoder2_f = build_decoder(256, 128, 128, 64, True, True)
self.decoder1_f = build_decoder(128, 64, 64, 64, True, True)
if self.rosta == 'RIM':
self.decoder0_g_tt = build_decoder(128, 64, 64, 3, False, True)
self.decoder0_g_ft = build_decoder(128, 64, 64, 2, False, True)
self.decoder0_g_bt = build_decoder(128, 64, 64, 2, False, True)
self.decoder0_f_tt = build_decoder(128, 64, 64, 1, False, True)
self.decoder0_f_ft = build_decoder(128, 64, 64, 1, False, True)
self.decoder0_f_bt = build_decoder(128, 64, 64, 1, False, True)
else:
self.decoder0_g = build_decoder(128, 64, 64, self.gd_channel, False, True)
self.decoder0_f = build_decoder(128, 64, 64, 1, False, True)
elif self.backbone=='r101':
##########################
### Backbone - Resnet101
##########################
self.resnet = models.resnet101(pretrained=True)
self.encoder0 = nn.Sequential(
self.resnet.conv1,
self.resnet.bn1,
self.resnet.relu)
self.encoder1 = nn.Sequential(
self.resnet.maxpool,
self.resnet.layer1)
self.encoder2 = self.resnet.layer2
self.encoder3 = self.resnet.layer3
self.encoder4 = self.resnet.layer4
self.psp_module = PSPModule(2048, 2048, (1, 3, 5))
self.bridge_block = build_bb(2048, 2048, 2048)
self.psp4 = conv_up_psp(2048, 1024, 2)
self.psp3 = conv_up_psp(2048, 512, 4)
self.psp2 = conv_up_psp(2048, 256, 8)
self.psp1 = conv_up_psp(2048, 64, 16)
self.decoder4_g = build_decoder(4096, 2048, 1024, 1024, True, True)
self.decoder3_g = build_decoder(2048, 1024, 512, 512, True, True)
self.decoder2_g = build_decoder(1024, 512, 256, 256, True, True)
self.decoder1_g = build_decoder(512, 256, 128, 64, True, True)
self.decoder4_f = build_decoder(4096, 2048, 1024, 1024, True, True)
self.decoder3_f = build_decoder(2048, 1024, 512, 512, True, True)
self.decoder2_f = build_decoder(1024, 512, 256, 256, True, True)
self.decoder1_f = build_decoder(512, 256, 128, 64, True, True)
if self.rosta == 'RIM':
self.decoder0_g_tt = build_decoder(128, 64, 64, 3, False, True)
self.decoder0_g_ft = build_decoder(128, 64, 64, 2, False, True)
self.decoder0_g_bt = build_decoder(128, 64, 64, 2, False, True)
self.decoder0_f_tt = build_decoder(128, 64, 64, 1, False, True)
self.decoder0_f_ft = build_decoder(128, 64, 64, 1, False, True)
self.decoder0_f_bt = build_decoder(128, 64, 64, 1, False, True)
else:
self.decoder0_g = build_decoder(128, 64, 64, self.gd_channel, False, True)
self.decoder0_f = build_decoder(128, 64, 64, 1, False, True)
elif self.backbone=='d121':
#############################
### Encoder part - DESNET121
#############################
self.densenet = models.densenet121(pretrained=True)
self.encoder0 = nn.Sequential(
self.densenet.features.conv0,
self.densenet.features.norm0,
self.densenet.features.relu0)
self.encoder1 = nn.Sequential(
self.densenet.features.denseblock1,
self.densenet.features.transition1)
self.encoder2 = nn.Sequential(
self.densenet.features.denseblock2,
self.densenet.features.transition2)
self.encoder3 = nn.Sequential(
self.densenet.features.denseblock3,
self.densenet.features.transition3)
self.encoder4 = nn.Sequential(
self.densenet.features.denseblock4,
nn.Conv2d(1024,512,3,padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2, ceil_mode=True))
self.psp_module = PSPModule(512, 512, (1, 3, 5))
self.psp4 = conv_up_psp(512, 256, 2)
self.psp3 = conv_up_psp(512, 128, 4)
self.psp2 = conv_up_psp(512, 64, 8)
self.psp1 = conv_up_psp(512, 64, 16)
self.decoder4_g = build_decoder(1024, 512, 512, 256, True, True)
self.decoder3_g = build_decoder(512, 256, 256, 128, True, True)
self.decoder2_g = build_decoder(256, 128, 128, 64, True, True)
self.decoder1_g = build_decoder(128, 64, 64, 64, True, True)
self.bridge_block = build_bb(512, 512, 512)
self.decoder4_f = build_decoder(1024, 512, 512, 256, True, True)
self.decoder3_f = build_decoder(768, 256, 256, 128, True, True)
self.decoder2_f = build_decoder(384, 128, 128, 64, True, True)
self.decoder1_f = build_decoder(192, 64, 64, 64, True, True)
if self.rosta == 'RIM':
self.decoder0_g_tt = build_decoder(128, 64, 64, 3, False, True)
self.decoder0_g_ft = build_decoder(128, 64, 64, 2, False, True)
self.decoder0_g_bt = build_decoder(128, 64, 64, 2, False, True)
self.decoder0_f_tt = build_decoder(128, 64, 64, 1, False, True)
self.decoder0_f_ft = build_decoder(128, 64, 64, 1, False, True)
self.decoder0_f_bt = build_decoder(128, 64, 64, 1, False, True)
else:
self.decoder0_g = build_decoder(128, 64, 64, self.gd_channel, False, True)
self.decoder0_f = build_decoder(128, 64, 64, 1, False, True)
if self.rosta=='RIM':
self.rim = nn.Sequential(
nn.Conv2d(3,16,1),
SELayer(16),
nn.Conv2d(16,1,1))
def forward(self, input):
glance_sigmoid = torch.zeros(input.shape)
focus_sigmoid = torch.zeros(input.shape)
fusion_sigmoid = torch.zeros(input.shape)
#################
### Encoder part
#################
e0 = self.encoder0(input)
e1 = self.encoder1(e0)
e2 = self.encoder2(e1)
e3 = self.encoder3(e2)
e4 = self.encoder4(e3)
##########################
### Decoder part - GLANCE
##########################
if self.backbone=='r34_2b':
e5 = self.encoder5(e4)
e6 = self.encoder6(e5)
psp = self.psp_module(e6)
d6_g = self.decoder6_g(torch.cat((psp, e6),1))
d5_g = self.decoder5_g(torch.cat((self.psp6(psp),d6_g),1))
d4_g = self.decoder4_g(torch.cat((self.psp5(psp),d5_g),1))
else:
psp = self.psp_module(e4)
d4_g = self.decoder4_g(torch.cat((psp,e4),1))
d3_g = self.decoder3_g(torch.cat((self.psp4(psp),d4_g),1))
d2_g = self.decoder2_g(torch.cat((self.psp3(psp),d3_g),1))
d1_g = self.decoder1_g(torch.cat((self.psp2(psp),d2_g),1))
if self.backbone=='r34_2b':
if self.rosta=='RIM':
d0_g_tt = self.decoder0_g_tt(d1_g)
d0_g_ft = self.decoder0_g_ft(d1_g)
d0_g_bt = self.decoder0_g_bt(d1_g)
else:
d0_g = self.decoder0_g(d1_g)
else:
if self.rosta=='RIM':
d0_g_tt = self.decoder0_g_tt(torch.cat((self.psp1(psp),d1_g),1))
d0_g_ft = self.decoder0_g_ft(torch.cat((self.psp1(psp),d1_g),1))
d0_g_bt = self.decoder0_g_bt(torch.cat((self.psp1(psp),d1_g),1))
else:
d0_g = self.decoder0_g(torch.cat((self.psp1(psp),d1_g),1))
if self.rosta=='RIM':
glance_sigmoid_tt = F.sigmoid(d0_g_tt)
glance_sigmoid_ft = F.sigmoid(d0_g_ft)
glance_sigmoid_bt = F.sigmoid(d0_g_bt)
else:
glance_sigmoid = F.sigmoid(d0_g)
##########################
### Decoder part - FOCUS
##########################
if self.backbone == 'r34_2b':
bb = self.bridge_block(e6)
d6_f = self.decoder6_f(torch.cat((bb, e6),1))
d5_f = self.decoder5_f(torch.cat((d6_f, e5),1))
d4_f = self.decoder4_f(torch.cat((d5_f, e4),1))
else:
bb = self.bridge_block(e4)
d4_f = self.decoder4_f(torch.cat((bb, e4),1))
d3_f = self.decoder3_f(torch.cat((d4_f, e3),1))
d2_f = self.decoder2_f(torch.cat((d3_f, e2),1))
d1_f = self.decoder1_f(torch.cat((d2_f, e1),1))
if self.backbone=='r34_2b':
if self.rosta=='RIM':
d0_f_tt = self.decoder0_f_tt(d1_f)
d0_f_ft = self.decoder0_f_ft(d1_f)
d0_f_bt = self.decoder0_f_bt(d1_f)
else:
d0_f = self.decoder0_f(d1_f)
else:
if self.rosta=='RIM':
d0_f_tt = self.decoder0_f_tt(torch.cat((d1_f, e0),1))
d0_f_ft = self.decoder0_f_ft(torch.cat((d1_f, e0),1))
d0_f_bt = self.decoder0_f_bt(torch.cat((d1_f, e0),1))
else:
d0_f = self.decoder0_f(torch.cat((d1_f, e0),1))
if self.rosta=='RIM':
focus_sigmoid_tt = F.sigmoid(d0_f_tt)
focus_sigmoid_ft = F.sigmoid(d0_f_ft)
focus_sigmoid_bt = F.sigmoid(d0_f_bt)
else:
focus_sigmoid = F.sigmoid(d0_f)
##########################
### Collaborative Matting
##########################
if self.rosta=='RIM':
fusion_sigmoid_tt = collaborative_matting('TT', glance_sigmoid_tt, focus_sigmoid_tt)
fusion_sigmoid_ft = collaborative_matting('FT', glance_sigmoid_ft, focus_sigmoid_ft)
fusion_sigmoid_bt = collaborative_matting('BT', glance_sigmoid_bt, focus_sigmoid_bt)
fusion_sigmoid = torch.cat((fusion_sigmoid_tt,fusion_sigmoid_ft,fusion_sigmoid_bt),1)
fusion_sigmoid = self.rim(fusion_sigmoid)
return [[glance_sigmoid_tt, focus_sigmoid_tt, fusion_sigmoid_tt],[glance_sigmoid_ft, focus_sigmoid_ft, fusion_sigmoid_ft],[glance_sigmoid_bt, focus_sigmoid_bt, fusion_sigmoid_bt], fusion_sigmoid]
else:
fusion_sigmoid = collaborative_matting(self.rosta, glance_sigmoid, focus_sigmoid)
return glance_sigmoid, focus_sigmoid, fusion_sigmoid