File size: 5,683 Bytes
8b4c6c7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
import torch
from torch import nn
from torch.nn import functional as F
class Basic_Conv3x3(nn.Module):
"""
Basic convolution layers including: Conv3x3, BatchNorm2d, ReLU layers.
"""
def __init__(
self,
in_chans,
out_chans,
stride=2,
padding=1,
):
super().__init__()
self.conv = nn.Conv2d(in_chans, out_chans, 3, stride, padding, bias=False)
self.bn = nn.BatchNorm2d(out_chans)
self.relu = nn.ReLU(True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class ConvStream(nn.Module):
"""
Simple ConvStream containing a series of basic conv3x3 layers to extract detail features.
"""
def __init__(
self,
in_chans = 4,
out_chans = [48, 96, 192],
):
super().__init__()
self.convs = nn.ModuleList()
self.conv_chans = out_chans.copy()
self.conv_chans.insert(0, in_chans)
for i in range(len(self.conv_chans)-1):
in_chan_ = self.conv_chans[i]
out_chan_ = self.conv_chans[i+1]
self.convs.append(
Basic_Conv3x3(in_chan_, out_chan_)
)
def forward(self, x):
out_dict = {'D0': x}
for i in range(len(self.convs)):
x = self.convs[i](x)
name_ = 'D'+str(i+1)
out_dict[name_] = x
return out_dict
class Fusion_Block(nn.Module):
"""
Simple fusion block to fuse feature from ConvStream and Plain Vision Transformer.
"""
def __init__(
self,
in_chans,
out_chans,
):
super().__init__()
self.conv = Basic_Conv3x3(in_chans, out_chans, stride=1, padding=1)
def forward(self, x, D):
F_up = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
out = torch.cat([D, F_up], dim=1)
out = self.conv(out)
return out
class Matting_Head(nn.Module):
"""
Simple Matting Head, containing only conv3x3 and conv1x1 layers.
"""
def __init__(
self,
in_chans = 32,
mid_chans = 16,
):
super().__init__()
self.matting_convs = nn.Sequential(
nn.Conv2d(in_chans, mid_chans, 3, 1, 1),
nn.BatchNorm2d(mid_chans),
nn.ReLU(True),
nn.Conv2d(mid_chans, 1, 1, 1, 0)
)
def forward(self, x):
x = self.matting_convs(x)
return x
class Detail_Capture(nn.Module):
"""
Simple and Lightweight Detail Capture Module for ViT Matting.
"""
def __init__(
self,
in_chans = [384, 1],
img_chans=4,
convstream_out = [48, 96, 192],
fusion_out = [256, 128, 64, 32],
):
super().__init__()
assert len(fusion_out) == len(convstream_out) + 1
self.convstream = ConvStream(in_chans=img_chans, out_chans=convstream_out)
self.conv_chans = self.convstream.conv_chans # [4, 48, 96, 192]
self.fusion_blks = nn.ModuleList()
self.fus_channs = fusion_out.copy()
self.fus_channs.insert(0, in_chans[0]) # [384, 256, 128, 64, 32]
for i in range(len(self.fus_channs)-1):
in_channels = self.fus_channs[i] + self.conv_chans[-(i+1)] if i != 2 else in_chans[1] + self.conv_chans[-(i+1)] # [256 + 192 = 448, 256 + 96 = 352, 128 + 48 = 176, 64 + 4 = 68]
out_channels = self.fus_channs[i+1] # [256, 128, 64, 32]
self.fusion_blks.append(
Fusion_Block(
in_chans = in_channels,
out_chans = out_channels,
)
)
self.matting_head = Matting_Head( # 32 --> 1
in_chans = fusion_out[-1],
)
def forward(self, features, images):
detail_features = self.convstream(images) # [1, 4, 672, 992] --> D0: [1, 4, 672, 992], D1: [1, 48, 336, 496], D2: [1, 96, 168, 248], D3: [1, 192, 84, 124]
for i in range(len(self.fusion_blks)): # D3
d_name_ = 'D'+str(len(self.fusion_blks)-i-1)
features = self.fusion_blks[i](features, detail_features[d_name_])
phas = torch.sigmoid(self.matting_head(features))
return {'phas': phas}
class Ori_Detail_Capture(nn.Module):
"""
Simple and Lightweight Detail Capture Module for ViT Matting.
"""
def __init__(
self,
in_chans = 384,
img_chans=4,
convstream_out = [48, 96, 192],
fusion_out = [256, 128, 64, 32],
):
super().__init__()
assert len(fusion_out) == len(convstream_out) + 1
self.convstream = ConvStream(in_chans = img_chans)
self.conv_chans = self.convstream.conv_chans
self.fusion_blks = nn.ModuleList()
self.fus_channs = fusion_out.copy()
self.fus_channs.insert(0, in_chans)
for i in range(len(self.fus_channs)-1):
self.fusion_blks.append(
Fusion_Block(
in_chans = self.fus_channs[i] + self.conv_chans[-(i+1)],
out_chans = self.fus_channs[i+1],
)
)
self.matting_head = Matting_Head(
in_chans = fusion_out[-1],
)
def forward(self, features, images):
detail_features = self.convstream(images)
for i in range(len(self.fusion_blks)):
d_name_ = 'D'+str(len(self.fusion_blks)-i-1)
features = self.fusion_blks[i](features, detail_features[d_name_])
phas = torch.sigmoid(self.matting_head(features))
return {'phas': phas}
|