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be88765 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | import torch.nn as nn
import torch
from .utils import common
class BaseNet(nn.Module):
def forward_one(self, x):
raise NotImplementedError()
def forward(self, imgs):
res = self.forward_one(imgs)
return res
class Cyclindrical_ConvNet(BaseNet):
def __init__(self, inchan=3, dilated=True, dilation=1, bn=True, bn_affine=False):
BaseNet.__init__(self)
self.inchan = inchan
self.curchan = inchan
self.dilated = dilated
self.dilation = dilation
self.bn = bn
self.bn_affine = bn_affine
self.ops = nn.ModuleList([])
def _make_bn_2d(self, outd):
return nn.BatchNorm2d(outd, affine=self.bn_affine)
def _make_bn_3d(self, outd):
return nn.BatchNorm3d(outd, affine=self.bn_affine)
def _add_conv_2d(self, outd, k=3, stride=1, dilation=1, bn=True, relu=True):
d = self.dilation * dilation
self.dilation *= stride
self.ops.append(nn.Conv2d(self.curchan, outd, kernel_size=(k, k), dilation=d))
if bn and self.bn: self.ops.append( self._make_bn_2d(outd) )
if relu: self.ops.append( nn.ReLU(inplace=True) )
self.curchan = outd
def _add_conv_3d(self, outd, k, stride=1, dilation=1, bn=True, relu=True):
d = self.dilation * dilation
self.dilation *= stride
self.ops.append(nn.Conv3d(self.curchan, outd, kernel_size=(k[0], k[1], k[2]), dilation=d))
if bn and self.bn: self.ops.append( self._make_bn_3d(outd) )
if relu: self.ops.append( nn.ReLU(inplace=True) )
self.curchan = outd
def forward_one(self, x):
assert self.ops, "You need to add convolutions first"
for n,op in enumerate(self.ops):
k_exist = hasattr(op, 'kernel_size')
if k_exist:
if len(op.kernel_size) == 3:
x = common.pad_image_3d(x, op.kernel_size[1] + (op.kernel_size[1]-1)*(op.dilation[0]-1))
else:
if len(x.shape) == 5:
x = x.squeeze(2)
mid_feat = x
x = common.pad_image(x, op.kernel_size[0] + (op.kernel_size[0]-1)*(op.dilation[0]-1))
x = op(x)
try:
mid_feat
except NameError:
return x
else:
return x, mid_feat
class Cylindrical_Net (Cyclindrical_ConvNet):
"""
Compute a 32D descriptor for cylindrical feature maps
"""
def __init__(self, inchan=16, dim=32, **kw ):
Cyclindrical_ConvNet.__init__(self, inchan=inchan, **kw)
add_conv_2d = lambda n, **kw: self._add_conv_2d(n, **kw)
add_conv_3d = lambda n, **kw: self._add_conv_3d(n, **kw)
add_conv_3d(64, k=[3, 3, 3])
add_conv_2d(64)
add_conv_2d(128)
add_conv_2d(128)
add_conv_2d(64)
add_conv_2d(64)
add_conv_2d(32)
add_conv_2d(32, bn=False, relu=False)
self.out_dim = dim
class Cylindrical_UNet(nn.Module):
"""
Compute a 32D descriptor for cylindrical feature maps with U-Net-like architecture
"""
def __init__(self, inchan=16, dim=32):
super(Cylindrical_UNet, self).__init__()
# Initial Conv3D Block
self.conv3d = nn.Sequential(
nn.Conv3d(inchan, 32, kernel_size=(3, 3, 3), stride=1, dilation=1),
nn.BatchNorm3d(32),
nn.ReLU(inplace=True),
)
# U-Net Encoder
self.encoder1 = self.make_conv_block(32, 32) # Encoder Level 1
self.encoder2 = self.make_conv_block(32, 64) # Encoder Level 2
self.encoder3 = self.make_conv_block(64, 128) # Encoder Level 3
# U-Net Bottleneck
self.bottleneck = self.make_conv_block(128, 128)
# U-Net Decoder
self.decoder3 = self.make_conv_block(128 + 128, 64) # Concat with Encoder Level 3
self.decoder2 = self.make_conv_block(64 + 64, 32) # Concat with Encoder Level 2
self.decoder1 = self.make_conv_block(32 + 32, 32) # Concat with Encoder Level 1
# Final Output Layer
self.output_layer = nn.Sequential(
nn.Conv2d(32, dim, kernel_size=3, stride=1, dilation=1),
nn.BatchNorm2d(dim),
nn.ReLU(inplace=True),
)
def make_conv_block(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, bn=True, relu=True):
layers = [nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation)]
if bn:
layers.append(nn.BatchNorm2d(out_channels))
if relu:
layers.append(nn.ReLU(inplace=True))
return nn.Sequential(*layers)
def forward(self, x):
# Conv3D Feature Extraction
x = self.conv3d(common.pad_image_3d(x, kernel_size=3))
x = x.squeeze(2) # Squeeze 3D output to 2D
# U-Net Encoder
enc1 = self.encoder1(common.pad_image(x, kernel_size=3)) # Level 1
enc2 = self.encoder2(common.pad_image(enc1, kernel_size=3)) # Level 2
enc3 = self.encoder3(common.pad_image(enc2, kernel_size=3)) # Level 3
# U-Net Bottleneck
bottleneck = self.bottleneck(common.pad_image(enc3, kernel_size=3))
# U-Net Decoder with Concatenation-based Skip Connections
dec3 = self.decoder3(common.pad_image(torch.cat([bottleneck, enc3], dim=1), kernel_size=3)) # Concat with Encoder Level 3
dec2 = self.decoder2(common.pad_image(torch.cat([dec3, enc2], dim=1), kernel_size=3)) # Concat with Encoder Level 2
dec1 = self.decoder1(common.pad_image(torch.cat([dec2, enc1], dim=1), kernel_size=3)) # Concat with Encoder Level 1
# Final Output
output = self.output_layer(common.pad_image(dec1, kernel_size=3))
return output, None
class CostBlock(BaseNet):
def __init__(self, inchan=32, dilated=True, dilation=1, bn=True, bn_affine=False):
BaseNet.__init__(self)
self.inchan = inchan
self.curchan = inchan
self.dilated = dilated
self.dilation = dilation
self.bn = bn
self.bn_affine = bn_affine
self.ops = nn.ModuleList([])
def _make_bn_2d(self, outd):
return nn.BatchNorm2d(outd, affine=self.bn_affine)
def _make_bn_3d(self, outd):
return nn.BatchNorm3d(outd, affine=self.bn_affine)
def _add_conv_2d(self, outd, k=3, stride=1, dilation=1, bn=True, relu=True):
d = self.dilation * dilation
self.dilation *= stride
self.ops.append(nn.Conv2d(self.curchan, outd, kernel_size=(k, k), dilation=d))
if bn and self.bn: self.ops.append( self._make_bn_2d(outd) )
if relu: self.ops.append( nn.ReLU(inplace=True) )
self.curchan = outd
def _add_conv_3d(self, outd, k, stride=1, dilation=1, bn=True, relu=True):
d = self.dilation * dilation
self.dilation *= stride
self.ops.append(nn.Conv3d(self.curchan, outd, kernel_size=(k[0], k[1], k[2]), dilation=d))
if bn and self.bn: self.ops.append( self._make_bn_3d(outd) )
if relu: self.ops.append( nn.ReLU(inplace=True) )
self.curchan = outd
def forward_one(self, x):
assert self.ops, "You need to add convolutions first"
for n,op in enumerate(self.ops):
x = op(x)
return x
class CostNet(CostBlock):
"""
Cost aggregation
"""
def __init__(self, inchan=32, dim=1, **kw ):
CostBlock.__init__(self, inchan=inchan, **kw)
add_conv_2d = lambda n, **kw: self._add_conv_2d(n, **kw)
add_conv_3d = lambda n, **kw: self._add_conv_3d(n, **kw)
add_conv_3d(32, k=[3, 3, 3])
add_conv_3d(64, k=[3, 3, 3])
add_conv_3d(64, k=[3, 1, 3])
add_conv_3d(128, k=[3, 1, 3])
add_conv_3d(128, k=[3, 1, 3])
add_conv_3d(64, k=[3, 1, 3])
add_conv_3d(64, k=[3, 1, 3])
add_conv_3d(32, k=[3, 1, 3])
add_conv_3d(32, k=[3, 1, 3])
add_conv_3d(dim, k=[2, 1, 2], bn=False, relu=False)
self.out_dim = dim
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