def __init__(self,
in_channels,
out_channels,
reduction=1,
norm_layer=nn.BatchNorm2d,
**kwargs):
super(FeatureFusion, self).__init__()
self.conv1x1 = _ConvBNReLU(in_channels,
out_channels,
1,
1,
0,
norm_layer=norm_layer,
**kwargs)
self.channel_attention = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
_ConvBNReLU(out_channels,
out_channels // reduction,
1,
1,
0,
norm_layer=norm_layer),
_ConvBNReLU(out_channels // reduction,
out_channels,
1,
1,
0,
norm_layer=norm_layer), nn.Sigmoid())
def __init__(self, in_channels, out_channels, norm_layer=nn.BatchNorm2d, **kwargs):
super(SpatialPath, self).__init__()
inter_channels = 64
self.conv7x7 = _ConvBNReLU(in_channels, inter_channels, 7, 2, 3, norm_layer=norm_layer)
self.conv3x3_1 = _ConvBNReLU(inter_channels, inter_channels, 3, 2, 1, norm_layer=norm_layer)
self.conv3x3_2 = _ConvBNReLU(inter_channels, inter_channels, 3, 2, 1, norm_layer=norm_layer)
self.conv1x1 = _ConvBNReLU(inter_channels, out_channels, 1, 1, 0, norm_layer=norm_layer)
def __init__(self, backbone='resnet18', pretrained_base=True, norm_layer=nn.BatchNorm2d, **kwargs):
super(ContextPath, self).__init__()
if backbone == 'resnet18':
pretrained = resnet18(pretrained=pretrained_base, **kwargs)
else:
raise RuntimeError('unknown backbone: {}'.format(backbone))
self.conv1 = pretrained.conv1
self.bn1 = pretrained.bn1
self.relu = pretrained.relu
self.maxpool = pretrained.maxpool
self.layer1 = pretrained.layer1
self.layer2 = pretrained.layer2
self.layer3 = pretrained.layer3
self.layer4 = pretrained.layer4
inter_channels = 128
self.global_context = _GlobalAvgPooling(512, inter_channels, norm_layer)
self.arms = nn.ModuleList(
[AttentionRefinmentModule(512, inter_channels, norm_layer, **kwargs),
AttentionRefinmentModule(256, inter_channels, norm_layer, **kwargs)]
)
self.refines = nn.ModuleList(
[_ConvBNReLU(inter_channels, inter_channels, 3, 1, 1, norm_layer=norm_layer),
_ConvBNReLU(inter_channels, inter_channels, 3, 1, 1, norm_layer=norm_layer)]
)
def __init__(self, in_channels, out_channels, norm_layer=nn.BatchNorm2d, **kwargs):
super(AttentionRefinmentModule, self).__init__()
self.conv3x3 = _ConvBNReLU(in_channels, out_channels, 3, 1, 1, norm_layer=norm_layer)
self.channel_attention = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
_ConvBNReLU(out_channels, out_channels, 1, 1, 0, norm_layer=norm_layer),
nn.Sigmoid()
)
def __init__(self, in_channels, reduced_channels, out_channels, norm_layer, **kwargs):
super(_AttentionGeneration, self).__init__()
self.conv_reduce = _ConvBNReLU(in_channels, reduced_channels, 1, norm_layer=norm_layer)
self.attention = nn.Sequential(
_ConvBNReLU(reduced_channels, reduced_channels, 1, norm_layer=norm_layer),
nn.Conv2d(reduced_channels, out_channels, 1, bias=False))
self.reduced_channels = reduced_channels
def __init__(self, nclass, norm_layer=nn.BatchNorm2d, **kwargs):
super(_PSAHead, self).__init__()
# psa_out_channels = crop_size // 8 ** 2
self.psa = _PointwiseSpatialAttention(2048, 3600, norm_layer)
self.conv_post = _ConvBNReLU(1024, 2048, 1, norm_layer=norm_layer)
self.project = nn.Sequential(
_ConvBNReLU(4096, 512, 3, padding=1, norm_layer=norm_layer),
nn.Dropout2d(0.1, False), nn.Conv2d(512, nclass, 1))
def __init__(self, in_channels, inter_channels, nclass, norm_layer=nn.BatchNorm2d, **kwargs):
super(_BiSeHead, self).__init__()
self.block = nn.Sequential(
_ConvBNReLU(in_channels, inter_channels, 3, 1, 1, norm_layer=norm_layer),
nn.Dropout(0.1),
nn.Conv2d(inter_channels, nclass, 1)
)
def __init__(self, num_classes=1000, multiplier=1.0, norm_layer=nn.BatchNorm2d, **kwargs):
super(MobileNet, self).__init__()
conv_dw_setting = [
[64, 1, 1],
[128, 2, 2],
[256, 2, 2],
[512, 6, 2],
[1024, 2, 2]]
input_channels = int(32 * multiplier) if multiplier > 1.0 else 32
features = [_ConvBNReLU(3, input_channels, 3, 2, 1, norm_layer=norm_layer)]
for c, n, s in conv_dw_setting:
out_channels = int(c * multiplier)
for i in range(n):
stride = s if i == 0 else 1
features.append(_DepthwiseConv(input_channels, out_channels, stride, norm_layer))
input_channels = out_channels
features.append(nn.AdaptiveAvgPool2d(1))
self.features = nn.Sequential(*features)
self.classifier = nn.Linear(int(1024 * multiplier), num_classes)
# weight initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.zeros_(m.bias)
def __init__(self, in_channels, nclass, norm_layer=nn.BatchNorm2d, **kwargs):
super(APNModule, self).__init__()
self.conv1 = _ConvBNReLU(in_channels, in_channels, 3, 2, 1, norm_layer=norm_layer)
self.conv2 = _ConvBNReLU(in_channels, in_channels, 5, 2, 2, norm_layer=norm_layer)
self.conv3 = _ConvBNReLU(in_channels, in_channels, 7, 2, 3, norm_layer=norm_layer)
self.level1 = _ConvBNReLU(in_channels, nclass, 1, norm_layer=norm_layer)
self.level2 = _ConvBNReLU(in_channels, nclass, 1, norm_layer=norm_layer)
self.level3 = _ConvBNReLU(in_channels, nclass, 1, norm_layer=norm_layer)
self.level4 = _ConvBNReLU(in_channels, nclass, 1, norm_layer=norm_layer)
self.level5 = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
_ConvBNReLU(in_channels, nclass, 1))
def __init__(self,
nclass,
backbone="",
aux=False,
jpu=False,
pretrained_base=False,
**kwargs):
super(DFANet, self).__init__()
self.pretrained = get_xception_a(pretrained_base, **kwargs)
self.enc2_2 = Enc(240, 48, 4, **kwargs)
self.enc3_2 = Enc(144, 96, 6, **kwargs)
self.enc4_2 = Enc(288, 192, 4, **kwargs)
self.fca_2 = FCAttention(192, **kwargs)
self.enc2_3 = Enc(240, 48, 4, **kwargs)
self.enc3_3 = Enc(144, 96, 6, **kwargs)
self.enc3_4 = Enc(288, 192, 4, **kwargs)
self.fca_3 = FCAttention(192, **kwargs)
self.enc2_1_reduce = _ConvBNReLU(48, 32, 1, **kwargs)
self.enc2_2_reduce = _ConvBNReLU(48, 32, 1, **kwargs)
self.enc2_3_reduce = _ConvBNReLU(48, 32, 1, **kwargs)
self.conv_fusion = _ConvBNReLU(32, 32, 1, **kwargs)
self.fca_1_reduce = _ConvBNReLU(192, 32, 1, **kwargs)
self.fca_2_reduce = _ConvBNReLU(192, 32, 1, **kwargs)
self.fca_3_reduce = _ConvBNReLU(192, 32, 1, **kwargs)
self.conv_out = nn.Conv2d(32, nclass, 1)
self.__setattr__(
"exclusive",
[
"enc2_2",
"enc3_2",
"enc4_2",
"fca_2",
"enc2_3",
"enc3_3",
"enc3_4",
"fca_3",
"enc2_1_reduce",
"enc2_2_reduce",
"enc2_3_reduce",
"conv_fusion",
"fca_1_reduce",
"fca_2_reduce",
"fca_3_reduce",
"conv_out",
],
)
def __init__(self,
num_classes=1000,
multiplier=1.0,
norm_layer=nn.BatchNorm2d,
**kwargs):
super(MobileNetV2, self).__init__()
inverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1]
]
# building first layer
input_channels = int(32 * multiplier) if multiplier > 1.0 else 32
last_channels = int(1280 * multiplier) if multiplier > 1.0 else 1280
features = [
_ConvBNReLU(3,
input_channels,
3,
2,
1,
relu6=True,
norm_layer=norm_layer)
]
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
out_channels = int(c * multiplier)
for i in range(n):
stride = s if i == 0 else 1
features.append(
InvertedResidual(input_channels, out_channels, stride, t,
norm_layer))
input_channels = out_channels
# building last several layers
features.append(
_ConvBNReLU(input_channels,
last_channels,
1,
relu6=True,
norm_layer=norm_layer))
features.append(nn.AdaptiveAvgPool2d(1))
self.features = nn.Sequential(*features)
self.classifier = nn.Sequential(nn.Dropout2d(0.2),
nn.Linear(last_channels, num_classes))
# weight initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.zeros_(m.bias)
def __init__(self, backbone='resnet18', pretrained_base=True, norm_layer=nn.BatchNorm2d, **kwargs):
super(ContextPath, self).__init__()
self.backbone = backbone
if backbone == 'resnet18':
pretrained = resnet18(pretrained=pretrained_base, **kwargs)
elif backbone == 'resnet50':
pretrained = resnet50(pretrained=pretrained_base, **kwargs)
elif backbone == 'xception':
pretrained = get_xception_71(pretrained=pretrained_base, **kwargs)
elif backbone == 'mobilenet':
pretrained = get_mobilenet_v2(pretrained=pretrained_base, **kwargs)
else:
raise RuntimeError('unknown backbone: {}'.format(backbone))
if backbone == 'xception':
self.conv1 = pretrained.conv1
self.bn1 = pretrained.bn1
self.relu = pretrained.relu
self.conv2 = pretrained.conv2
self.bn2 = pretrained.bn2
self.block1 = pretrained.block1
self.block2_1 = pretrained.block2_1
self.block2_2 = pretrained.block2_2
self.block2 = pretrained.block2
self.block3 = pretrained.block3
self.midflow = pretrained.midflow
self.block20 = pretrained.block20
self.conv3 = pretrained.conv3
self.bn3 = pretrained.bn3
self.conv4 = pretrained.conv4
self.bn4 = pretrained.bn4
self.conv5 = pretrained.conv5
self.bn5 = pretrained.bn5
self.avgpool = pretrained.avgpool
self.fc = pretrained.fc
elif backbone == 'mobilenet':
self.down8 = pretrained.down8
self.down16 = pretrained.down16
self.down32 = pretrained.down32
else :
self.conv1 = pretrained.conv1
self.bn1 = pretrained.bn1
self.relu = pretrained.relu
self.maxpool = pretrained.maxpool
self.layer1 = pretrained.layer1
self.layer2 = pretrained.layer2
self.layer3 = pretrained.layer3
self.layer4 = pretrained.layer4
inter_channels = 128
in_channels = 512
second_in_channels = 256
third_in_channels = 728
if backbone == 'resnet50' or backbone == 'xception':
in_channels = 2048
second_in_channels = 728
elif backbone == 'mobilenet':
in_channels = 1280
second_in_channels = 96
self.global_context = _GlobalAvgPooling(in_channels, inter_channels, norm_layer)
self.arms = nn.ModuleList(
[AttentionRefinmentModule(in_channels, inter_channels, norm_layer, **kwargs),
AttentionRefinmentModule(second_in_channels, inter_channels, norm_layer, **kwargs),
AttentionRefinmentModule(third_in_channels, inter_channels, norm_layer, **kwargs)]
)
self.refines = nn.ModuleList(
[_ConvBNReLU(inter_channels, inter_channels, 3, 1, 1, norm_layer=norm_layer),
_ConvBNReLU(inter_channels, inter_channels, 3, 1, 1, norm_layer=norm_layer),
_ConvBNReLU(inter_channels, inter_channels, 3, 1, 1, norm_layer=norm_layer)]
)