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,
nclass,
aux,
backbone='resnet50',
jpu=False,
pretrained_base=True,
**kwargs):
super(SegBaseModel, self).__init__()
dilated = False if jpu else True
self.aux = aux
self.nclass = nclass
if backbone == 'resnet50':
self.pretrained = resnet50_v1s(pretrained=pretrained_base,
dilated=dilated,
**kwargs)
elif backbone == 'resnet18':
self.pretrained = resnet18(pretrained=pretrained_base, **kwargs)
elif backbone == 'resnet101':
self.pretrained = resnet101_v1s(pretrained=pretrained_base,
dilated=dilated,
**kwargs)
elif backbone == 'resnet152':
self.pretrained = resnet152_v1s(pretrained=pretrained_base,
dilated=dilated,
**kwargs)
else:
raise RuntimeError('unknown backbone: {}'.format(backbone))
self.jpu = JPU([512, 1024, 2048], width=512, **kwargs) if jpu else None
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)]
)