def __init__(self, in_channels, out_channels, norm_layer):
super(DANetHead, self).__init__()
inter_channels = in_channels // 4
self.conv5a = nn.Sequential(
nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
norm_layer(inter_channels), nn.ReLU())
self.conv5c = nn.Sequential(
nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
norm_layer(inter_channels), nn.ReLU())
self.sa = PAM_Module(inter_channels)
self.sc = CAM_Module(inter_channels)
self.conv51 = nn.Sequential(
nn.Conv2d(inter_channels, inter_channels, 3, padding=1,
bias=False), norm_layer(inter_channels), nn.ReLU())
self.conv52 = nn.Sequential(
nn.Conv2d(inter_channels, inter_channels, 3, padding=1,
bias=False), norm_layer(inter_channels), nn.ReLU())
self.conv6 = nn.Sequential(nn.Dropout2d(0.1, False),
nn.Conv2d(512, out_channels, 1))
self.conv7 = nn.Sequential(nn.Dropout2d(0.1, False),
nn.Conv2d(512, out_channels, 1))
self.conv8 = nn.Sequential(nn.Dropout2d(0.1, False),
nn.Conv2d(512, out_channels, 1))
def __init__(self, BAND_A, BAND_B, classes):
super(SAR_simple, self).__init__()
self.name = 'sar_simple_'
# sar
self.conv21 = nn.Sequential(
nn.Conv2d(BAND_B, 32, kernel_size=3, padding=0),
nn.BatchNorm2d(32, momentum=1, affine=True),
mish()
)
self.conv22 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64, momentum=1, affine=True),
mish()
)
self.conv23 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64, momentum=1, affine=True),
mish()
)
self.conv24 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64, momentum=1, affine=True),
mish()
)
self.conv25 = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, padding=1),
nn.BatchNorm2d(32, momentum=1, affine=True),
mish()
)
# fusion
self.batch_norm_optical = nn.Sequential(
nn.BatchNorm2d(32, momentum=1, affine=True),
mish(),
nn.Dropout(p=0.5)
)
self.batch_norm_sar = nn.Sequential(
nn.BatchNorm2d(32, momentum=1, affine=True),
mish(),
nn.Dropout(p=0.5)
)
self.global_pooling = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(32, classes),
# nn.Softmax(dim=-1)
)
self.cam = CAM_Module(32)
self.pam = PAM_Module(32)