def seresnext101_unet_v2(input_channels=6,
num_classes=5,
dropout=0.0,
pretrained=True,
classifiers=True):
encoder = E.SEResNeXt101Encoder(pretrained=pretrained,
layers=[0, 1, 2, 3, 4])
return UnetV2SegmentationModel(
encoder,
num_classes=num_classes,
disaster_type_classes=len(DISASTER_TYPES) if classifiers else None,
damage_type_classes=len(DAMAGE_TYPES) if classifiers else None,
unet_channels=[128, 128, 256, 384],
dropout=dropout,
abn_block=partial(ABN, activation=ACT_RELU),
)
def seresnext101_fpnsum256(num_classes=5, dropout=0.0, pretrained=True):
encoder = E.SEResNeXt101Encoder(pretrained=pretrained)
return FPNSumSegmentationModel(encoder, num_classes=num_classes, fpn_channels=256, dropout=dropout)
def seresnext101_deeplab256(num_classes=1, dropout=0.0):
encoder = E.SEResNeXt101Encoder()
return DeeplabV3SegmentationModel(encoder,
num_classes=num_classes,
high_level_bottleneck=256,
dropout=dropout)