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Generelize UNet: custom # of input channels (instead of default 3) #297

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Oct 28, 2020
Merged

Generelize UNet: custom # of input channels (instead of default 3) #297

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a34a326
custom input channels
annikabrundyn Oct 22, 2020
39c42c0
Update pl_bolts/models/vision/unet.py
annikabrundyn Oct 25, 2020
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4 changes: 3 additions & 1 deletion pl_bolts/models/vision/unet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,21 +17,23 @@ class UNet(nn.Module):

Args:
num_classes: Number of output classes required
input_channels: Number of channels in input images (default 3)
num_layers: Number of layers in each side of U-net (default 5)
features_start: Number of features in first layer (default 64)
bilinear (bool): Whether to use bilinear interpolation or transposed convolutions (default) for upsampling.
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"""
def __init__(
self,
num_classes: int,
input_channels: int = 3,
num_layers: int = 5,
features_start: int = 64,
bilinear: bool = False
):
super().__init__()
self.num_layers = num_layers

layers = [DoubleConv(3, features_start)]
layers = [DoubleConv(input_channels, features_start)]

feats = features_start
for _ in range(num_layers - 1):
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