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Add optional BatchNorm layers for the Unet architecture #7

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Add optional BatchNorm layers for the Unet architecture #7

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21 changes: 19 additions & 2 deletions funlib/learn/torch/models/unet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@ def __init__(
out_channels,
kernel_sizes,
activation,
batch_norm=False,
padding='valid'):

super(ConvPass, self).__init__()
Expand Down Expand Up @@ -44,7 +45,21 @@ def __init__(
kernel_size,
padding=pad))
except KeyError:
raise RuntimeError("%dD convolution not implemented" % self.dims)
raise RuntimeError(
"%dD convolution not implemented" % self.dims)

if batch_norm:
try:
bn = {
2: torch.nn.BatchNorm2d,
3: torch.nn.BatchNorm3d
}[self.dims]

layers.append(
bn(out_channels))
except KeyError:
raise RuntimeError(
"BatchNorm%dD not implemented" % self.dims)

in_channels = out_channels

Expand Down Expand Up @@ -128,7 +143,6 @@ def __init__(
stride=scale_factor)

else:

self.up = torch.nn.Upsample(
scale_factor=scale_factor,
mode=mode)
Expand Down Expand Up @@ -234,6 +248,7 @@ def __init__(
kernel_size_down=None,
kernel_size_up=None,
activation='ReLU',
batch_norm=False,
fov=(1, 1, 1),
voxel_size=(1, 1, 1),
num_fmaps_out=None,
Expand Down Expand Up @@ -373,6 +388,7 @@ def __init__(
num_fmaps*fmap_inc_factor**level,
kernel_size_down[level],
activation=activation,
batch_norm=batch_norm,
padding=padding)
for level in range(self.num_levels)
])
Expand Down Expand Up @@ -410,6 +426,7 @@ def __init__(
else num_fmaps_out,
kernel_size_up[level],
activation=activation,
batch_norm=batch_norm,
padding=padding)
for level in range(self.num_levels - 1)
])
Expand Down