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Merged
harisreedhar merged 1 commit into from
Oct 9, 2026
Merged

MaskNet support for 512 and 1024 resolution #103

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81 changes: 65 additions & 16 deletions hyperswap/src/networks/masknet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,39 +12,88 @@ def __init__(self, config_parser : ConfigParser) -> None:
self.config_input_channels = config_parser.getint('training.model.masker', 'input_channels')
self.config_output_channels = config_parser.getint('training.model.masker', 'output_channels')
self.config_num_filters = config_parser.getint('training.model.masker', 'num_filters')
self.down_samples = self.create_down_samples(self.config_input_channels, self.config_num_filters)
self.up_samples = self.create_up_samples(self.config_num_filters)
self.config_output_size = config_parser.getint('training.model.generator', 'output_size')
self.scale_down_samples = self.create_scale_down_samples()
self.base_down_samples = self.create_base_down_samples()
self.base_up_samples = self.create_base_up_samples()
self.scale_up_samples = self.create_scale_up_samples()
self.bottleneck = BottleNeck(self.config_num_filters * 4)
self.conv = nn.Conv2d(self.config_num_filters, self.config_output_channels, kernel_size = 1)
self.sigmoid = nn.Sigmoid()

@staticmethod
def create_down_samples(input_channels : int, num_filters : int) -> nn.ModuleList:
return nn.ModuleList(
def create_scale_down_samples(self) -> nn.ModuleList:
scale_down_samples = nn.ModuleList(
[
DownSample(input_channels, num_filters),
DownSample(num_filters, num_filters * 2),
DownSample(num_filters * 2, num_filters * 4)
DownSample(self.config_input_channels, self.config_num_filters)
])

@staticmethod
def create_up_samples(num_filters : int) -> nn.ModuleList:
return nn.ModuleList(
if self.config_output_size == 512:
scale_down_samples.extend(
[
DownSample(self.config_num_filters, self.config_num_filters)
])

if self.config_output_size == 1024:
scale_down_samples.extend(
[
DownSample(self.config_num_filters, self.config_num_filters),
DownSample(self.config_num_filters, self.config_num_filters)
])

return scale_down_samples

def create_base_down_samples(self) -> nn.ModuleList:
base_down_samples = nn.ModuleList(
[
DownSample(self.config_num_filters, self.config_num_filters * 2),
DownSample(self.config_num_filters * 2, self.config_num_filters * 4)
])

return base_down_samples

def create_base_up_samples(self) -> nn.ModuleList:
base_up_samples = nn.ModuleList(
[
UpSample(num_filters * 4, num_filters * 2),
UpSample(num_filters * 2, num_filters),
UpSample(num_filters, num_filters)
UpSample(self.config_num_filters * 4, self.config_num_filters * 2),
UpSample(self.config_num_filters * 2, self.config_num_filters),
UpSample(self.config_num_filters, self.config_num_filters)
])

return base_up_samples

def create_scale_up_samples(self) -> nn.ModuleList:
scale_up_samples = nn.ModuleList()

if self.config_output_size == 512:
scale_up_samples.extend(
[
UpSample(self.config_num_filters, self.config_num_filters)
])

if self.config_output_size == 1024:
scale_up_samples.extend(
[
UpSample(self.config_num_filters, self.config_num_filters),
UpSample(self.config_num_filters, self.config_num_filters)
])

return scale_up_samples

def forward(self, input_tensor : Tensor, input_feature : Feature) -> Mask:
output_mask = torch.cat([ input_tensor, input_feature ], dim = 1)

for down_sample in self.down_samples:
for down_sample in self.scale_down_samples:
output_mask = down_sample(output_mask)

for down_sample in self.base_down_samples:
output_mask = down_sample(output_mask)

output_mask = self.bottleneck(output_mask)

for up_sample in self.up_samples:
for up_sample in self.base_up_samples:
output_mask = up_sample(output_mask)

for up_sample in self.scale_up_samples:
output_mask = up_sample(output_mask)

output_mask = self.conv(output_mask)
Expand Down
6 changes: 5 additions & 1 deletion hyperswap/tests/test_networks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,11 +33,15 @@ def test_aad_with_unet(output_size : int) -> None:
assert output_tensor.shape == (1, 3, output_size, output_size)


@pytest.mark.parametrize('output_size', [ 256, 512 ])
@pytest.mark.parametrize('output_size', [ 256, 512, 1024 ])
def test_mask_net(output_size : int) -> None:
config_parser = ConfigParser()
config_parser.read_dict(
{
'training.model.generator':
{
'output_size': str(output_size)
},
'training.model.masker':
{
'input_channels': '67',
Expand Down
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