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Conditioning for MLP architecture #16

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Conditioning for MLP architecture #16

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cceabf2
implemented conditioning mlp
jorgesarrato Mar 5, 2025
7547a01
Add notebook for conditioned mlp
jorgesarrato Mar 5, 2025
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482 changes: 482 additions & 0 deletions notebooks/two_moons_demo_conditioned.ipynb
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14 changes: 10 additions & 4 deletions score_models/architectures/mlp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ def __init__(
raise ValueError(f"Conditioning must be in ['None', 'Input'], received {c}")
if c.lower() != "none":
self.conditioned = True
if conditioning_channels is not None:
if conditioning_channels is None:
raise ValueError("conditioning_channels must be provided when the network is conditioned")
elif c.lower() == "none" and self.conditioned:
raise ValueError(f"Cannot have a mix of 'None' and other type of conditioning, received the list {conditioning}")
Expand Down Expand Up @@ -57,7 +57,13 @@ def __init__(
for _ in range(time_branch_layers):
modules.append(nn.Linear(t_dim, t_dim))
# main branch
modules.append(nn.Linear(dimensions+t_dim, units))
if self.conditioned:
total_cond_channels = 0
for chan in conditioning_channels:
total_cond_channels+=chan
modules.append(nn.Linear(dimensions+t_dim+total_cond_channels, units))
else:
modules.append(nn.Linear(dimensions+t_dim, units))
if bottleneck is not None:
assert isinstance(bottleneck, int)
self.bottleneck = bottleneck
Expand All @@ -79,15 +85,15 @@ def __init__(
self.act = get_activation(activation)
self.all_modules = nn.ModuleList(modules)

def forward(self, t, x):
def forward(self, t, x, *args):
B, D = x.shape
modules = self.all_modules
temb = modules[0](t)
i = 1
for _ in range(self.time_branch_layers):
temb = self.act(modules[i](temb))
i += 1
x = torch.cat([x, temb], dim=1)
x = torch.cat([x, temb, *args], dim=1)
x = modules[i](x)
i += 1
for _ in range(self.layers//2):
Expand Down