How It Works

🧠 ContextualConv Layers

ContextualConv1d and ContextualConv2d extend standard convolutions by allowing the output to be modulated by a global context vector.

🔄 Forward Pass

1. Input tensor x is passed through a standard nn.Conv1d or nn.Conv2d layer.

2. If a context vector c is provided:

   • It is passed through a shared ContextProcessor module:

     • If h_dim is not set: just a Linear(context_dim → out_channels)

     • If h_dim is set: an MLP Linear → ReLU → Linear

     • If both scale and bias are used: output is 2 * out_channels and split into γ and β

   • The result is a scale (γ) and/or bias (β) of shape (B, out_channels)

   • These parameters are broadcast and applied to the conv output:

y = γ(c) * conv(x) + β(c)

🧩 Architecture (1D/2D)

x → Conv1d/2d → y
c → ContextProcessor → γ, β
y → γ * y + β → final output

✅ Notes

• The context vector is global — it does not vary across spatial/temporal locations.

• The ContextProcessor is shared across all locations and all groups (if used).

• If context_dim is not set, the layer behaves like a standard convolution.