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

    • The result is a bias of shape (B, out_channels)

    • This bias is broadcast over the output and added as a per-channel bias

🧩 Architecture (1D/2D)

x → Conv1d/2d → y
c → ContextProcessor → bias
y + bias → 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.