# FAQ

### ❓ What is the context vector used for?

The context vector is a global input (e.g., class label, latent code) that is transformed into a **per-channel bias**, **per-channel scale**, or both (FiLM-style). These parameters are applied uniformly across all spatial (Conv2d) or temporal (Conv1d) positions in the output.

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### ❓ How is the context processed?

The context is passed through a `ContextProcessor`:
- If `h_dim` is not set → a single linear layer:  
  ```python
  Linear(context_dim, out_channels)
  ```
- If `h_dim` is set → a two-layer MLP with ReLU:
  ```python
  Linear(context_dim, h_dim) → ReLU → Linear(h_dim, out_channels)
  ```
- If both scale and bias are used, the processor outputs `2 * out_channels`, split into `γ` and `β`.

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### ❓ Does the context affect each spatial location differently?

No — the same per-channel parameters (scale and/or bias) are **broadcast** across all positions. The context acts globally.

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### ❓ What happens if I don’t provide `context_dim`?

The layer behaves exactly like a standard `nn.Conv1d` or `nn.Conv2d`.

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### ❓ Can this replace SE blocks or FiLM?

Yes. ContextualConv supports **FiLM-style** modulation (`y = γ(c) * conv(x) + β(c)`) and is a lightweight alternative to SE blocks or conditional normalization.

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### ❓ Is it compatible with grouped/depthwise convolutions?

Yes. The same context scale/bias is shared across groups, respecting the weight-sharing behavior of grouped convolutions.