contextual-conv 0.3.0

PyTorch convolutional layers with global context conditioning

ContextualConv  

ContextualConv – PyTorch convolutional layers with global context conditioning: per‑channel bias, scale, or FiLM‑style scale + bias.

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🚀 Quick start

from contextual_conv import ContextualConv2d
import torch

# FiLM‑style (scale + bias)
conv = ContextualConv2d(
    in_channels=16,
    out_channels=32,
    kernel_size=3,
    padding=1,
    context_dim=10,   # size of global vector c
    h_dim=64,         # optional MLP hidden dim
    use_scale=True,   # γ(c)
    use_bias=True     # β(c)
)

x = torch.randn(8, 16, 32, 32)  # feature map
c = torch.randn(8, 10)          # context vector

out = conv(x, c)  # shape: (8, 32, 32, 32)

Modes at a glance

use_scale	use_bias	Behaviour
False	True	Contextual bias (original behaviour)
True	False	Per‑channel scale only
True	True	FiLM – scale and bias

If both flags are False, the constructor raises ValueError.

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🔧 Key features

• ⚙️ Drop‑in replacement for nn.Conv1d / nn.Conv2d
  → Same arguments + optional context options.
• 🧠 Global vector conditioning via learnable γ(c) and/or β(c).
• 🪶 Lightweight – one small MLP (or single Linear) per layer.
• 🧑‍🔬 FiLM ready – reproduce Feature‑wise Linear Modulation with two lines.
• 🧩 Modular – combine with any architecture, works on CPU / GPU.
• ✅ Unit‑tested and documented.

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📦 Installation

pip install contextual-conv  # coming soon: v0.3.0

Or install from source:

git clone https://github.com/abbassix/ContextualConv.git
cd ContextualConv
pip install -e .[dev]

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📐 Context vector details

• Shape: (B, context_dim)
  (one global descriptor per sample – class label embedding, latent code, etc.)
• Processed by a ContextProcessor:
  • Linear(context_dim, out_dim) (bias‑only / scale‑only)
  • Small MLP if h_dim is set.
• Output dims:
  • out_channels → bias or scale
  • 2 × out_channels → FiLM (scale + bias)

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🧪 Running tests

pytest tests/

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📘 Documentation

Full API reference & tutorials: https://contextualconv.readthedocs.io

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🤝 Contributing

Bug reports, feature requests, and PRs are welcome! See CONTRIBUTING.md.

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📄 License

GNU GPLv3 – see LICENSE file for details.