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multi-source-convolution

A PyTorch 2d convolution block that simultaneously takes image, vector, and scalar data.

Motivation

What if an image comes with useful scalar metadata? For example, in image signal processing, each raw image comes with a camera ISO scalar, which can be useful. How can we make a CNN simultaneously take image and scalar data?

MSConv2d can be used out of the box and is faster than the naive tile_concat.py.

Tile, Concatenate, and Convolve

In tile_concat.py, I implement a naive version of tile-concat-conv paradigm. This allows us to combine scalars, vectors, and images into the same input tensor.

Tile

  • [N, C1, H, W] image: remain the same
  • [N, C2, H] vector: unsqueeze to [N, C2, H, 1], tile to [N, C2, H, W]
  • [N, C3, W] vector: unsqueeze to [N, C3, 1, W], tile to [N, C3, H, W]
  • [N, C4] scalar: unsqueeze to [N, C4, 1, 1], tile to [N, C4, H, W]

Tiling is done with torch.expand to avoid copying the underlying tensors in memory.

Concatenate

The 4 resulting tensors from above are concatenated into a single [N, C1+C2+C3+C4, H, W] tensor.

Convolve

The resulting [N, C1+C2+C3+C4, H, W] tensor is passed to a classic nn.Conv2d convolution block.

Applications

This tile-concat-conv operation can be performed at the beginning, in the middle, and at the end of a network. The [N, C4] input scalars can come from an MLP or a text embedding network, not necessarily some raw input scalars.

Should we flatten vectors?

It's also possible to flatten [N, C2, H] to [N, C2 * H] and [N, C3, W] to [N, C3 * W] and treat them as scalar inputs. If the vectors indeed encode row-wise or column-wise metadata, this flattening typically worsens performance.

Multi-source Convolution

This is my main contribution based on the original formula for convolution. It also implements tile-concat-conv paradigm while being faster and more memory-efficient.

Let C be the number of output channels, MSConv2d handles each input tensors as follows:

  • [N, C1, H, W] image: 2d convolution along H and W, produces [N, C, H, W]
  • [N, C2, H] vector: 1d convolution along H, produces [N, C, H]
  • [N, C3, W] vector: 1d convolution along H, produces [N, C, W]
  • [N, C4] scalar: fully connected layer (nn.Linear), produces [N, C]

The kernel sizes are adjusted accordingly for the 1d convolutions. Also, only one of the above layers need bias.

These output tensors then unsqueeze to [N, C, H, W], [N, C, H, 1], [N, C, 1, W], [N, C, 1, 1] respectively. They are then added with broadcasting, producing [N, C, H, W] output.

The core design comes from the observation that after naive tiling, the input tensor to the nn.Conv2d block will have lots of constant channels (all elements in the channels are the same). In that case, kernel_size * kernel_size parameters convolve with a constant receptive field, so only 1 parameter is necessary to represent the same computation. This saves lots of computation time and nn.Conv2d weights.

Equivalence and Efficiency

For any set of nn.Conv2d weights in a vanilla tile-concat-conv architecture, there's a MSConv2d block with weights that result in the same inference-time computational output. However, this doesn't guarantee that MSConv2d will learn these equivalent weights when it's in the same position as nn.Conv2d with the same hyperparameters following tile_and_concatenate. Let me know if there are significant differences. I haven't found any.

I like to compare this to quadratic forms $x^TAx$. We always assume that $A$ is a symmetric matrix, because in the context of quadratic forms, even if $A$ is not symmetric, we can easily produce $(A+A^T)/2$, which gives the same output for all $x$.

MSConv2d is maximally efficient because the scalars and vectors don't need to repeatedly go through convolution kernels.