mps-correlation 0.1.1

Correlation layer for optical flow on Apple Silicon (MPS)

MPS Correlation

Correlation layer for optical flow on Apple Silicon (M1/M2/M3/M4).

Drop-in replacement for spatial-correlation-sampler and mmcv's correlation op.

Why?

Correlation layers are essential for optical flow estimation:

• RAFT: State-of-the-art optical flow
• PWC-Net: Efficient optical flow
• FlowNet/FlowNet2: Classic deep optical flow

But existing implementations are CUDA-only. On Mac you get:

NotImplementedError: correlation not implemented for MPS

This package provides a native Metal implementation.

Installation

pip install mps-correlation

Or from source:

git clone https://github.com/mpsops/mps-correlation
cd mps-correlation
pip install -e .

Quick Start

Basic Usage

import torch
from mps_correlation import correlation

# Two feature maps from consecutive frames
fmap1 = torch.randn(1, 256, 64, 64, device='mps')
fmap2 = torch.randn(1, 256, 64, 64, device='mps')

# Compute correlation volume
corr = correlation(
    fmap1, fmap2,
    kernel_size=1,
    max_displacement=4,
    stride1=1,
    stride2=1,
    pad_size=4
)
# Output: (1, 81, 64, 64) - 81 = (2*4+1)^2 displacement channels

Correlation Module

from mps_correlation import Correlation

corr_layer = Correlation(
    kernel_size=1,
    max_displacement=4,
    stride1=1,
    stride2=1,
    pad_size=4
)

corr = corr_layer(fmap1, fmap2)

RAFT-style All-Pairs Correlation

from mps_correlation import CorrBlock

# Build correlation pyramid
corr_block = CorrBlock(fmap1, fmap2, num_levels=4, radius=4)

# Lookup at specific coordinates
coords = torch.zeros(1, 2, 64, 64, device='mps')  # (x, y) coordinates
corr_features = corr_block(coords)

API Reference

correlation(input1, input2, kernel_size, max_displacement, stride1, stride2, pad_size, is_multiply)

Parameter	Type	Description
input1	Tensor	First feature map (N, C, H, W)
input2	Tensor	Second feature map (N, C, H, W)
kernel_size	int	Size of correlation kernel (default: 1)
max_displacement	int	Maximum displacement to search (default: 4)
stride1	int	Stride for input1 (default: 1)
stride2	int	Stride for displacement (default: 1)
pad_size	int	Padding size (default: 4)
is_multiply	bool	Use multiplication (True) or subtraction (False)

CorrBlock

RAFT-style correlation block with pyramid and lookup.

How It Works

Correlation computes similarity between patches at different displacements:

For each position (x, y) in output:
    For each displacement (dx, dy) in [-max_disp, max_disp]:
        corr[x, y, dx, dy] = sum(fmap1[x, y, :] * fmap2[x+dx, y+dy, :])

This creates a 4D cost volume that optical flow networks use to estimate motion.

Compatibility

• PyTorch: 2.0+
• macOS: 12.0+ (Monterey)
• Hardware: Apple Silicon (M1/M2/M3/M4)

Features

• Full forward and backward pass (training supported)
• fp32 and fp16 supported
• Compatible with RAFT, PWC-Net, FlowNet architectures

Credits

• spatial-correlation-sampler - Reference implementation
• RAFT - State-of-the-art optical flow
• PWC-Net - Efficient optical flow

License

MIT