mps-deform-conv 0.1.0

Deformable Convolution 2D for PyTorch on Apple Silicon (MPS)

MPS Deformable Convolution

Deformable Convolution 2D for PyTorch on Apple Silicon (M1/M2/M3/M4).

Drop-in replacement for torchvision.ops.deform_conv2d that actually works on MPS.

Why?

Deformable convolutions are used everywhere:

• Detection: DETR, Deformable DETR, mmdetection models
• Video: BasicVSR++, EDVR, optical flow models
• Segmentation: Mask R-CNN with DCN backbones

But torchvision's implementation is CUDA-only. On Mac you get:

NotImplementedError: deform_conv2d not implemented for MPS

This package provides a native Metal implementation.

Installation

pip install mps-deform-conv

Or from source:

git clone https://github.com/imperatormk/mps-deform-conv
cd mps-deform-conv
pip install -e .

Quick Start

Basic Usage

import torch
from mps_deform_conv import deform_conv2d

# Input: (batch, channels, height, width)
input = torch.randn(1, 64, 32, 32, device='mps')

# Weight: (out_channels, in_channels, kernel_h, kernel_w)
weight = torch.randn(64, 64, 3, 3, device='mps')

# Offset: (batch, 2 * kernel_h * kernel_w, out_h, out_w)
# 2 values (dy, dx) for each position in the 3x3 kernel
offset = torch.randn(1, 2*9, 32, 32, device='mps')

# Run deformable convolution
output = deform_conv2d(input, offset, weight, padding=(1, 1))

DeformConv2d Module

from mps_deform_conv import DeformConv2d

# Create layer
conv = DeformConv2d(
    in_channels=64,
    out_channels=128,
    kernel_size=3,
    padding=1
).to('mps')

# Forward pass requires input and offset
x = torch.randn(1, 64, 32, 32, device='mps')
offset = torch.randn(1, 2*9, 32, 32, device='mps')
output = conv(x, offset)

ModulatedDeformConv2d (DCNv2)

Includes the offset predictor - a complete conv layer replacement:

from mps_deform_conv import ModulatedDeformConv2d

# DCNv2 with internal offset/mask prediction
conv = ModulatedDeformConv2d(
    in_channels=64,
    out_channels=128,
    kernel_size=3,
    padding=1
).to('mps')

# Just pass input - offsets learned internally
x = torch.randn(1, 64, 32, 32, device='mps')
output = conv(x)

API Reference

deform_conv2d(input, offset, weight, bias, stride, padding, dilation, mask)

Functional interface matching torchvision.ops.deform_conv2d.

Parameter	Type	Description
input	Tensor	Input tensor (N, C_in, H, W)
offset	Tensor	Offset tensor (N, 2*K*K*groups, H_out, W_out)
weight	Tensor	Weight tensor (C_out, C_in/groups, K, K)
bias	Tensor	Optional bias (C_out,)
stride	tuple	Convolution stride (default: (1, 1))
padding	tuple	Input padding (default: (0, 0))
dilation	tuple	Kernel dilation (default: (1, 1))
mask	Tensor	Optional DCNv2 mask (N, K*K*groups, H_out, W_out)

DeformConv2d

Module wrapping deform_conv2d. Takes input and offset in forward.

ModulatedDeformConv2d

Self-contained DCNv2 module with internal offset prediction. Takes only input in forward.

How It Works

Standard convolution samples on a fixed grid:

[•] [•] [•]
[•] [x] [•]
[•] [•] [•]

Deformable convolution learns offsets to sample from arbitrary positions:

    [•]
[•]     [•]
    [x]     [•]
[•]     [•]
    [•]

This lets the network adapt its receptive field to the input content - useful for detecting objects at different scales, handling geometric transformations, etc.

Compatibility

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

Features

• Full forward and backward pass (training supported)
• Gradients verified against torchvision (< 0.00001 error)
• fp32 and fp16 supported
• Grouped convolutions supported

Credits

• torchvision - Reference CUDA implementation
• Deformable Convolutional Networks - Original paper
• Deformable ConvNets v2 - Modulated DCN

License

MIT