GitHub - JoHof/volresample: Fast Cython/OpenMP-powered 3D volume resampling for NumPy, with PyTorch- and SciPy-compatible nearest, linear, area, cubic, and grid sampling on CPU.

Fast 3D volume resampling with Cython and OpenMP parallelization.

Implemented against PyTorch's F.interpolate and F.grid_sample as a reference, producing identical results for nearest, linear, and area modes. The cubic mode matches scipy.ndimage.zoom(order=3, mode='reflect'), using grid_mode=True when align_corners=False and grid_mode=False when align_corners=True. Can be used as a drop-in replacement when PyTorch or SciPy is not available or when better performance is desired on CPU.

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Features

• Cython-optimized with OpenMP parallelization
• Simple API: resample() and grid_sample()
• Interpolation modes: nearest, linear, area, and cubic
• Supports 3D, 4D (multi-channel), and 5D (batched multi-channel) volumes
• Supports align_corners=True for linear and cubic resampling
• Supports uint8, int16 (nearest) and float32 dtypes (all other modes)

Installation

Or build from source:

git clone https://github.com/JoHof/volresample.git
cd volresample
uv sync

Quick Start

Basic Resampling

import numpy as np
import volresample

# Create a 3D volume
volume = np.random.rand(128, 128, 128).astype(np.float32)

# Resample to a different size
resampled = volresample.resample(volume, (64, 64, 64), mode='linear')
print(resampled.shape)  # (64, 64, 64)

Cubic Resampling (scipy-compatible)

# Cubic B-spline resampling.
# align_corners=False -> scipy zoom(..., grid_mode=True)
# align_corners=True  -> scipy zoom(..., grid_mode=False)
resampled = volresample.resample(volume, (64, 64, 64), mode='cubic')

Align Corners

# For linear and cubic modes, align_corners=True preserves the corner voxels.
aligned = volresample.resample(volume, (192, 192, 192), mode='linear', align_corners=True)

Multi-Channel Volumes

# 4D volume with 4 channels
volume_4d = np.random.rand(4, 128, 128, 128).astype(np.float32)

# Resample all channels
resampled_4d = volresample.resample(volume_4d, (64, 64, 64), mode='linear')
print(resampled_4d.shape)  # (4, 64, 64, 64)

Batched Multi-Channel Volumes

# 5D volume with batch dimension (N, C, D, H, W)
volume_5d = np.random.rand(2, 4, 128, 128, 128).astype(np.float32)

# Resample all batches and channels
resampled_5d = volresample.resample(volume_5d, (64, 64, 64), mode='linear')
print(resampled_5d.shape)  # (2, 4, 64, 64, 64)

Grid Sampling

# Input volume: (N, C, D, H, W)
input = np.random.rand(2, 3, 32, 32, 32).astype(np.float32)

# Sampling grid with normalized coordinates in [-1, 1]
grid = np.random.uniform(-1, 1, (2, 24, 24, 24, 3)).astype(np.float32)

# Sample with linear interpolation
output = volresample.grid_sample(input, grid, mode='linear', padding_mode='zeros')
print(output.shape)  # (2, 3, 24, 24, 24)

Parallelization

import volresample

# Check default thread count (min of cpu_count and 4)
print(volresample.get_num_threads())  # e.g., 4

# Set custom thread count
volresample.set_num_threads(8)

# All subsequent operations use 8 threads
resampled = volresample.resample(volume, (64, 64, 64), mode='linear')

API Reference

resample(data, size, mode='linear', align_corners=False)

Resample a 3D, 4D, or 5D volume to a new size.

Parameters:

• data (ndarray): Input volume of shape (D, H, W), (C, D, H, W), or (N, C, D, H, W)
• size (tuple): Target size (D_out, H_out, W_out)
• mode (str): Interpolation mode:
  • 'nearest': Nearest neighbor (works with all dtypes)
  • 'linear': Trilinear interpolation (float32 only)
  • 'area': Area-based averaging (float32 only, suited for downsampling)
  • 'cubic': Tricubic B-spline interpolation with IIR prefilter (float32 only). Matches scipy.ndimage.zoom(order=3, mode='reflect')
• align_corners (bool): Only supported for mode='linear' and mode='cubic'
  • False (default): matches PyTorch align_corners=False for linear, and SciPy grid_mode=True for cubic
  • True: matches PyTorch align_corners=True for linear, and SciPy grid_mode=False for cubic
  • Passing align_corners=True with nearest or area raises ValueError

PyTorch correspondence:

align_corners is intentionally limited to the modes where the reference APIs support it: linear and cubic.

SciPy correspondence:

Returns:

• Resampled array with same number of dimensions as input

Supported Dtypes:

• uint8, int16: Only with mode='nearest'
• float32: All modes (nearest, linear, area, cubic)

grid_sample(input, grid, mode='linear', padding_mode='zeros')

Sample input at arbitrary locations specified by a grid.

Parameters:

• input (ndarray): Input volume of shape (N, C, D, H, W)
• grid (ndarray): Sampling grid of shape (N, D_out, H_out, W_out, 3)
  • Values in range [-1, 1] where -1 maps to the first voxel, 1 to the last
• mode (str): 'nearest' or 'linear'
• padding_mode (str): 'zeros', 'border', or 'reflection'

PyTorch correspondence:

The behavior matches PyTorch's grid_sample with align_corners=False.

Returns:

• Sampled array of shape (N, C, D_out, H_out, W_out)

set_num_threads(num_threads)

Set the number of threads used for parallel operations.

Parameters:

• num_threads (int): Number of threads to use (must be >= 1)

get_num_threads()

Get the current number of threads used for parallel operations.

Returns:

• Current thread count (default: min(cpu_count, 4))

Performance

Benchmarks below were produced by the default curated benchmark profile on an Intel i7-8565U using 4 CPU threads:

python tests/benchmark.py --threads 4

The default profile runs for about 30-60 seconds using adaptive repeat counts.

volresample vs PyTorch

volresample vs SciPy

volresample vs PyTorch (grid_sample)

Average speedup across the default benchmark suite: 3.99×.

Notes:

• Cubic mode is validated against SciPy rather than PyTorch. The align_corners flag selects between SciPy's grid_mode=True and grid_mode=False, and both paths are benchmarked above.
• int16 nearest shows the largest speedup because PyTorch must round-trip through float32, while volresample operates directly on int16.
• Area mode remains one of the strongest CPU wins because the implementation parallelizes efficiently over spatial work.
• 4D and 5D coverage is included in the benchmark suite so multi-channel and batched paths are represented, even when the raw speedups are smaller than the single-volume cases.
• These are machine-specific measurements. CPU architecture, memory bandwidth, thermal throttling, and installed library versions can shift the absolute numbers substantially.

Development

Running Tests

# Run all tests
pytest tests/

# Run with PyTorch comparison tests
pip install torch
pytest tests/ -v

# Skip PyTorch tests
pytest tests/ --skip-torch

Running Benchmarks

# Curated default run: all modes plus grid_sample, roughly 30-60 seconds
python tests/benchmark.py

# Faster smoke benchmark
python tests/benchmark.py --profile quick

# Or pin the thread count
python tests/benchmark.py --threads 4

# Output is printed live while the benchmark runs
python -u tests/benchmark.py

Building from Source

pip install -e ".[dev]"
python setup.py build_ext --inplace

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome. Please submit a Pull Request.