Complex Steerable Pyramid in PyTorch

This is a PyTorch implementation of the Complex Steerable Pyramid described in Portilla and Simoncelli (IJCV, 2000).

It uses PyTorch's efficient spectral decomposition layers torch.fft and torch.ifft. Just like a normal convolution layer, the complex steerable pyramid expects a batch of images of shape [N,C,H,W] with current support only for grayscale images (C=1). It returns a list structure containing the low-pass, high-pass and intermediate levels of the pyramid for each image in the batch (as torch.Tensor). Computing the steerable pyramid is significantly faster on the GPU as can be observed from the runtime benchmark below.

Usage

In addition to the PyTorch implementation defined in SCFpyr_PyTorch the original SciPy version is also included in SCFpyr for completeness and comparison. As the GPU implementation highly benefits from parallelization, the cwt and power methods expect signal batches of shape [N,H,W] containing a batch of N images of shape HxW.

from steerable.SCFpyr_PyTorch import SCFpyr_PyTorch
import steerable.utils as utils

# Load batch of images [N,1,H,W]
im_batch_numpy = utils.load_image_batch(...)
im_batch_torch = torch.from_numpy(im_batch_numpy).to(device)

# Requires PyTorch with MKL when setting to 'cpu' 
device = torch.device('cuda:0')

# Initialize Complex Steerbale Pyramid
pyr = SCFpyr_PyTorch(height=5, nbands=4, scale_factor=2, device=device)

# Decompose entire batch of images 
coeff = pyr.build(im_batch_torch)

# Reconstruct batch of images again
im_batch_reconstructed = pyr.reconstruct(coeff)

# Visualization
coeff_single = utils.extract_from_batch(coeff, 0)
coeff_grid = utils.make_grid_coeff(coeff, normalize=True)
cv2.imshow('Complex Steerable Pyramid', coeff_grid)
cv2.waitKey(0)

Benchmark

Performing parallel the CSP decomposition on the GPU using PyTorch results in a significant speed-up. Increasing the batch size will give faster runtimes. The plot below shows a comprison between the scipy versus torch implementation as function of the batch size N and input signal length. These results were obtained on a powerful Linux desktop with NVIDIA Titan X GPU.

Installation

Clone and install:

git clone https://github.com/tomrunia/PyTorchSteerablePyramid.git
cd PyTorchSteerablePyramid
pip install -r requirements.txt
python setup.py install

Requirements

• Python 2.7 or 3.6 (other versions might also work)
• Numpy (developed with 1.15.4)
• Scipy (developed with 1.1.0)
• PyTorch >= 0.4.0 (developed with 1.0.0; see note below)

The steerable pyramid depends utilizes torch.fft and torch.ifft to perform operations in the spectral domain. At the moment, PyTorch only implements these operations for the GPU or with the MKL back-end on the CPU. Therefore, if you want to run the code on the CPU you might need to compile PyTorch from source with MKL enabled. Use torch.backends.mkl.is_available() to check if MKL is installed.

References

• J. Portilla and E.P. Simoncelli, Complex Steerable Pyramid (IJCV, 2000)
• The Steerable Pyramid
• Official implementation: matPyrTools
• perceptual repository by Dzung Nguyen

License

MIT License

Copyright (c) 2018 Tom Runia ([email protected])

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.