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.
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)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.
Clone and install:
git clone https://github.com/tomrunia/PyTorchSteerablePyramid.git
cd PyTorchSteerablePyramid
pip install -r requirements.txt
python setup.py install- 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.
- J. Portilla and E.P. Simoncelli, Complex Steerable Pyramid (IJCV, 2000)
- The Steerable Pyramid
- Official implementation: matPyrTools
- perceptual repository by Dzung Nguyen
MIT License
Copyright (c) 2018 Tom Runia (tomrunia@gmail.com)
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