dctorch

library0.1.2pypypiunverified

DCTorch is a Python library providing fast discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) implementations optimized for PyTorch tensors. It enables efficient frequency domain analysis and manipulation within deep learning models, supporting 2D and 3D transforms. The current version is 0.1.2, and it appears to be actively maintained with recent commits.

ai-ml data

pip install dctorch

package ↗

INSTALL

IMPORT

SIG · DCTORCH

dctorch

ai-mlpythonv0.1.2

Install

81.2s avg

Import

—

Disk

4983MB

Pass rate

3/ 10

Env Coverage3 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.1.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 87.8s

py 3.11

✕ build_error

✓ 84.15s

py 3.12

✕ build_error

✓ 71.55s

py 3.13

✕ build_error

py 3.9

✕ build_error

✕ timeout

4983MB installed

● package 4983MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

dct_2d

✓ from dctorch import dct_2d

✗ from dctorch import dct_2d

This quickstart demonstrates how to perform 2D Discrete Cosine Transform (DCT) and its inverse (IDCT) on a PyTorch tensor. It includes device selection for GPU acceleration and verifies the reconstruction accuracy.

import torch from dctorch import dct_2d, idct_2d # Determine device (CPU or CUDA if available) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # Create a random tensor (Batch size, Channels, Height, Width) x = torch.randn(1, 3, 224, 224, device=device) print(f"Original tensor shape: {x.shape}") # Perform 2D Discrete Cosine Transform y = dct_2d(x) print(f"DCT transformed tensor shape: {y.shape}") # Perform Inverse 2D Discrete Cosine Transform x_recon = idct_2d(y) print(f"Reconstructed tensor shape: {x_recon.shape}") # Verify reconstruction accuracy reconstruction_error = torch.norm(x - x_recon).item() print(f"Reconstruction error (L2 norm): {reconstruction_error:.6f}") # Note: Error is typically very small due to floating point precision assert reconstruction_error < 1e-4

Debug

Known issues

gotchaPerformance heavily depends on PyTorch's underlying CUDA/CPU implementation. Ensure PyTorch is correctly installed and configured for your hardware for optimal speed, especially with large tensors.

fix

Verify your PyTorch installation and device setup. Ensure CUDA drivers are up-to-date if using a GPU. Always move tensors to the correct device (e.g., `tensor.to('cuda')`).

affects: All

gotchaDCTorch functions assume input tensors are floating-point types (e.g., `torch.float32`, `torch.float64`). Passing integer tensors will result in type errors or unexpected behavior.

fix

Convert input tensors to a float type using `.to(torch.float32)` or `.to(torch.float64)` before passing them to `dctorch` functions.

affects: All

gotchaWhile DCT and IDCT are often used with square inputs in traditional signal processing, `dctorch` functions `dct_2d` and `idct_2d` will work with rectangular inputs. However, ensure the spatial dimensions (height, width) are consistent for corresponding DCT and IDCT operations to correctly reconstruct the original signal.

fix

No specific fix, but users should be aware of this behavior and ensure input dimensions are managed appropriately for their use case.

affects: All

Upgrade

Version history

0.1.2latest on PyPI · released Sep 21, 2023

Audit

Dependencies

torchrequiredcore functionality relies on PyTorch tensors for inputs and outputs

Agent activity

33 hits · last 30 days

node

Amazon

OpenAI (training)

Resources

packagehttps://pypi.org/project/dctorch/ ↗