torchflint 0.0.1b16.post1.dev3

TorchFlint is a collection of advanced utility functions designed for tensor manipulation within the PyTorch ecosystem. It provides granular control over high-dimensional data processing, bridging the gap between high-level neural modules and low-level tensor operations.

TorchFlint: Advanced Tensor Operations and State Management for PyTorch

TorchFlint is a collection of advanced utility functions designed for tensor manipulation within the PyTorch ecosystem. It provides granular control over high-dimensional data processing, bridging the gap between high-level neural modules and low-level tensor operations.

✨ Key Features

1. Transparent Buffer Management

Simplifies the registration of non-parameter states in nn.Module using a Pythonic assignment syntax.

• torchflint.buffer(tensor, persistent): Wraps a tensor so it can be assigned directly inside a module. It automatically handles device movement and state_dict saving without using torch.nn.Module.register_buffer.
• Containers: Includes BufferObject, BufferList and BufferDict for organized state management.

2. Patchwork

A core module for handling N-dimensional patches, serving as the foundation for convolution and pooling operations.

• High Performance: Includes torchflint.patchwork.unfold_space, torchflint.patchwork.fold_space, and torchflint.patchwork.fold_stack. Notably, the speed of torchflint.patchwork.fold_stack is close to the official torch.nn.functional.fold (only support images less than 2D) in many scenarios.
• N-Dimensional Support: Works seamlessly on 1D, 2D, and 3D+ spatial data.
• Functional Convolution & Pooling: Built upon the torchflint.patchwork module, many functions expose the intermediate steps of convolution and pooling, like torchflint.patchwork.conv_patches, torchflint.patchwork.masked_conv, torchflint.patchwork.pool, torchflint.patchwork.max_pool, and torchflint.patchwork.masked_avg_pool. However, although many of them works well in some tests, but limited (masked version was only tested for their forward process), they will be tested more rigorously in the future.

3. Convenient Tensor Utilities

A suite of tools in torchflint.functional.

🛠️ Installation

pip install torchflint

Requirement: Python 3.7+ and PyTorch >= 1.6.0.

📚 Documentation

Complete API documentation:
https://torchflint.readthedocs.io/

🚀 Usage Examples

Using Buffers

Register buffers via simple assignment:

import torch
import torchflint


class MyModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        # Registers a persistent buffer named 'buffer'
        self.buffer = torchflint.buffer(torch.zeros(10), persistent=True)


model = MyModule()

Patch-based Operations

Using the high-performance patch functions:

from torchflint import patchwork


tensor = torch.randn(32, 3, 32, 32)


# Extract patches
patches = patchwork.unfold_space(tensor, kernel_size=3, stride=2, padding=1)


# Reconstruct
cumulative_output = patchwork.fold_stack(patches, stride=2)
average_output = patchwork.fold_space(patches, stride=2)

Convolution (Standard & Masked)

Perform convolutions, optionally applying a mask to handle sparse data or boundary validity.

from torchflint import patchwork


tensor = torch.randn(1, 3, 32, 32)
weight = torch.randn(16, 3, 3, 3) # Standard weight shape: [Out, In, K, K]


# Standard Convolution
output = patchwork.conv(tensor, weight, stride=2, padding=1)


# Masked Convolution
# Apply convolution only where input_mask is valid (1)
input_mask = torch.randn(1, 1, 32, 32) > 0.5
masked_output = patchwork.masked_conv(
    tensor, weight, 
    stride=2, padding=1, 
    input_mask=input_mask
)

Pooling (Standard & Masked)

Apply pooling operations. Masked pooling allows accurate statistical reduction by ignoring invalid regions instead of padding with zeros or infinity.

from torchflint import patchwork


tensor = torch.randn(1, 3, 32, 32)


# Standard Max Pooling
output = patchwork.max_pool(tensor, kernel_size=2, stride=2)


# Masked Max Pooling
# Regions marked as 0 in the mask are ignored during max calculation
# (Useful for non-rectangular images or padding handling)
mask = torch.ones_like(tensor)
mask[..., 0:5, 0:5] = 0 # Invalidate top-left corner
masked_output = patchwork.masked_max_pool(
    tensor, kernel_size=2, stride=2, 
    mask=mask.bool()
)

📄 License

This project is licensed under the MIT License.

Citation

@misc{torchflint,
  author = {Juanhua Zhang},
  title = {TorchFlint: Advanced Tensor Operations and State Management for PyTorch},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Caewinix/torchflint}}
}