Col2Im 5D Tensor Operation

Overview

This project implements the Column-to-Image (Col2Im) operation for 5D tensors in C++. The Col2Im operation is a crucial component in convolutional neural networks, particularly in the backward pass of convolution layers. It transforms a column-based representation back into a spatial representation.

Features

• Efficient C++ implementation for 5D tensors
• Supports arbitrary input shapes, block shapes, dilations, paddings, and strides
• Memory-efficient with minimal allocations
• Easy to integrate into existing C++ neural network frameworks

Implementation Details

The core of the implementation is the Col2Im5D class, which contains a static compute method. This method performs the Col2Im operation with the following parameters:

• columns: Input column data
• output: Output tensor data
• input_shape: Shape of the original input tensor (N, C, D, H, W)
• block_shape: Shape of the convolution kernel (kD, kH, kW)
• dilations: Dilation factors for each spatial dimension
• pads: Padding for the beginning and end of each spatial dimension
• strides: Stride of the convolution for each spatial dimension

Usage

Here's a basic example of how to use the Col2Im5D class:

#include "col2im_5d.hpp"
#include <vector>

int main() {
    // Define input parameters
    int input_shape[] = {2, 3, 10, 10, 10}; // N, C, D, H, W
    int block_shape[] = {3, 3, 3}; // kD, kH, kW
    int dilations[] = {1, 1, 1};
    int pads[] = {1, 1, 1, 1, 1, 1};
    int strides[] = {1, 1, 1};

    // Calculate sizes
    int N = input_shape[0], C = input_shape[1], D = input_shape[2], H = input_shape[3], W = input_shape[4];
    int kD = block_shape[0], kH = block_shape[1], kW = block_shape[2];
    int out_depth = (D + pads[0] + pads[3] - (dilations[0] * (kD - 1) + 1)) / strides[0] + 1;
    int out_height = (H + pads[1] + pads[4] - (dilations[1] * (kH - 1) + 1)) / strides[1] + 1;
    int out_width = (W + pads[2] + pads[5] - (dilations[2] * (kW - 1) + 1)) / strides[2] + 1;
    int columns_size = N * C * kD * kH * kW * out_depth * out_height * out_width;

    // Create input and output data
    std::vector<float> columns(columns_size);
    std::vector<float> output(N * C * D * H * W);

    // Perform col2im operation
    Col2Im5D::compute(columns.data(), output.data(), input_shape, block_shape, dilations, pads, strides);

    // Use the output...

    return 0;
}

Integration

To integrate this into your project:

1. Include the col2im_5d.hpp header in your source files.
2. Ensure that the input columns data is correctly formatted from your previous operations.
3. Allocate the output tensor before calling the compute method.
4. Call Col2Im5D::compute with the appropriate parameters.

Performance Considerations

• The implementation uses raw pointers for efficiency.
• A single temporary buffer is allocated for the padded output to minimize memory operations.
• Nested loops are used for clarity, but could be further optimized for specific architectures or use cases.

Future Improvements

• SIMD vectorization for improved performance on supported architectures.
• GPU implementation using CUDA or OpenCL for parallel processing.
• Template metaprogramming for compile-time optimizations based on tensor dimensions.

Contributing

Contributions to improve the implementation or extend its functionality are welcome. Please submit pull requests or open issues for any bugs or feature requests.Please reach out to [email protected] for any questions

License

This project is licensed under the MIT License - see the LICENSE file for details.