子像素卷积优化记录

待完善

1. 理解子像素卷积
   子像素卷积是一种通过重排列特征图的通道来实现上采样的方法。它通过将低分辨率特征图的多个通道重新排列成高分辨率图像的空间维度，从而实现上采样。

2. 子像素卷积的实现
   在PyTorch中，可以使用torch.nn.PixelShuffle来实现子像素卷积。以下是一个简单的示例：

import torch
import torch.nn as nn

class SubPixelConv(nn.Module):
    def __init__(self, upscale_factor):
        super(SubPixelConv, self).__init__()
        self.pixel_shuffle = nn.PixelShuffle(upscale_factor)

    def forward(self, x):
        return self.pixel_shuffle(x)

# Example usage
input_tensor = torch.randn(1, 16, 8, 8)  # Batch size 1, 16 channels, 8x8 feature map
upscale_factor = 2
model = SubPixelConv(upscale_factor)
output_tensor = model(input_tensor)
print(output_tensor.shape)  # Output shape will be (1, 4, 16, 16)

3. CUDA优化
   为了在CUDA上优化子像素卷积，可以使用自定义CUDA内核来实现高效的像素重排列。以下是一个简化的示例，展示如何编写自定义CUDA内核：

#include <cuda_runtime.h>

__global__ void subpixel_conv_kernel(float* input, float* output, int channels, int height, int width, int upscale_factor) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total_elements = channels * height * width;

    if (idx < total_elements) {
        int c = idx / (height * width);
        int h = (idx % (height * width)) / width;
        int w = idx % width;

        int new_c = c / (upscale_factor * upscale_factor);
        int offset = c % (upscale_factor * upscale_factor);
        int new_h = h * upscale_factor + offset / upscale_factor;
        int new_w = w * upscale_factor + offset % upscale_factor;

        int new_idx = new_c * (height * upscale_factor) * (width * upscale_factor) + new_h * (width * upscale_factor) + new_w;
        output[new_idx] = input[idx];
    }
}

extern "C" void subpixel_conv(float* input, float* output, int channels, int height, int width, int upscale_factor) {
    int total_elements = channels * height * width;
    int threads_per_block = 1024;
    int num_blocks = (total_elements + threads_per_block - 1) / threads_per_block;

    subpixel_conv_kernel<<<num_blocks, threads_per_block>>>(input, output, channels, height, width, upscale_factor);
}

4. 调用CUDA内核
   在Python中，可以使用pycuda或cupy来调用自定义CUDA内核。以下是一个示例：

import numpy as np
import cupy as cp

# Load the CUDA kernel
kernel_code = """
extern "C" __global__ void subpixel_conv_kernel(float* input, float* output, int channels, int height, int width, int upscale_factor) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total_elements = channels * height * width;

    if (idx < total_elements) {
        int c = idx / (height * width);
        int h = (idx % (height * width)) / width;
        int w = idx % width;

        int new_c = c / (upscale_factor * upscale_factor);
        int offset = c % (upscale_factor * upscale_factor);
        int new_h = h * upscale_factor + offset / upscale_factor;
        int new_w = w * upscale_factor + offset % upscale_factor;

        int new_idx = new_c * (height * upscale_factor) * (width * upscale_factor) + new_h * (width * upscale_factor) + new_w;
        output[new_idx] = input[idx];
    }
}
"""

module = cp.RawModule(code=kernel_code)
subpixel_conv_kernel = module.get_function("subpixel_conv_kernel")

def subpixel_conv(input_tensor, upscale_factor):
    channels, height, width = input_tensor.shape
    output_tensor = cp.zeros((channels // (upscale_factor * upscale_factor), height * upscale_factor, width * upscale_factor), dtype=cp.float32)
    
    threads_per_block = 1024
    total_elements = channels * height * width
    num_blocks = (total_elements + threads_per_block - 1) // threads_per_block
    
    subpixel_conv_kernel((num_blocks,), (threads_per_block,), (input_tensor, output_tensor, channels, height, width, upscale_factor))
    
    return output_tensor

# Example usage
input_tensor = cp.random.randn(16, 8, 8).astype(cp.float32)
upscale_factor = 2
output_tensor = subpixel_conv(input_tensor, upscale_factor)
print(output_tensor.shape)  # Output shape will be (4, 16, 16)