90 lines
4.2 KiB
Plaintext
90 lines
4.2 KiB
Plaintext
#include <vector>
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#include <iostream>
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#include <iomanip> // 用于格式化输出
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using namespace std;
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int main() {
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// --------------------- 1. 初始化输入图像 ---------------------
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// 假设输入图像形状为 [3, 896, 896]（通道优先：C×H×W）
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const int C = 3; // 通道数（RGB）
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const int H = 896; // 图像高度
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const int W = 896; // 图像宽度
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vector<float> inp_raw(C * H * W, 1.0f); // 示例：全1数据（实际需替换为真实图像数据）
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// --------------------- 2. 分块参数计算 ---------------------
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const int num_patches_h = 14; // Height方向分块数
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const int num_patches_w = 14; // Width方向分块数
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const int patch_h = H / num_patches_h; // 单个Patch的高度（64）
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const int patch_w = W / num_patches_w; // 单个Patch的宽度（64）
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// --------------------- 3. 提取每个Patch的统计特征（示例：均值） ---------------------
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// patch_means[ph][pw][c] 存储第ph行、第pw列Patch的第c通道均值
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vector<vector<vector<float>>> patch_means(
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num_patches_h,
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vector<vector<float>>(num_patches_w, vector<float>(C))
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);
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for (int c = 0; c < C; ++c) {
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for (int ph = 0; ph < num_patches_h; ++ph) {
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for (int pw = 0; pw < num_patches_w; ++pw) {
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float sum = 0.0f;
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int count = 0;
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// 遍历Patch内所有像素
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for (int h = ph * patch_h; h < (ph + 1) * patch_h; ++h) {
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for (int w = pw * patch_w; w < (pw + 1) * patch_w; ++w) {
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int idx = c * H * W + h * W + w; // 计算inp_raw中像素的索引
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sum += inp_raw[idx];
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count++;
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}
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}
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patch_means[ph][pw][c] = sum / count; // 计算该Patch的通道均值
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}
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}
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}
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// --------------------- 4. 构建目标维度：4096×2×3×14×14 ---------------------
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const int batch_size = 4096; // Batch大小
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const int num_views = 2; // 视角/分支数（如时间步、参考帧与当前帧）
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// 目标数组形状：[batch_size, num_views, C, num_patches_h, num_patches_w]
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vector<float> target(
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batch_size * num_views * C * num_patches_h * num_patches_w
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);
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// 填充目标数组：每个Batch元素、视角、通道、Patch位置均使用对应Patch的均值
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for (int n = 0; n < batch_size; ++n) {
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for (int m = 0; m < num_views; ++m) {
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for (int c = 0; c < C; ++c) {
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for (int ph = 0; ph < num_patches_h; ++ph) {
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for (int pw = 0; pw < num_patches_w; ++pw) {
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// 计算目标数组的一维索引
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int target_idx = n * num_views * C * num_patches_h * num_patches_w
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+ m * C * num_patches_h * num_patches_w
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+ c * num_patches_h * num_patches_w
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+ ph * num_patches_w + pw;
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target[target_idx] = patch_means[ph][pw][c];
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}
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}
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}
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}
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}
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// --------------------- 5. 验证输出（可选） ---------------------
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cout << "目标数组总元素数: " << target.size() << "（预期: "
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<< batch_size * num_views * C * num_patches_h * num_patches_w << "）" << endl;
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// 打印第一个Batch、第一个视角、第一个通道的所有Patch值（前几个）
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cout << "\n第一个Batch、第一个视角、第一个通道的Patch均值（前20个）:" << endl;
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for (int ph = 0; ph < min(5, num_patches_h); ++ph) { // 只打印前5个Patch的行
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for (int pw = 0; pw < min(5, num_patches_w); ++pw) { // 只打印前5个Patch的列
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int idx = 0 * num_views * C * num_patches_h * num_patches_w
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+ 0 * C * num_patches_h * num_patches_w
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+ 0 * num_patches_h * num_patches_w
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+ ph * num_patches_w + pw;
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cout << fixed << setprecision(2) << target[idx] << "\t";
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}
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cout << endl;
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}
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return 0;
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} |