96 lines
3.1 KiB
C++
96 lines
3.1 KiB
C++
// Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
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#include <sys/types.h>
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#include <unistd.h>
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#include <cstdint>
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#include <cstdio>
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#include <cstdlib>
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#include <ctime>
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#include <vector>
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namespace {
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constexpr int kModelWidth = 320;
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constexpr int kModelHeight = 640;
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constexpr int kRetryWindow = 20;
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constexpr int kSlowThresholdMs = 10;
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double millis_since_boot() {
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timespec stamp{};
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#ifdef CLOCK_BOOTTIME
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clock_gettime(CLOCK_BOOTTIME, &stamp);
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#else
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clock_gettime(CLOCK_MONOTONIC, &stamp);
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#endif
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return stamp.tv_sec * 1000.0 + stamp.tv_nsec * 1e-6;
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}
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inline float identity_input(uint8_t value) {
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return value;
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}
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void pack_monitoring_tensor(uint8_t *nv12_frame, float *tensor_out) {
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const int half_h = kModelHeight / 2;
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const int half_w = kModelWidth / 2;
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const int plane_area = half_w * half_h;
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const int uv_base = kModelWidth * kModelHeight;
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for (int row = 0; row < half_h; ++row) {
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for (int col = 0; col < half_w; ++col) {
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const int slot = col * half_h + row;
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const int y_row = row * 2;
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const int y_col = col * 2;
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tensor_out[slot] = identity_input(nv12_frame[(y_row * kModelWidth) + y_col]);
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tensor_out[slot + plane_area] = identity_input(nv12_frame[((y_row + 1) * kModelWidth) + y_col]);
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tensor_out[slot + (plane_area * 2)] = identity_input(nv12_frame[(y_row * kModelWidth) + y_col + 1]);
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tensor_out[slot + (plane_area * 3)] = identity_input(nv12_frame[((y_row + 1) * kModelWidth) + y_col + 1]);
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tensor_out[slot + (plane_area * 4)] = identity_input(nv12_frame[uv_base + (row * half_w) + col]);
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tensor_out[slot + (plane_area * 5)] = identity_input(nv12_frame[uv_base + plane_area + (row * half_w) + col]);
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}
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}
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}
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double average_runtime_ms(uint8_t *nv12_frame, float *tensor_out) {
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double total_ms = 0.0;
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for (int i = 0; i < kRetryWindow; ++i) {
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const double start_ms = millis_since_boot();
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pack_monitoring_tensor(nv12_frame, tensor_out);
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total_ms += millis_since_boot() - start_ms;
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}
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return total_ms / static_cast<double>(kRetryWindow);
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}
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void dump_stall_trace(uint8_t *nv12_frame, float *tensor_out) {
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for (int i = 0; i < 200; ++i) {
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const double start_ms = millis_since_boot();
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pack_monitoring_tensor(nv12_frame, tensor_out);
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printf("%.2f ", millis_since_boot() - start_ms);
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}
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printf("\n");
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}
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} // namespace
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int main() {
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const size_t nv12_bytes = kModelWidth * kModelHeight * 3 / 2;
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const size_t tensor_floats = (kModelWidth / 2) * (kModelHeight / 2) * 6;
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while (true) {
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auto *nv12_frame = static_cast<uint8_t *>(malloc(nv12_bytes));
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auto *tensor_out = static_cast<float *>(malloc(tensor_floats * sizeof(float)));
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printf("allocate -- %p 0x%zx -- %p 0x%zx\n", nv12_frame, nv12_bytes, tensor_out, tensor_floats * sizeof(float));
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const double mean_ms = average_runtime_ms(nv12_frame, tensor_out);
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if (mean_ms > kSlowThresholdMs) {
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printf("HIT %.2f\n", mean_ms);
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printf("BAD\n");
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dump_stall_trace(nv12_frame, tensor_out);
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return 0;
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}
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printf("got %.2f\n", mean_ms);
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}
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}
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