gc old stuff in modeld

selfdrive/modeld/tests/dmon_lag/repro.cc

@@ -1,101 +0,0 @@
-// clang++ -O2 repro.cc && ./a.out
-
-#include <sched.h>
-#include <sys/types.h>
-#include <unistd.h>
-
-#include <cstdint>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <ctime>
-
-static inline double millis_since_boot() {
-  struct timespec t;
-  clock_gettime(CLOCK_BOOTTIME, &t);
-  return t.tv_sec * 1000.0 + t.tv_nsec * 1e-6;
-}
-
-#define MODEL_WIDTH 320
-#define MODEL_HEIGHT 640
-
-// null function still breaks it
-#define input_lambda(x) x
-
-// this is copied from models/dmonitoring.cc, and is the code that triggers the issue
-void inner(uint8_t *resized_buf, float *net_input_buf) {
-  int resized_width = MODEL_WIDTH;
-  int resized_height = MODEL_HEIGHT;
-
-  // one shot conversion, O(n) anyway
-  // yuvframe2tensor, normalize
-  for (int r = 0; r < MODEL_HEIGHT/2; r++) {
-    for (int c = 0; c < MODEL_WIDTH/2; c++) {
-      // Y_ul
-      net_input_buf[(c*MODEL_HEIGHT/2) + r] = input_lambda(resized_buf[(2*r*resized_width) + (2*c)]);
-      // Y_ur
-      net_input_buf[(c*MODEL_HEIGHT/2) + r + (2*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(2*r*resized_width) + (2*c+1)]);
-      // Y_dl
-      net_input_buf[(c*MODEL_HEIGHT/2) + r + ((MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(2*r*resized_width+1) + (2*c)]);
-      // Y_dr
-      net_input_buf[(c*MODEL_HEIGHT/2) + r + (3*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(2*r*resized_width+1) + (2*c+1)]);
-      // U
-      net_input_buf[(c*MODEL_HEIGHT/2) + r + (4*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(resized_width*resized_height) + (r*resized_width/2) + c]);
-      // V
-      net_input_buf[(c*MODEL_HEIGHT/2) + r + (5*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(resized_width*resized_height) + ((resized_width/2)*(resized_height/2)) + (r*resized_width/2) + c]);
-    }
-  }
-}
-
-float trial() {
-  int resized_width = MODEL_WIDTH;
-  int resized_height = MODEL_HEIGHT;
-
-  int yuv_buf_len = (MODEL_WIDTH/2) * (MODEL_HEIGHT/2) * 6; // Y|u|v -> y|y|y|y|u|v
-
-  // allocate the buffers
-  uint8_t *resized_buf = (uint8_t*)malloc(resized_width*resized_height*3/2);
-  float *net_input_buf = (float*)malloc(yuv_buf_len*sizeof(float));
-  printf("allocate -- %p 0x%x -- %p 0x%lx\n", resized_buf, resized_width*resized_height*3/2, net_input_buf, yuv_buf_len*sizeof(float));
-
-  // test for bad buffers
-  static int CNT = 20;
-  float avg = 0.0;
-  for (int i = 0; i < CNT; i++) {
-    double s4 = millis_since_boot();
-    inner(resized_buf, net_input_buf);
-    double s5 = millis_since_boot();
-    avg += s5-s4;
-  }
-  avg /= CNT;
-
-  // once it's bad, it's reliably bad
-  if (avg > 10) {
-    printf("HIT %f\n", avg);
-    printf("BAD\n");
-
-    for (int i = 0; i < 200; i++) {
-      double s4 = millis_since_boot();
-      inner(resized_buf, net_input_buf);
-      double s5 = millis_since_boot();
-      printf("%.2f   ", s5-s4);
-    }
-    printf("\n");
-
-    exit(0);
-  }
-
-  // don't free so we get a different buffer each time
-  //free(resized_buf);
-  //free(net_input_buf);
-
-  return avg;
-}
-
-int main() {
-  while (true) {
-    float ret = trial();
-    printf("got %f\n", ret);
-  }
-}
-

selfdrive/modeld/tests/tf_test/build.sh

@@ -1,2 +0,0 @@
-#!/usr/bin/env bash
-clang++ -I /home/batman/one/external/tensorflow/include/ -L /home/batman/one/external/tensorflow/lib -Wl,-rpath=/home/batman/one/external/tensorflow/lib main.cc -ltensorflow

selfdrive/modeld/tests/tf_test/main.cc

@@ -1,69 +0,0 @@
-#include <cassert>
-#include <cstdio>
-#include <cstdlib>
-#include "tensorflow/c/c_api.h"
-
-void* read_file(const char* path, size_t* out_len) {
-  FILE* f = fopen(path, "r");
-  if (!f) {
-    return NULL;
-  }
-  fseek(f, 0, SEEK_END);
-  long f_len = ftell(f);
-  rewind(f);
-
-  char* buf = (char*)calloc(f_len, 1);
-  assert(buf);
-
-  size_t num_read = fread(buf, f_len, 1, f);
-  fclose(f);
-
-  if (num_read != 1) {
-    free(buf);
-    return NULL;
-  }
-
-  if (out_len) {
-    *out_len = f_len;
-  }
-
-  return buf;
-}
-
-static void DeallocateBuffer(void* data, size_t) {
-  free(data);
-}
-
-int main(int argc, char* argv[]) {
-  TF_Buffer* buf;
-  TF_Graph* graph;
-  TF_Status* status;
-  char *path = argv[1];
-
-  // load model
-  {
-    size_t model_size;
-    char tmp[1024];
-    snprintf(tmp, sizeof(tmp), "%s.pb", path);
-    printf("loading model %s\n", tmp);
-    uint8_t *model_data = (uint8_t *)read_file(tmp, &model_size);
-    buf = TF_NewBuffer();
-    buf->data = model_data;
-    buf->length = model_size;
-    buf->data_deallocator = DeallocateBuffer;
-    printf("loaded model of size %d\n", model_size);
-  }
-
-  // import graph
-  status = TF_NewStatus();
-  graph = TF_NewGraph();
-  TF_ImportGraphDefOptions *opts = TF_NewImportGraphDefOptions();
-  TF_GraphImportGraphDef(graph, buf, opts, status);
-  TF_DeleteImportGraphDefOptions(opts);
-  TF_DeleteBuffer(buf);
-  if (TF_GetCode(status) != TF_OK) {
-    printf("FAIL: %s\n", TF_Message(status));
-  } else {
-    printf("SUCCESS\n");
-  }
-}

selfdrive/modeld/tests/tf_test/pb_loader.py

@@ -1,8 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import tensorflow as tf
-
-with open(sys.argv[1], "rb") as f:
-  graph_def = tf.compat.v1.GraphDef()
-  graph_def.ParseFromString(f.read())
-  #tf.io.write_graph(graph_def, '', sys.argv[1]+".try")

selfdrive/modeld/tests/timing/benchmark.py

@@ -1,39 +0,0 @@
-#!/usr/bin/env python3
-# type: ignore
-
-import os
-import time
-import numpy as np
-
-import cereal.messaging as messaging
-from openpilot.system.manager.process_config import managed_processes
-
-
-N = int(os.getenv("N", "5"))
-TIME = int(os.getenv("TIME", "30"))
-
-if __name__ == "__main__":
-  sock = messaging.sub_sock('modelV2', conflate=False, timeout=1000)
-
-  execution_times = []
-
-  for _ in range(N):
-    os.environ['LOGPRINT'] = 'debug'
-    managed_processes['modeld'].start()
-    time.sleep(5)
-
-    t = []
-    start = time.monotonic()
-    while time.monotonic() - start < TIME:
-      msgs = messaging.drain_sock(sock, wait_for_one=True)
-      for m in msgs:
-        t.append(m.modelV2.modelExecutionTime)
-
-    execution_times.append(np.array(t[10:]) * 1000)
-    managed_processes['modeld'].stop()
-
-  print("\n\n")
-  print(f"ran modeld {N} times for {TIME}s each")
-  for _, t in enumerate(execution_times):
-    print(f"\tavg: {sum(t)/len(t):0.2f}ms, min: {min(t):0.2f}ms, max: {max(t):0.2f}ms")
-  print("\n\n")