FirefoxMetzger

FirefoxMetzger

#! /usr/bin/env python

import os
import sys
import tempfile
import shutil
import builtins
import textwrap

import setuptools

FirefoxMetzger

'''Train a simple residual network on the CIFAR10 small images dataset.

It gets to 75% validation accuracy in 25 epochs, and 79% after 50 epochs.
(it's still underfitting at that point, though).
'''

from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator

FirefoxMetzger

from scenario import gazebo as scenario_gazebo
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from scipy.spatial.transform import Rotation as R
import ropy.transform as tf
import ropy.ignition as ign


def camera_parser(msg):

FirefoxMetzger

12026 execve("/usr/bin/git", ["git", "clone", "--sparse", "https://github.com/microsoft/WSL"...], 0x7fff95630170 /* 22 vars */) = 0
12026 brk(NULL)                         = 0x5565d9152000
12026 arch_prctl(0x3001 /* ARCH_??? */, 0x7ffd1d104450) = -1 EINVAL (Invalid argument)
12026 access("/etc/ld.so.preload", R_OK) = -1 ENOENT (No such file or directory)
12026 openat(AT_FDCWD, "/etc/ld.so.cache", O_RDONLY|O_CLOEXEC) = 3
12026 fstat(3, {st_mode=S_IFREG|0644, st_size=97898, ...}) = 0
12026 mmap(NULL, 97898, PROT_READ, MAP_PRIVATE, 3, 0) = 0x7f4cfd2b5000
12026 close(3)                          = 0
12026 openat(AT_FDCWD, "/lib/x86_64-linux-gnu/libpcre2-8.so.0", O_RDONLY|O_CLOEXEC) = 3
12026 read(3, "\177ELF\2\1\1\0\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0\340\"\0\0\0\0\0\0"..., 832) = 832

FirefoxMetzger

FirefoxMetzger

# vouch config
# bare minimum to get vouch running with OpenID Connect (such as okta)

vouch:
  logLevel: debug
  testing: true
  listen: 0.0.0.0
  port: 9090
  allowAllUsers: true

FirefoxMetzger

# vouch config
# bare minimum to get vouch running with OpenID Connect (such as okta)

vouch:
  logLevel: debug
  testing: true
  listen: 0.0.0.0
  port: 9090
  allowAllUsers: true

FirefoxMetzger

import numpy as np

random_numbers = np.random.rand(int(2e8)).tolist()

def random():
    try:
        return random_numbers.pop()
    except IndexError:
        raise IndexError("Out of random numbers; generate more next time.")

FirefoxMetzger

import timeit

number = 10000
numpy_time = timeit.timeit("[np.random.rand() for _ in range(int(1e3))]", "import numpy as np", number=number)
random_time = timeit.timeit("[random.random() for _ in range(int(1e3))]", "import random", number=number)
numpy_batch_time = timeit.timeit("np.random.rand(int(1e3))", "import numpy as np", number=number)

print("Timings")
print("=======")
print(f"Numpy Single: {numpy_time:.3f}")