jS5t3r

rostopic list
/accel_cmd
/angular_gravity
/base_waypoints
/behavior_state
/brake_cmd
/can_info
/can_velocity
/carla/actor_list
/carla/debug_marker

jS5t3r

name: detection
channels:
  - conda-forge
  - pytorch
  - anaconda
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - absl-py=0.10.0=py37_0
  - blas=1.0=mkl

jS5t3r

# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: linux-64
_libgcc_mutex=0.1=main
absl-py=0.10.0=py37_0
blas=1.0=mkl
bzip2=1.0.8=h7b6447c_0
ca-certificates=2021.10.26=h06a4308_2
certifi=2021.10.8=py37h06a4308_0
cffi=1.13.2=py37h2e261b9_0

jS5t3r

DataParallel(
  (module): Wide_ResNet(
    (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (layer1): Sequential(
      (0): wide_basic(
        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv1): Conv2d(16, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (dropout): Dropout(p=0.3, inplace=False)
        (bn2): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

jS5t3r

WideResNet(
  (init_conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv2): Sequential(
    (0): WideBasic(
      (residual): Sequential(
        (0): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (1): ReLU(inplace=True)
        (2): Conv2d(16, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (4): ReLU(inplace=True)

jS5t3r

"""
Code adapted from https://github.com/Jingkang50/OpenOOD.
Adapted the Evaluater class from https://github.com/Jingkang50/OpenOOD/blob/main/openood/evaluation_api/evaluator.py to generate an attack pipeline.
"""

from typing import Callable, List, Type

import os
import numpy as np
import pandas as pd