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

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

# 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

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

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

jS5t3r

<!-- -->
<launch>
  <arg name='role_name' default='ego_vehicle'/>

  <include file="$(env CARLA_AUTOWARE_ROOT)/my_map.launch"/>
  <include file="$(env CARLA_AUTOWARE_ROOT)/my_sensing.launch"/>
  <include file="$(env CARLA_AUTOWARE_ROOT)/my_localization.launch">
    <arg name='role_name' value='$(arg role_name)'/>
  </include>
  <include file="$(env CARLA_AUTOWARE_ROOT)/my_detection.launch">

jS5t3r

# Enter your code here. Read input from STDIN. Print output to STDOUT
class Node:
    def __init__(self,value,point):
        self.value = value
        self.point = point
        self.parent = None
        self.H = 0
        self.G = 0
    def move_cost(self,other):
        return 0 if self.value == '.' else 1

jS5t3r

<?xml version="1.0" encoding="UTF-8"?>
<OpenSCENARIO>
    <FileHeader revMajor="0" revMinor="9" date="2019-06-25" description="PedestrianCrossing" author="" />
    <Catalogs>
        <VehicleCatalog>
            <Directory path="Catalogs/VehicleCatalogs" />
        </VehicleCatalog>
        <DriverCatalog>
            <Directory path="Catalogs/DriverCatalogs" />
        </DriverCatalog>

jS5t3r

<?xml version="1.0" encoding="UTF-8"?>
<OpenSCENARIO>
    <FileHeader revMajor="0" revMinor="9" date="2019-06-25" description="PedestrianCrossing" author="" />
    <Catalogs>
        <VehicleCatalog>
            <Directory path="Catalogs/VehicleCatalogs" />
        </VehicleCatalog>
        <DriverCatalog>
            <Directory path="Catalogs/DriverCatalogs" />
        </DriverCatalog>

jS5t3r

import numpy as np
from sklearn.datasets import make_moons
from sklearn.cross_validation import train_test_split


n_feature = 2
n_class = 2


def make_network(n_hidden=100):