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jblitzar
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github-python-corpus

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This is:
1. Create the Accessory object you want.
2. Add it to an AccessoryDriver, which will advertise it on the local network,
setup a server to answer client queries, etc.
"""
import logging
import signal
import random
from pyhap.accessory import Accessory, Bridge
from pyhap.accessory_driver import AccessoryDriver
import pyhap.loader as loader
from pyhap import camera
from pyhap.const import CATEGORY_SENSOR
logging.basicConfig(level=logging.INFO, format="[%(module)s] %(message)s")
class TemperatureSensor(Accessory):
"""Fake Temperature sensor, measuring every 3 seconds."""
category = CATEGORY_SENSOR
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
serv_temp = self.add_preload_service('TemperatureSensor')
self.char_temp = serv_temp.configure_char('CurrentTemperature')
@Accessory.run_at_interval(3)
async def run(self):
self.char_temp.set_value(random.randint(18, 26))
def get_bridge(driver):
"""Call this method to get a Bridge instead of a standalone accessory."""
bridge = Bridge(driver, 'Bridge')
temp_sensor = TemperatureSensor(driver, 'Sensor 2')
temp_sensor2 = TemperatureSensor(driver, 'Sensor 1')
bridge.add_accessory(temp_sensor)
bridge.add_accessory(temp_sensor2)
return bridge
def get_accessory(driver):
"""Call this method to get a standalone Accessory."""
return TemperatureSensor(driver, 'MyTempSensor')
# Start the accessory on port 51826
driver = AccessoryDriver(port=51826)
# Change `get_accessory` to `get_bridge` if you want to run a Bridge.
driver.add_accessory(accessory=get_accessory(driver))
# We want SIGTERM (terminate) to be handled by the driver itself,
# so that it can gracefully stop the accessory, server and advertising.
signal.signal(signal.SIGTERM, driver.signal_handler)
# Start it!
driver.start()
# <FILESEP>
# Implementation adapted from XNAS: https://github.com/MAC-AutoML/XNAS
"""BigNAS subnet evaluation"""
import torch
import core.config as config
import logger.meter as meter
import logger.logging as logging
from core.builder import setup_env
from core.config import cfg
from datasets.loader import get_normal_dataloader
from logger.meter import TestMeter
from bignas.cnn import _infer_BigNAS_CNN
# Load config and check
config.load_configs()
logger = logging.get_logger(__name__)
def main():
setup_env()
net = _infer_BigNAS_CNN()
[train_loader, valid_loader] = get_normal_dataloader()
test_meter = TestMeter(len(valid_loader))
# Validate
top1_err, top5_err = validate(net, train_loader, valid_loader, test_meter)
logger.info("top1_err:{} top5_err:{}".format(top1_err, top5_err))
@torch.no_grad()