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return self.disconnect()
import socket
class Blynk(BlynkProtocol):
def __init__(self, auth, **kwargs):
self.insecure = kwargs.pop('insecure', False)
self.server = kwargs.pop('server', 'blynk.cloud')
self.port = kwargs.pop('port', 80 if self.insecure else 443)
BlynkProtocol.__init__(self, auth, **kwargs)
self.on('redirect', self.redirect)
def redirect(self, server, port):
self.server = server
self.port = port
self.disconnect()
self.connect()
def connect(self):
print('Connecting to %s:%d...' % (self.server, self.port))
s = socket.socket()
s.connect(socket.getaddrinfo(self.server, self.port)[0][-1])
try:
s.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
except:
pass
if self.insecure:
self.conn = s
else:
try:
import ussl
ssl_context = ussl
except ImportError:
import ssl
ssl_context = ssl.create_default_context()
self.conn = ssl_context.wrap_socket(s, server_hostname=self.server)
try:
self.conn.settimeout(SOCK_TIMEOUT)
except:
s.settimeout(SOCK_TIMEOUT)
BlynkProtocol.connect(self)
def _write(self, data):
#print('<', data)
self.conn.write(data)
# TODO: handle disconnect
def run(self):
data = b''
try:
data = self.conn.read(self.buffin)
#print('>', data)
except KeyboardInterrupt:
raise
except socket.timeout:
# No data received, call process to send ping messages when needed
pass
except: # TODO: handle disconnect
return
self.process(data)
# <FILESEP>
from lib.config import cfg, args
from lib.utils.ply_utils import *
import numpy as np
import os
import glob
def run_dataset():
from lib.datasets import make_data_loader
import tqdm
cfg.train.num_workers = 0
data_loader = make_data_loader(cfg, is_train=False)
for batch in tqdm.tqdm(data_loader):
pass
def run_network():
from lib.networks import make_network
from lib.datasets import make_data_loader
from lib.utils.net_utils import load_network
from lib.utils.data_utils import to_cuda
import tqdm
import torch
import time
network = make_network(cfg).cuda()
load_network(network, cfg.trained_model_dir, epoch=cfg.test.epoch)
network.eval()
data_loader = make_data_loader(cfg, is_train=False)
total_time = 0
for batch in tqdm.tqdm(data_loader):
batch = to_cuda(batch)
with torch.no_grad():
torch.cuda.synchronize()
start = time.time()
network(batch)
torch.cuda.synchronize()